All righty, well let's get started here. Hello and welcome. My name is Shannon Kemp, and I'm the executive editor for DataVercity, and we're broadcasting live today from the NoSQL Now conference in Expo. We would like to thank you for joining today's DataVercity webinar, Big Challenges in Data Modeling, Modeling on Structure Data, Schema Design, sponsored today by CouchBase, CA Geology, some filmmakers of Irwin, and Sandhill Consulting. And as the series is moderated by our Steampant moderator, Karen Lopez. So just a couple of points to get us started. There's a large number of people that attend these sessions. You will be muted during the webinar. There are questions. We will be collecting them via the Q&A section in the bottom right-hand corner of your screen. Or if you'd like to tweet, we encourage you to share our highlights or questions via Twitter using hashtag BCDModeling, Big Challenges in Data Modeling. As always, we will send a follow-up email within two business days containing links to the recording of this session and any additional information requested throughout the webinar. And then please to introduce to the moderator for this webinar series, Karen Lopez. Karen is a senior project manager and architect at InfoAdvisor. She specializes in the practical application of data management principles. Karen is a frequent speaker, blogger, and supports us on the professional data issues. She is a Microsoft SQL Server MVP specializing in data modeling and database design. She's an advisor to the DAMA International Board and a member of the advisory board of Zachman International.   First, I have Dipti Bokhar, the Director of Product Management at CouchBase, where she is responsible for the company's flagship product, CouchBase Service, and works with customers and users to understand emerging requirements for low-latency scalable data stores. Dipti has been a technical experience in the database industry having worked at IBM as a software engineer and development manager for the DB2 server team and then at MarkLogic as a senior product manager. Over the next month is Alex Peek. Alex is passionate about extracting value from data and has been for over 20 years. In the early days, it was with startups, several of them that he has owned. He has also worked with larger companies like Iron Mountain, PayPal, and Intuit. He has designed and implemented systems for managing data and analyzing it. Then the relational world, he has worked with OTP systems like Oracle and BoltDB. And for data warehouse systems, I'm getting tight here. Vertica. And the non-relational world, he has also worked with to do ecosystems, no SQL databases like assigned to HBase and MongoDB and streaming solutions. In the non-relational world, he has built systems for marketing segmentation and used machine learning methodologies with tools like R and MATLAB. And I'm certainly not least pleased to welcome Hamaslyn Hayes. Ham is a senior consultant for SandHero Consultants. Over time, Ham has led much of the evolution of the CEI Erwin product suite and its support education courses. He has provided his extensive expertise in information, process, and enterprise modeling to numerous major North American corporations and government agencies. Ham has authored articles and delivered presentations to industry groups on enterprise modeling and its role in improving performance. And the focus of his consulting and teaching has helped enterprises bridge the space between technical modeling and business success. He has also researched and modeling using Erwin products to model nonlinear social reactions. So please welcome Karen and our panelists. And with that, I will turn it over to Karen to get us started. Hello, everyone. Welcome. Thanks, Shannon. It's so great to be sitting across from you in the next few weeks. And we still have two more on the phone. And I wanted to also warn my panelists on the phone that we might just sit here in the room and totally forget about you. So you might have to edge in to get your comments. I also wanted to thank CouchBase as a new sponsor for this panel. That makes me really happy. And both CA Technologies and Sandhill Consultants as previous and existing sponsors. It's really important that we have sponsorship from the vendor community for our community of data professionals. And one of the things it says to me is that these vendors are interested in making sure that you have access to people who have great opinions and expertise in these areas. And the way they show the fact that they care about you is by sponsoring these things. We couldn't do it without you. And of course, Tony and Shannon are fine friends at Dataversity because they make this happen. But I also wanted to thank you, the audience, because I consider you also panelists on this because you have a chat available to you where you can also give your insights and opinions and try to watch those and multitask and do all this. I'm sorry, multi-thread and do all this. And if you have a formal question for the panel, please do post it in the Q&A and don't wait for the end. We're going to try to grab some of those questions as we go. And also your comments for those things. As Shannon mentioned, you and I are on Twitter and we'll try to watch Twitter if you use that BCD modeling hashtag to see what's going on there. That's also how you, the audience, help be a panelist by sharing the insights you're hearing here with the rest of the Twitterverse. The next topic is on sort of NoSQL, schema-less design, modeling for unstructured data. When does it happen? Does it happen? And one of the reasons why we have this topic this week is because we're here in San Jose at the NoSQL Now conference. And this is the last day of the conference so I've been absorbing a lot of stuff and updating my questions as I go. In fact, there's a session going on right now that I'd like to be in on modeling for our database. And so there's a lot of modeling chat going on this year as opposed to previous years. But what I think is important is most of us in the audience I'm guessing that you are data architects or data professionals. But you want a DB2 data architect or a SQL server database designer. Not really. If you're a data professional and those platforms change, I think it's important that we as data people have a good understanding about all the places where data goes to sleep at night. The technical term is persistence, but I like to think that data is data no matter where it is and that we have a professional responsibility to understand not just how to persist data in traditional relational technologies, but to understand how it's persisted in these non-relational ones. Also that we also have a great set of domain knowledge that we can lend to data as it's being consumed. So most of the schema lists are no-SQL design platforms, those models of persisting data. There's not a lot of modeling done up front, although we're going to talk about that. But understanding what data is, what makes for a valid instance of that data, what makes for good data, what makes for probably wrong data, what makes for valid ways of using data together, that's something that I think data architects have a lot to contribute to. It's just that we're used to providing that expertise as the database is being built instead of as the data is being used, consumed, shared, and analyzed. I also think that if we continue to ignore our no-SQL or non-relational platforms, we become less and less relevant to our companies and to our company's customers. I also think there's a lot of myths and mysteries and questions about the roles of data architects and database designers in no-SQL projects, so I want to make sure we talk about those. So having said that, let me jump over to the questions. So which of my panelists would like to, in a sound bite, explain what we mean by no-SQL? Thank you. I'll take a short of that, Karen. Coming from the relational space, moving from Davy to the no-SQL itself, I've learned a lot along the way. We look at no-SQL. It is the next generation of databases that allows you to store data in not rows and columns, as I would put it, something that does not have to live in a relational format, but that can be a document. It could be stored in a slightly different format, maybe a column family, or maybe even in a graphical structure. And so no-SQL, in general, means many things to many people, but the way we really think of it is it is the next generation database for semi-structured, unstructured, poly-structured data that really doesn't fit well in rows and columns. Is that you who wanted to also say something? Yeah, I was going to sort of give a slightly different viewpoint. So I agree with what Dipty has said, but I'd also say that the emergence of a lot of these no-SQL databases has beaten around simplifying the model of the relational database in order to gain different optimizations. So in many of the no-SQL databases, there are design decisions taken that make certain aspects of them more optimal for particular use cases, but in doing so, they give up the generality of the traditional relational database. So that's sort of not in contrast to what Dipty said, but in addition, it's really about taking a different set of trade-offs, giving up the generality for concepts like availability, performance, reliability, so on. I'm going to contribute, too. This is Ham. I think I almost see it as a philosophical level as well that when dealing with well-structured data design using relational principles for several decades now, it's quite a mature area. But we've also seen with the end of the internet its tremendous growth in both the quantity of data as well as the accessibility, as Alex pointed out. One of the properties we're now dealing with is that we really have to take a fresh look at how we design and manage our data systems. No SQL to me doesn't mean no structure. It means that we have to account for new requirements in our overall data management. And it's evolving. I think we're people on the path of breaking through some new thought forms and some new approaches. It's quite exciting. Jump right away and say that in one of the contentious issues, it's just a term, no SQL in general, that it originally came out as really sort of an attack on relational databases. It was no SQL. We don't use SQL anymore. And there are some really valid reasons why a solution should be done in a relational database and very valid ones why it just makes sense to put those into storage data in other platforms beyond relational. And for someone like me who's been doing relational, building relational systems for decades now, that just means I'm experienced, not old, is that it's hard for me to think outside of the SQL box because that's where I live every day. But I don't definitely see all these valid uses, but I'm still hearing, I come to not just this conference, but other conferences. And I'm still hearing SQL versus no SQL thing tossed around as it's an either or and that SQL is broken and I'll hear things not really from the speakers or the experts, but sort of a myth around the development community or the engineering community, which is SQL doesn't work. So one of the things I've been tweeting at this conference is sort of these throwaway facts that, you know, once a SQL database hits 50 gigs, it just falls down and can't do something. And we know that's not true. We have lots of valid implementations of very large multi-parabyte systems, even petabyte systems implemented where the data is persisted in traditional RDBMSs. But what's the best way for a data architect to participate in the conversation about what's the right tool to be used, which is the new definition of no SQL, not only SQL? You know, what's the best way for a solutions architect, an enterprise architect, and even a data architect to participate in that discussion? Now, the underlying elements are just sort of to understand the strengths of each and to be able to explain the strengths of each.  And, you know, I worked at PayPal eBay for a while and that's one of the largest teradata installations in the world. Many, many petabytes, you know, double digit petabytes. So there's nothing about relational that says it can't scale. So, you know, size is not the issue. There are a number of issues which do differentiate.  I mean, as I mentioned earlier, by using, you know, just pick an example like Cassandra because we use that here into it fairly extensively. You know, using a database like Cassandra, you get, you know, great economics for performing certain kinds of tasks. So, you know, we can get very large data, very low latency, high availability, you know, great performance as long as the task fits what Cassandra is good at. And, you know, if you step outside of that, then suddenly, you know, it's not magical anymore. So in explaining, you know, one of the world's all coexist, and, you know, all we've done is add more tools to the toolkit. It's just that understanding and the strengths of each. Yeah, I'd validate that with the understanding what the requirements are for your particular environment and it's just not an either or in our minds, it is, as Alex is saying, it's a set of tools to tackle an expanding set of requirements with regard to data design and data management. So it is something that I think we do embrace as let's look at the technology, let's look at the architecture, maybe that's an oxymoron, maybe the architecture of NoSQL. I agree. I think there is architecture to NoSQL. So let's look at that and just embrace it and look at it against the requirements of dealing with as architects in our particular environment. So it's a real environment. Go ahead. As I mentioned, you know, the same companies that they have these large installations of relational databases, particularly the data warehouse inside, the same companies are looking at NoSQL databases. They're the same companies that we are talking to for the generation applications. And so the one thing we see is, we see NoSQL as something that fits on the side, right? It probably sits next to a relational database on the OLTP side. We see a lot more interactive applications or OLTP-like applications that are using NoSQL databases like Couchbase and MongoDB. There's obviously Hadoop on the analytics side, but there are obviously relational databases that will continue to be used for certain use cases. And so I think it's extremely use case-driven. We see some migrations in cases of interactive web-scale applications where if you want to reach out to a user base of 10 million users or 100 million users, perhaps the performance and the scale that you need may not be something that your relational database can provide. And that's one kind of use case. The second use case is completely different applications. Applications with data that's being actually managed is semi-structured. It's object-oriented. And so when you are building a data aggregation platform, for example, for social reasons, let's say you're collecting Twitter feeds, Facebook feeds, LinkedIn information, all this is so varied and diverse in terms of the data model. You need a system that is flexible and that can handle it. And that's actually where data modeling fits into NoSQL. Some people might think that if it's, you know, the key model, if it's semi-structured, you don't really need to worry about data modeling. But data modeling is actually a very important aspect of NoSQL. And the first step that we advise people and developers to think about is actually how do you design your documents, what are the kind of objects that you would create and represent these documents with? And then where do you go from there? So what's the next step from document modeling to staging and then deployment? So I'm glad that you made that segue there because that's also one of our questions. So in most of us when we talk, when we think about data modeling and logical and physical data modeling, we're thinking ERD, traditional modeling tools, and some of the questions that come up is are there going to be sufficient notations and tools for doing the data modeling I'm talking about is not what's the profile structure of the disk, which is what traditional data modeling gets closer to by designing tables and columns and rows and indexes and all that stuff. So where does still modeling, where does it fit in a no SQL project? I'd like to take that one if I could because I've given a few talks on the topic of modeling in the no SQL world and I use examples like Cassandra, the column family idea and document databases like Mongo and Couch base. And I think the fundamental difference between modeling in the relational world and modeling in the no SQL world at least the subset mention is that in the relational world you are modeling the data. And with the idea that I don't a priori know all the uses that I need to make of that data, I'm modeling in a way that's application agnostic. I can use it for new applications at a later time. In the no SQL world, the modeling is basically very application specific and what you're really doing to all intents and purposes is modeling the answer to a query. So for example, as you're modeling in the Cassandra world, what you have to start with is what questions are going to be asked to arrange your data such that it answers the question, which is rather antithetical to the way that it's done in the relational world. So one of the fundamental differences is no SQL databases are typically application specific and the whole concept of the relational database, you know, this is sort of common dates, great ideas, we're all about, I'm agnostic, I can use the data in ways I hadn't previously anticipated. It's a slightly different perspective on that. I think it is application specific in that you need to think about what are the objects, the entities you're modeling. So Karen, you mentioned it's further away from the database structure on disk. I completely agree with that. We look at it as a more logical modeling exercise where it's closer to the entities or the objects you're trying to represent. And then based on that, you create structures or document just rough schemas for those documents, what they might look like. And of course, this doesn't have to be fixed because every document could look different. You have different attributes. That's obviously the power of no SQL. But that's where you would start off. And so it's, in some sense, we think of it, it's closer to an object mapping kind of an exercise where it is about completely abstracting the underlying database or the persistence here with what the application developer or the data architect is building and just thinking about it logically in terms of entities, representing those in terms of document databases and then data change over time where those documents might evolve. That's where we need more tools. And where the existing tools that we have need to be either extended or new tools need to be built to be able to see how these schemas are evolving over time. The big issue, though, is that when you design schemas in the no SQL world, you have to design them around your access patterns because you don't have the concept of joins. So you have to pre-join. And so some new join requirement means create some new table. And there's a similar issue with aggregations. You typically have to pre-aggregate because you don't have effective aggregation mechanisms in no SQL databases either. So really what you have to do is start from what are my queries and model the answers to the queries in the no SQL world. So it's not an arbitrary model what data I want to put in. You've got to think about how am I going to ask for the data out and that will drive the shape of the model far more than what I want to store. And in fact, one of the things that you notice with no SQL databases is that you will think because of query patterns, you're going to have to have duplicate copies of data because of the different patterns by which you query them.  The database is not going to do that for you. It's ironic that we really have a multiverse here in terms of no SQL, which architecture do we really have? There's multiple architectures involved. We're also dealing with a finding problem. You know, it's easy to take data and to file it away and we can file it away in any way with a variety of schema structures. But what Alex is really pointing out is how do we find the relevant information that we need when it comes to the time of actually using what we've stored away. And I think that's a very, very large challenge for a kind of schema. This is more architectural than it is implementation. I think no SQL derived from the need to handle the larger quantities and volume of information and the unstructured aspects of information. Now it's coming back to, we still have to be able to define that structure, that architecture, and gee, we just have queries now built into our architecture rather than basically coming about as a result of a relational design. So I agree with what everyone has said here. And so this reminds me as a modeler of really more how we data model for data warehouse and business intelligence systems, where we've also taken similar changes to how we persist data. So in a data warehouse design, we aggregate data, we optimize the design of the relational database to maximize performance, and we don't know all the questions that are going to be asked, but we try to guess them, or at least support the ones we know we're going to get. So it reminds me of more of that type of modeling. So I'm wondering if a data warehouse dimensional modeler might be happier modeling for these no SQL databases than say someone in my cell who tends to stride in transactional processing data integrity constraints, I don't want any flexibility. Not entirely true. I think it's somewhat worse though. When you look at a star schema and you see your fat tables and your dimensions on the sides, you would think that and that's built that was designed for a data warehouse, that kind of schema. In a similar sense, the document modeling or the column family modeling that you do for different types of no SQL databases is similar, so you think about what are the objects, what are the pieces of data that really fit together, so you can think of it as being normalized and data warehouses normalized in some sense. And I think that a data warehouse architect might find it easier to conceptualize this compared with an OLTP architect where you're just really just looking at tables, rows, columns, orders, customers, users where you normalize as much as you can. So in that I would agree. I think it's a better way or closer way to think of it. And in some sense, given the size of data for that no SQL database store, you could think of them as data warehouses because many of the use cases that customers are building are the aggregation platforms. In some sense, it's a lighter weight version of a data warehouse. Of course, you have Hadoop and the big data systems on the side that do a lot more number crunching and batch processing. The OLTP server, OLAP side, we actually kind of support both these use cases who are starting to see a lot more larger data sets than your typical OLTP system would. I think the difference is a greater than perhaps we might first think.  You're joining the dimensions to the fact table and you're still in the realm of when I update a dimension I have relatively few rows to update. Whereas if you try and do that in, say, a column family or document database, you have to pre-join because you don't have the concept of joins and you have to duplicate all the data. So every row has to contain the related value of every dimension. So there's huge duplication. So I don't really think that NoSQL databases are very good at all at data warehousing and you can even go and look at all the companies that have huge data and you'll find that what they use for data warehousing are more traditional relational star schema kinds of data warehouses. But I think there's a real sweet spot for NoSQL databases but I do not think that data warehousing is it at all and I don't think their models are similar at all to star schemas. The other aspect of NoSQL is an issue in my mind has to do with the maintainability of the data with the extensive amount of duplication. There really is the ability to, in fact, maintain any necessary changes in the data consistently across all of those duplications. I think the data warehouse, that's an issue but it's still a manageable issue because of things like the dimensional modeling. Yeah. That's one of the things about NoSQL databases is that because we denormalize, because we've duplicated, then maintaining integrity is an exercise left to the programmer and we know how good they are. And I think one of the big differences, and correct me if I'm wrong, I see the use cases for NoSQL, just like in data warehousing, it's not about updating data. Even though some of the NoSQL databases are moving towards acid or other ways of being more transactional, I just think that the discussion point, the decision point is based on why are you building this data store? I'm just going to be generic that way. It really is more optimizing for the use and mining is probably the wrong word because it means something to a lot of people, but just the use and consumption of data and less about the transactional data. So if I have a billion row table, it's probably equally likely that I'm going to have updates to some fact in a transactional system to increase the data in that billion row table. So it's bish, and I might need to have to model it in a way that's kind of more flexible or generalized than I have in the past. All things that might have to do with me moving towards NoSQL, but all of these decisions are cost-benefit and risk decisions that have to do with finding the right tool for the right job. So when I say it's like data warehousing, I don't think it means that NoSQL is the only way to do data warehousing or that data warehousing should be done for the transactional things. I'm just thinking the thought process and the architecture process is closer to that. I think it was a real sweet spot for NoSQL. And that is not from the issues that we talked about of availability, so they've done some wonderful things for availability, linear scalability at reasonable cost, but where they've really seen to shine is a very high ingest rate, and they do that rather cleverly by not trying to update, but what they actually do is an append-only model, and that is a good architectural model for very high ingest rate. And then similarly, because the data is already preformed to answer questions, then they can have very low latency, very high throughput ways to answer those questions. So just to give you an example, one of the ways we use Cassandra here in Twitter is Customer Profile. So as a user arrives at an application, we want to quickly look up their profile in terms of preferences, and a database like Cassandra is just wonderful for that because we can have an absolutely huge number of queries given the profile of this user, a perfect use for no single database. It's looked up by key, and here's a complex document which describes their profile, so that it can do that very, very fast, very reliably. So they're a real sweet spot, but I don't think Analytica is a sweet spot. Yes, what we see is with most of our customers that I talk to concur expense reporting, for example, or Orbit, a lot of their use cases are, I call them OLTP-ish because there is really no, there is lightweight transactionality, it's massive, there is consistency. I think a lot of these use cases need strong consistency. There's different databases that support different consistency models, but user profiles towards that Alex mentioned, send stores, metadata stores for large pieces of content, these are some of the sweet spots for no FQI databases, particularly where key value access is part of the access pattern.  And some of our systems that customers deploy are running hundreds and thousands of operations per second. So it would be very difficult to tune a relational database for that use case, particularly for right scaling. So right scaling is a hard problem in a relational database. You could use Oracle has systems like RAC, for example, where you could start, but all, again, based on shared databases. So true shared nothing architectures really give you some of the scalability and the availability benefits that are harder to achieve with relational databases. So they're possible, aren't they? So let's consider VaultDB, for example. There is a true relational database, shared nothing, scale out, 3 million transactions per second, and how many of us need more than that, so let's not say relational databases cannot do this. Let's say that there are very good reasons to use no-SQL databases and sort of economics is one of those reasons. So how do you feel, having heard a lot of all this stuff, how the modelers and data architects are actually should participate in projects where these types of contentious discussions and try to define where's the right place to let day sleep at night? Well, the first aspect of this is really understanding the business requirements.  So if you're looking at an organization that has lots of transactional requirements, has lots of security requirements, data integrity, things of that nature, data architect in dealing with a change is going to need to understand those requirements and also what the capabilities are for the various systems. We've got one customer who's doing agile development for the applications, and they've got, you know, traditional, relational database architecture. And right now, they're finding that it's an interesting discussion that they're having between the need for rapid response to changing business requirements, which is represented by a frequent addition, modifications, or subtractions of columns to their data architecture and the need to maintain the integrity and security of their system so they do no harm to their customers or to, you know, the risk equation. So the approach has got to be one of skepticism, knowledge, and fundamentally, working through the nitty-gritty what is it that we really need to accomplish here and how do we fit the capabilities of the various systems together. I see no single solution on this kind of question. It's really going to be that kind of a process. That's an interesting study that you made about it. So I just wanted to throw in a thing there for him. It's just a little bit of snark here. Having attended a few demos here this week, I'm wondering if one of the best ways we can help out some of these teams is helping them identify and form meaningful names for these different items, math nodes, and everything. I'm seeing an awful lot of stuff like I used to see with database designs in the early 80s where people just picked short and non-unique names for things as well as, you know, helping people write down and I won't use the word document, what decided that thing was going to mean and what it should be called, and then all the other metadata that right now I can't find or I'm not sure where that would go or whether it would go into these databases, but all the metadata that we would normally keep about any data that's persisted, whether it's personally identifiable information, what are its security requirements, what it can be used with, what it should never be used with, what it should never be used for. I don't see a lot of those discussions in the NoSQL design world. I won't even use the word model. Is that some place where we could contribute? You know, it definitely is. At the core, the end of the day, when you get down to, you know, names of objects and definitions or properties of those objects, whether they're relational or non-relational, without paying attention to some of those core basics in terms of autonomy, naming standards, all things of that nature, you know, any system is going to succeed or fail based upon how well the atomic level is addressed. Thank you. Yeah, a couple of issues. So, picking up on the first phase of what Ham was talking about, you know, that flexibility of data model is a very interesting topic. And I think sometimes, you know, mission is understood in the relational world. So, I'll just give two examples which sort of operate at the ends of the scale. So, when I was working at PayPal, we had one table in the system that is absolutely hammered constantly all day every day. And the idea of adding a column to that table was almost impossible because the only way to do it was to log the database for some reasonable period of time, and that man can't take payments. So, flexibility of data model in a situation like that is just a wonderful thing. On the other hand, I think we often forget that you can have flexible models in relational databases. You can actually store name value pairs, guess what, in a relational database. So, you know, I think we ought to understand both ends of the issues of flexible data model. To comment on the second phase of what Ham was talking about, you know, I think metadata is absolutely crucial regardless of what kind of data store you have. If you don't know what data you have and what it means and where it is, then your business is not going to go very far. Amen. And I see a lot of the developers who are embracing these technologies very excited about making data move fast. That's just sexy, hot, fast. The performance demos are always great to see, but the old-school data architect love your data, respect your data, take care of it. I always kind of cringe, and I know these are features of the tool, and I embrace those.  And why we do it? Like, we don't need to have perfect data for all use cases, and we trade that off for something else, right? But what if my bank started using eventual consistency? I would have a perfect example. I always do. So, I'm happy with my bank continuing to use strong assets for their transactions, right? But there are a lot of other applications, right? There are a lot of different kinds of content applications, and particular collaborative applications where, you know, there may be a need for eventual consistency, or I was just posting here on the chat, eventual durability. So, what we do is that user requirements for these applications vary quite a bit. I started seeing it as a spectrum. I think that... So, cashmere is strongly consistent, but Cassandra, for example, is eventually consistent with the ability to have strong consistency. And you can do that, right? And it's tunable. And I think that's where we're actually headed, is a tunable model based on your requirements. Again, it's going back to the application requirements. One thing you'll find, though, is that many systems that you think are strongly consistent are actually eventually consistent. So, you know, you talked about banking. Banking is actually an eventually consistent model. Credit card processing is actually an eventually consistent model. They are not acid-consistent models. It's part of them, and I totally get... That's why we still think in banking of batch overnight updates and posting dates and all of that stuff is the type of eventual consistency where different users accessing what appears to them to be the same data set, getting drastically different answers. You don't want two tellers sitting next to each other with different account balances for me. So, that's a lot of tunability as well. Yeah, so if you think about the two-party transaction, so I send money to you. Yes. So, it's important to record that, you know, I have the money available to send, and I did send it, and I sent it to you. So, all those pieces of information are important to keep an atomic transaction. But when the money actually gets to you, you don't even know I sent it. So, it doesn't have to be part of an acid transaction. That can be an eventual consistency. But, you know, if you design the data to be consistent, then that eventual validation or completion of the transaction, you've got confidence that the result is correct. But if you have an inconsistent design, you know, that bad design will never reach consistency. There will have to be some post-transactional reconciliation, which is very expensive. So, the importance of the metadata design, I think, Alex, you touched on this earlier. You know, we need good metadata design no matter what the architecture is of our system. Yeah, I think so. Going back to the tunability, right? We see, and it's not just consistency, right? It's ASCID. Yeah. It's all these aspects. We've already had added knobs for each of these because some customers are not okay with eventual durability, for example. So, you know, but you have availability in different scenarios. For example, if a node goes down, things that are in memory may not have been persisted to disk, so you lose them. But you have a replica sitting on the other side. And so people are thinking about durability in a different way, where they might be okay with having a replica for persistence. They are okay with isolation where you need to see your own changes, but you're okay if, you know, another agent sees a different view or part of the transaction. But strong consistency and strong durability are the two requirements that come up very commonly with our users, particularly with user profiles, for example, right? The one that we talked about. If I update my preference and I go back to look at and view that change, if I don't see that change there, I'm confused. And so we see that if it's the same user who's made the change, he's usually able to see his own change. And that's one of the requirements that's coming up. But over time, you'll see a spectrum of knobs for assets. So we have just 15 minutes left and we've got some great questions in the Q&A that I want to make sure we get to. I'd like to get to a lot of them. So if we can keep our responses as efficient and highly high performance as possible, that would be great. Just as a really good observation, I'd really like to know the answer to how do I model NoSQL data? The panel seems to be dealing with platforms and projects, not modeling. So as an architect, I'm interested in exposing metadata about NoSQL databases. So, hey, I'd like you to answer that one really quickly. How are we going to model this data with our current modeling tools and environments? Well, as far as what we need, we need basically to understand that we need to have the data about the data. We need metadata design that says, what are the objects in the system? What are the properties of those objects? No matter whether it's a SQL model or a relational model, or a SQL model or a relational model. So fundamentally, without going beyond that point, that's the first thing we need to have. Do you have any tools for that now? Do you think? Certainly, you can use existing relational tools just to build a data dictionary. I mean, really, concepts have been around for a long time in terms of accurate depiction of what is it we're trying to do? Is the meaning of the patterns that we have or the symbols that we have in our system? So a customer means the same thing that everybody within the span or the scope of the system of interest that we have multiple definitions. These kinds of things that we see consistently not happening properly with a lot of customer models is you will get zip code. I had one model that had four different data types and 11 different definitions of zip code. Most of the kinds of inconsistency in a risk system are going to lead to a lot of problems. Some of the metals is go back to the fundamentals. Do the basics, use definitions. Then if you need your schema, whether it's relational or non-relational, you can end to build your architecture from that. That's a good point. We need to keep our answers shorter. Sorry about that. I know. We need two hours for these webinars. One of the first questions is if you're modeling for a specific application, we'd actually end up crippling your data for other uses. So I think that the answer to this is yes, and that's on purpose. I think this is how a traditional architect approaches data modeling. We're thinking about a lot of reusability, but is that necessarily one of the goals? So I think I'm going to use the ability you're talking about multiple applications accessing the same data. And for multiple reasons. Correct. So I think that at the moment, while no SQL databases do not have joins, it's at the moment. These are early products in the lifecycle. So we will have some notion of joins across objects in the future. But that doesn't mean you have 100 tables to represent one object. It means that you would have a couple of related objects and that's the way we think of it. So you would have a few key objects or data about a specific object belongs to one document. And so you still have the ability to use these data across multiple applications because hopefully the kind of objects that you're representing are what we call a business normal form, which is actually business objects that can be used across multiple applications. Excellent. Alex, one of the questions we have in here is the shift to notes as SQL is due to the advent of big data because we've seen that people say lump-ups, especially traditional data architects, tend to think of all these new technologies and platforms and the whole big data growth as being very similar, mostly because they're not about additional data systems. So where do you draw the line and where do those things fit and how do data architects think about notes, SQL, and big data? Interesting. So I think we should separate the evolution from what you have available to you on your toolkit. So from an evolution perspective, how it all started, I think the idea of very large amounts of data and very high-end ingest rates and the structured nature of data all drove the movement towards no SQL. The idea of the huge cost of really large relational databases, commercial relational databases, was also a motivator.  But I think today what you look at is you have a toolkit and you can do big data in relational or non-relational. You can do fast in relational or non-relational. But really what it's about is understanding the trade-offs and picking the solution that's most appropriate for your use case. Well, you sound like an architect there. Sorry about that. Cost, benefit, and risk, you know. That's the whole thing. So the question is, the one that said, after we do these definitions, now what? Do we do an ERD model? Should the data management profession and inclusive here of SQL, no SQL, big data, whatever, appliances, whatever, should we be developing some different notations or different ways of modeling? Because one of the key factors in modeling is I personally don't do modeling just for the nice pictures in the documentation. I want to be able to do round-trip modeling where I can reverse engineer things, forward engineer them, compare one version to another. And I think, is that going to be going or should we be using our current ERD-based tools and handing them over in nice documents? Well, I think the shape of the data. So the shape of the data is kind of a somewhat tabular sort of like a Cassandra Collins family. That's basically a big flat table. Or it might be, you know, a hierarchical document shape. So, you know, think XML JSON. So if you've got those two shapes, I think our current tools already deal with those. And so I don't think there's anything special about modeling that requires new tools. Mostly, you know, what are these new databases that are about are physical implementations. We usually talk about physical trade-offs. It's the physical side. But, you know, the logical modeling, they're familiar models by the tabular or, you know, hierarchical document. I know what those tools are that I can point to Cassandra and compare it to my logical or my physical ERD-based model and be able to say what's different, what's the same. Like the real sort of, you know, getting a data modeling tool like an engineering tool, not just a diagramming representation of some business model. Like I want to do both, definitely. But we're not there yet.  And the long-trip engineering is not so much a limitation of the notation as the technology and also the procedures in which we use. You know, the way we actually conduct data management, I think, is at the core and how we use things like standards in our processing as well as things like oversight, you know, governance, you know, quality control and things of that nature. This has been well documented for years and courses and books and so forth, so. Yeah. So, that's a good point. Yeah. Going back to your point about, you know, how do you run it against Cassandra or couch base, that does not exist right now. For one, there isn't a common language or a common framework that can be used across databases. And one has their own notion of how to do an update, which looks very different. Everyone has their own notion of metadata. Couch base does not have any data other than databases. Yeah. There are no tables. There are no, you know, that's pretty much it. And so until you have every, so what I'm getting to is it's early. It is early. I think that modeling tools might exist for your logical definitions and modeling, but when you want to relate it directly with the underlying database, that's something that will evolve over time. And we will actually need to be able to do reverse engineering to find what a schema is for a common schema across documents is given data that already exists. So you might actually have to do it backwards. Yep. Data derives the model from the data itself. That's a really good point. And I joke that's going to be my next startup. Well, time. So we're coming to the end of all this and I really would like to talk more about this. So maybe we'll have another more focused webinar later in the next year or something like that so that we can continue this discussion because our data architects and data modelers to be part of those types of discussions. So I'm going to ask each of my panelists, do you have any short 10 seconds, one sentence takeaway that you want everyone to hear? So, Ham, I'll start with you. So, I think that the, thank you for the opportunity. I think we have here new opportunities to look at, you know, changes in our problem set. And I think that if we start looking at the measure of relational methodologies and no-sequel methodologies that we will wind up with some exciting new changes. Good. You remember that the broad range of no-sequel solutions adjust, you know, new additions to your toolbox. You still need to model.  Excellent. I would agree data modeling is important no matter what backend persistence mechanism you use. And I think relational data architects have a huge opportunity to actually reuse and reinvent themselves. The concepts remain the same. It's just that the backends are different. Excellent. And I didn't pay any of you to say all that. Did I? Except they and our champagne here. And Shannon. So we're coming just up at the end. I really want to thank Couchbase for sponsoring this webinar for you being here to see. That's so nice to have people from all the communities and all the right, wide-ranging technologies, exciting new technologies that are happening. I also want to thank CA again as well for being a long-term sponsor and Sandhill Consultants as well. And we're all in this together with the trying to figure out where we're all going to fit. I thank Shannon again for being an excellent cat herder for all of us and getting us here and getting the audience there. I wish we could have gotten to all the audience questions because I still consider all you audience people part of the panel. I want to thank Christina for lending us her room so that we could do this. And my panelists and Alex, thank you so much for providing your insight. I have attended a few of your talks and I really appreciate your making this type of knowledge. Any data architects out there that get an opportunity to hear him talk about NoSQL implementations and these design considerations, it's definitely worth doing at EDW. His room was packed and I got one of the last actual seats in the room so I was very happy about that. And Ham, we've known each other for a long time and have come through the traditional data architecture world so I'm really glad you can join us. And Shannon, off the top of your head, do you remember what our topic is for next month? You can look that up right away. While she's doing that, I should be better prepared here. While she's doing that, I wanted you to know that really soon we'll be turning off the recording for the session and we're going to stay on as long, probably about another 10 or 15 minutes so that we can continue some sort of offline discussions about these things. And Karen, I just want to reiterate what you said and thanks to Couchface and thanks to today and thanks to Sandhill for sponsoring you guys or just if you can't do it without you guys and just so appreciate it. And the next month's topic is data model pattern. October's is data model pattern. Okay, in October we're talking about data model pattern. What's wrong with teams? Data modelers and project managers. Oh, excellent. That should be good. Thank you guys. You know some project managers that want to talk data modeling. Thank you everybody for attending today. I will turn off the recording so that we can have our little open discussion off the books. And again, everyone, thank you so much. I love how much you guys interact and participate in these sessions. So it's awesome.


My name is Andrea with Food and Mentory Adventures in Food and today is Tuesday, February 1st and I promised to give an update on the Pantry and Freezer Challenge. I did the Pantry and Freezer Challenge from January 3rd until the 31st and so I just wanted to provide an update for you. So this is our pantry and I made some progress in the pantry probably used about maybe 30% of the items that were in the pantry I still have a long ways to go so I'm just kind of letting you all see what's still in there it's not organized at all and then right over here bottom shelf I have a lot of pasta this second shelf I've got a bunch of like sauces and things like that I've got some waffle mix and then I've got all my canned stuff right here and right up here I have condiments and then I have a basket full of like seasoning packages and things like that it's not organized because I feel like there's so much stuff it's kind of hard to organize and get to stuff so even though I've made some progress I've got a ways to go I've got Howard cereal up there we've got canned goods spaghetti sauce you know miscellaneous things chips and etc right up there so this is what the pantry looks like like I said I've got a long ways to go so let me show you all the cabinet where I have extra food extra storage okay so here's the cabinet the other side of the cabinet I just have dishes and things like that but I have the food over here so I have made some progress here I was able to organize this cabinet a little bit better so some things are definitely missing from that I didn't realize how many cookie mixes I have right there on the second shelf and then definitely went through a lot of these like pastas and things like that side dishes rice noodles things we went through a lot of that during the pantry challenge so let me show you our inside freezer so I think the freezer the inside freezer looks pretty good so this top shelf is pretty cleared out up here I just have like cookie dough sweet treats things like that as you can see I've got some whipped topping this shelf is all prepared food so I've got frozen pizzas and spring rolls a lot of Asian food some individual pizzas I've got tamales I've got I've been working on these these are little mini corn dogs Trader Joe's stuff like beef bulgogi sorry not beef bulgogi it's rice with kimchi so that's pretty good like I said a bunch of Asian stuff because we love Asian food so that's on the second shelf prepared food items and then this third shelf is just meat and leftovers so right here leftover pot roast but then I've got chicken I've got Costco ready to cook bacon I've got some frozen shrimp so it's just meat um in this shelf and then this very bottom shelf it's kind of bread products I guess so I've got some pie shells I've got some Hawaiian rolls I've got some yeast rolls I've got some what is that non mini croissants and then in this drawer the first drawer is just like frozen like peppers and onions things like that no actual vegetables per se like corn just like onions and peppers and things and then in the second drawer I have broccoli I have corn and beans things like that so these two drawers were so stuffed with things I couldn't even get into them so we did make some progress here and that's the alarm on the door and then in this drawer I've got cheese that either I didn't get to or we opened and ate some and I put it in the freezer before you know it could go bad so I've got that there and then we've got some more cheese and then down here I have nuts so almonds pecans things like that so next let's go to the outdoors or the garage freezer okay so we are in our garage freezer now so let me show you what we've got I'll start up here on the door the refrigerator we've got cookie dough some mini croissants from Trader Joe's and I love so much and then butter we actually did go through quite a bit of butter and cookie dough during the challenge and then I have a bunch of pork sausage and a bunch of ground beef and there's ground beef in this Ziploc bag too and then more pork sausage on the bottom so I found all that pork sausage on sale it was like $1.99 or something so I racked up at the time on the top shelf I've got poultry so I've got chicken and I've got turkey and these two baskets and then at the very bottom I have some ground pork in this drawer I have whoops I have more sausage stuff I found on clearance and then I've got hot dogs in there as well a couple of pizzas so there's pizzas there and then pizzas in the very back that I need to get through from Costco I bought them a long time ago and so that's what's in this drawer this shelf I've got taco salad shells that I love so much because they're hard to find so when I find them and when my mom finds them she buys them for me and I freeze them I've got some bread dough and in front right here those are pork steaks that I froze right down here I've got some Costco bacon some more of those taco salad shells tempura shrimp chicken spring rolls pizza pie crust that I got during I think probably like November I guess at Sam's or someplace like that and I just froze them and then that's the alarm going off guys nothing I can do about that and then some beef right there and then some french fries and then mandu beef mandu that we love and then this very bottom shelf we've got some pizzas and we've got pie crust meat lasagna painful shrimp all sorts of good stuff and then in this drawer I've got a bunch of beef my parents bought a I think they bought a half a cow and they gave me some of their meat so I've got a lot of beef in here I've got some chicken wings in here there's some um um ham slices down there then also some chicken nuggets at the very bottom that's that's all that's in here oh so that is it for the freezer so you can see that we have a lot of stuff left I'm going to continue on with the pantry challenge if I do see something in the grocery store and it's something that we use and it's on sale I'll probably go ahead and pick it up but I'm going to keep working on this freezer and pantry because we have so much stuff left so anyway I hope you all really enjoyed this video if you did please make sure to give us a thumbs up if you have not subscribed to our channel we'd love to have you thanks so much for watching and we'll see you all next time


Thank you very much. I'm very happy to be here today, and I thank all of you for coming. I know that all of us have been praying and thinking very deeply about the current health crisis with the pandemic of the coronavirus, and I know we're all thinking about what we can do, including how we're putting it to practice today, the guidelines from the government of California for public gatherings. So thank you for complying with that. I'm hoping that this lecture today will help all of you understand why I think that prayer is a practical and viable tool in helping us pray for the world. I thought that I would begin by telling you how I like to introduce myself to people.  I'm a computer scientist because, as you heard, I also have a degree in computer science, and I'm a Christian scientist. I am a lifelong student of the science of Christianity. So as you can tell, I love science. What I love about science is how it asks us to look beyond what we can see. It seeks order from seeming chaos. It pushes the limits of what's possible and emphasizes what we can do instead of what we cannot do. I really did love my career in the earth sciences, but long before I was a meteorologist, a manager, an engineer, software developer, I was an 11-year-old girl who was scared of thunderstorms. I'd like to share with you today how I rose above that fear. I'd like to share this story with people because I think that all of us have, at one time or another, felt afraid about something that seemed so much bigger than us. I think that's how a lot of people have been feeling recently, and I think that you'll find that some of the ideas that helped me rise above the fear of severe weather can help all of us today in praying for the world about the fear of contagion. So when I was 11 years old, my family lived in Kentucky. We were just getting ready to move to the state of Oklahoma. About two weeks before we moved, I saw us sitting on a table in our living room in a magazine with a picture of a tornado on the cover and across the top, a single word headline, Oklahoma. This caught my attention.  I read the article and it said among other things that Oklahoma is in the heart of what's known as Tornado Alley, a region of the central plains of the United States that has the highest frequency of tornadoes. This made me more than a little apprehensive. I didn't realize at the time, but two weeks later when we moved, it was right at the height of tornado season in early June. For the first two or three weeks we lived there, we had severe weather of one kind or another almost every night. I was terrified. I literally thought that we had moved to the most dangerous place on earth. Over the coming years, I spent many hours hiding in my closet with my dog under one arm, I had my radio under the other, and I was just cowering in fear waiting for the storms to be over. But as scary as I was, I could see that my parents were completely unafraid, and I knew their fearlessness came from their trust in God. I wanted more than anything to feel that freedom from fear that they felt. They talked to me about how they prayed about things. For example, we talked about stories in the Bible, like the story of Elijah, when God tells him to go stand on the mount before the Lord. And the Bible says that a great and strong wind rent the mountains and broken pieces the rocks before the Lord, but the Lord was not in the wind. And after the wind and earthquake, but the Lord was not in the earthquake, and after the earthquake, a fire. But the Lord was not in the fire, and after the fire, a still small voice. Bible commentaries I've read agree that in this experience, Elijah was learning about the nature of God as not in the wind or the earthquake or the fire, but in the still small voice. I was also familiar with passages from the book of Psalms, like this one. The Lord on high is mightier than the noise of many waters than the mighty waves of the sea. And this one, God is our refuge and strength, a very present health in trouble. I was also familiar with stories in the New Testament about Jesus stilling the storm, about Paul and all his shipmates being saved from the shipwreck at sea in the violent storm, all because of their trust in God. I knew I couldn't just will myself not to be afraid. I mean, let's face it, I was trying that and it wasn't working. I knew this had to be a spiritual victory. So I began a spiritual journey that lasted many years, and there were important milestones along the way. Little by little, I was coming to understand more about God as love, not just that God is loving, which is true, but that God is love itself. As the Bible says in the book of 1 John, God is love. And I was coming to trust more and more in the ever presence and the omnipotence of this God that is love. Let me just share with you one of those milestone experiences I had. I was in high school at the time, and we were in one of those overflow classrooms in Spanish class. And, you know, this is one of those buildings that had the metal walls, the metal roof, freestanding prefabricated buildings. And while we were in class that particular day, a severe thunderstorm came roaring through town, and large hail began to fall. Now, you can imagine how loud that was on that metal roof. It was so loud, in fact, that we had to suspend class. I lived in Oklahoma for a few years by this time, and I thought a lot about how to pray in the face of severe weather. But I was still very much afraid. There were a lot of concerned looks going back and forth among everyone in the classroom that day. Right at the height of the storm, with that pounding hail and the howling wind, I just decided that I wasn't going to be a victim of violence anymore. That's exactly how it came to my thought. I am not going to be a victim of violence anymore. And I stood up in that classroom and I shouted at the top of my voice, God is love! I didn't care what anybody thought, and they couldn't hear me anyway over the hail. What I was doing in that moment was I was declaring the allness and the oneliness of God good, and the powerlessness, and the nothingness of evil. And the most amazing thing happened. I felt such calm. Now, I can't say that every last vestige of fear left me, but there was a marked difference, and almost immediately the hail stopped and the storm quieted. This made quite an impression on me that stating a simple biblical truth like that, God is love, could have such an effect, not only in the atmosphere of my thought, but in the atmosphere of the whole community, literally and figuratively. And I didn't think of this as a coincidence. Rather, I saw it as divinity, or the divine, or God embracing humanity. An important lesson on my spiritual journey. Now somewhere along the way, once I got into college, I decided to major in meteorology. Some people might call that ironic. Some people might say that I was just trying to understand my nemesis because I was still having to pray about fear. I can honestly say that it was not my motive just to understand my nemesis, but even if it had been without my realizing it, I can also honestly say that it wouldn't have worked. Because the more I found out through my study of meteorology, the more I realized how much we still don't know about the formation of storms, about the life cycle of the path. If anything, my study of meteorology was making me more apprehensive, not less. But I was continuing to gain in my study of Christian science. My spiritual journey culminated in a broadcast I made at the National Weather Service forecast office in Oklahoma City. I was working there as a NOAA weather radio announcer. We were having a lot of severe weather across the state on this particular day, and as is typical in a situation like that, I was doing a lot of live broadcasting. So at one point I went over to the printer and I ripped the copy off and I took it over to the microphone and I started reading on the air cold. And this is what I read. A tornado has been spotted two miles southwest of the Oklahoma City Airport, moving to the northeast at 25 miles per hour. I was at the airport. I realized while I was reading this copy from the public that I was reading a tornado warning for my exact location. And I wasn't in a storm cellar or a bunker. I was at a little building just exposed out in the middle of the airport grounds with no basement, but I also realized something wonderful. I was completely unafraid. For the first time since I was 11 years old in the face of severe weather, I was completely unafraid. I knew I was where I was supposed to be, doing what I was supposed to be doing, and God blessed that right activity. God blesses all right activity. And I knew, I just knew that nothing could keep me from it. Not even a tornado. The tornado dissipated and the storm passed. And I realized something else wonderful. I knew right then I would never have to be afraid of the weather again. And I haven't been. I've been in situations just that serious, but I finally learned that God is not in the wind or the earthquake or the fire or the epidemic, but in the still small voice of love. And in the still small voice of love is where true power really lies. Over the years, I've pondered the fact that my study of physical science didn't help me rise above this fear, whereas my study of Christian science did. And it's caused me to think more deeply about this science of Christianity. So I'd like to share with you today some things that have occurred to me over the years about this science. First of all, I hope that nobody here is confusing Christian science with Scientology. That's a common misconception, but in fact the two are very different. They sound kind of alike because of the word science, but unlike Scientology, Christian science is based entirely on the Bible, on both the Hebrew scriptures as well as the New Testament. This is the King James version that I have here. Science is the study of something. Christian science is the study of the Christ, the study of the life and teachings of Christ Jesus. I've also always thought that Christian science fits the definition of science given in Webster's unabridged dictionary. Let me just read you part of that definition. Science is a system of knowledge covering general truths or the operation of general laws, especially as obtained and tested through use of the scientific method. So we have general truths, the operation of general laws. Earth science studies physical laws thought to be governing the Earth in order to understand and apply those laws for the benefit of humanity. Computer science studies laws governing computers, the laws of circuitry and logic in order to understand and apply those laws for the benefit of humanity. Christian science studies the laws of God, divine laws in order to understand and apply those spiritual laws to the benefit of humanity. So this point explains both the similarity and the difference between physical science and Christian science. Both are interested in law and the application of law, but physical science draws its conclusions based on the evidence of physical sense of the material senses while Christian science draws its conclusions based on the evidence of spiritual sense. I'll talk more about that as we go along. Back to this definition of science. The definition talks about something called the scientific method. So I'm guessing that that's a term that all of you have heard at some point. I think the scientific method of research is currently being taught in the first few years of formal education in the United States these days. For those of you that may not have been in elementary school for a while, I'll just remind you of the six steps of the scientific method. Step one, ask a question. Step two, do some research. Step three, form a hypothesis. Four, test the hypothesis through experimentation. Five, analyze the results. And six, publish the results. So for example, one time I read a firsthand account of someone who had had a conversation with Sir Isaac Newton in which Newton told him what he thought when he saw the apple fall from the tree that led to his discovery of the law of gravity. Apparently that really happened. Isaac Newton was sitting among some apple trees when he saw an apple fall and here's what he said he thought. Why should that apple always descend perpendicularly to the ground? Why should it not go sideways or upwards but constantly to the Earth's center? Assuredly the reason is that the Earth draws it. So then he went on to describe what he was seeing in mathematical terms and then tested his equations by looking at the motions of the planets in the solar system. And once he felt that his math was accurately describing what he was observing then he published the law of gravity for all the world to use. So that's an example of the scientific method. Well, I have always thought of the discovery of Christian science as having followed the scientific method. So I'd like to take a few minutes now and talk about how the discoverer of Christian science, Mary Baker Eddy, applied the scientific method in her discovery. Who remembers the first step in the scientific method? Yes, you were listening. Good. Ask a question. And the question Mary Baker Eddy asked was, where is true healing found? She grew up in New England in the early 1800s in a family of congregationalists. They loved God and they loved the Bible in her family. They read it every day. She was rather frail and sickly as a child and this condition continued well into her adulthood. But rather than is turning her away from God, she sought even more earnestly to understand God's role in healing. And she did this while investigating various methods of treatment which took her to the second step of the scientific method doing some research. She investigated not only conventional Western medical treatment but also other forms of treatment available at that time like homeopathy, hydropathy or water therapy, mesmerism or hypnotism. And she did find some measure of help for each of these different methods of treatment but she didn't find permanent cure. She was also left wondering about all of those stories she loved in the Bible in which people were healed through prayer alone. All during her research Mrs. Eddy remained a devoted student of the Bible. She really took to heart Jesus' words, He that believes on me, the works that I do, shall he do also. And it was her deep desire to understand the meaning of those words that propelled her forward. During this time Mrs. Eddy not only received homeopathic treatments but she also administered them. She later said one of the cases that came to her was a falling apple on her road to discovery making reference to Newton's experience. This was a case of a woman who had been diagnosed with Dropsy or an abnormal retention of fluid. Somewhere along the way Mrs. Eddy realized that she had prescribed for this woman the same medication that a previous homeopath had prescribed and she started to become concerned that a prolonged use of this particular drug would have an adverse effect on the patient. But the patient feeling that she was being helped by this drug didn't want to give it up. So Mrs. Eddy out of concern for the patient decided to try giving this woman unmedicated doses but she watched her very carefully. The woman continued to improve. At one point this patient said she would try giving up the drug but on the third day she relapsed and then she said she couldn't get along without it. So Mrs. Eddy started giving her the unmedicated doses again and this woman started to improve again and ultimately she completely recovered under that regimen. This case proved to Mrs. Eddy that it was not the drug but the woman's faith in the drug that had cured her. There was also another case of healing that Mrs. Eddy later called a falling apple and this one led her to the next step in the scientific method forming a hypothesis. This was her own healing in 1866. She was walking on the icy streets of Lynn, Massachusetts when she slipped and fell. It must have been a serious fall because a local newspaper at that time reported that she was taken up in an insensible condition to a nearby home. The next day she was moved to her own home and pronounced in critical condition. After several days of being treated by a physician with no improvement she was diagnosed as having suffered severe internal injuries and the doctor felt there was nothing more he could do. In her extremity Mary Baker Eddy asked for her Bible and while she was reading an account of Jesus healing someone she gained a deep insight into the nature and reality of being. So much so that she got up from her bed, she got dressed and she walked into the next room where her friends were astonished to see her recover. She later wrote that she was ever after in better health than she had before. The days of her being an invalid or a semi-invalid were over. As Isaac Newton had pondered the falling apple Mary Baker Eddy pondered what had happened to her and as he had done she formed a hypothesis. Hers was based not on human conjecture but on what she called divine revelation. And it was this. That spiritual healing of sickness and sin as Christ Jesus practiced it is based on universal divine laws that can be learned and applied today. Here's what she wrote about that experience. I knew the principle of all harmonious mind action to be God and then cures were produced in primitive Christian healing by holy uplifting faith but I must know the science of this healing. So she continued her research focusing intensely on the Bible. She was convinced of the truth she glimpsed but she knew she needed to understand it better so that she could explain it clearly to others. And she also knew that she shouldn't publish her theories and her deductions until she had thoroughly proven them. So as Mrs. Eddy moved to the next step in the scientific method she submitted her discovery of the rules of Christian healing to what she called the broadest practical tests.  I'll just share a few of them with you here. And these were not just minor ailments, they ran the full range of physical difficulty and disease including healings of contagious disease, diphtheria, tuberculosis, many of these in person and Mrs. Eddy feeling no ill effect from having helped those people. In other kinds of cases specifically there was a case of a man who wrote to Mrs. Eddy that a large timber had crushed the bones of his foot. He asked for a prayer treatment. Mrs. Eddy gave him one and the man reported that he was restored at once. He said that very day I put on my boot and walked several miles. In another case Mrs. Eddy's prayers cured an entire family. Very quickly the father was healed with rheumatism, the son was healed of the disease of the vowels, the daughter was healed of deafness. Her case had been given up as incurable. Many years later Mrs. Eddy was asked about this particular family and how those healings were accomplished. She replied, it is love that heals. Only love. And by this she meant the love that is God. She always gave God the credit for being the healer. There was a case of a man who had been diagnosed with enteritis and bowel stoppage. When the medical community felt there was nothing more they could do for him. Someone sent for Mrs. Eddy. Her prayers cured him that day. I like this particular example because this case was reported in a Boston newspaper. It was called a remarkable case. Not only because the man had undergone this wonderful physical healing but also he had undergone a complete character transformation. The paper reported that his wife said the thing she was most grateful for was his radical change morally and spiritually. Mrs. Eddy moved to the next step in the scientific method, analyzing the results of her work. She realized that as this last case illustrated people were not only being healed physically they were being renewed morally and spiritually. She came to see that Christian healing must be accompanied by a renewed sense of oneself because the true healing is mental. This is consistent with stories in the Bible in which people's names were sometimes even changed to denote their changing character. Jacob was renamed Israel. Simon became Peter. Saul was renamed Paul. She also found that people were healed most readily when they entrusted themselves wholeheartedly to her treatments. So conventional medical treatment begins from the premise that there is a broken physical body that has to be fixed. Christian science treatment begins from the premise that God's children created spiritually are already perfect and our task is to understand this more clearly. You can see that these are two competing points of view. Mrs. Eddy found that those were healed most readily whose thought was not divided between these two perspectives. At this point Mrs. Eddy was having consistent success in her own healing practice. Early in her research she had reasoned that if in fact her work was based on universal laws that she ought to be able to teach these laws to others and they ought to be able to repeat her healing work. So she began taking students. Over the years some students were more successful than others as is the case in any scientific endeavor but there was consistent progress in the growth of Christian science and in a number of cases healed as a result of its practice. So after nine years of research and practical application Mary Vaynereddie felt she had proved the truth of her hypothesis. That spiritual healing of sickness and sin as Christ Jesus practiced it is based on universal divine laws that can be learned and applied today. So she published her results in this book. It's called Science and Health with Key to the Scriptures. This was first published in 1875. Mrs. Eddy continued healing and teaching after it was published. She taught many students and ultimately formed a board of education which continues today offering avenues for people to take classes in learning about the science of Christianity. And she also edited Science and Health hundreds of times always looking for better, clearer ways to explain her discovery. There's a place in Science and Health in which Mary Vaynereddie gives what we might call the spiritual sense of the scientific method in three steps. Divine revelation, reason and demonstration. I'll take just a minute and talk about each of those. First, revelation. You may be interested to know that Mrs. Eddy included proof as an aspect of revelation. She never felt that she could reconcile any spiritual concept that come to her through divine revelation unless she could validate it through practical application. Actually, this concept of revelation was never a huge leap for me as a physical scientist. Remember I said one of the things I love about science is how it asks us to look beyond what we can see. New who looked beyond the falling apple. Scientists using the Hubble Space Telescope looked beyond darkness. They trained the telescope on a dark region of the sky a region where nothing appeared to be hypothesizing that there actually was something there if they could just look deeply enough into space. Sure enough, thousands of never-before-seen galaxies with billions of stars became evident. Christian science teaches that a deeper look into the realm of infinite divine love reveals the nature of God and God's love for all creation and this deeper look into the spiritual cosmos is made evident through transformed lives including physical healing. The second of this three-step summary of the scientific method is reason. All scientists reason through things logically. Actually, computer science is all about logic and reasoning. Isaac Newton reasoned that the phenomenon of the falling apple could be explained through universal law. There may be great reason that the phenomenon of Christian healing could be explained through universal law. It does take logic and reasoning in order to apply these laws for healing and I'll talk more in just a minute about how I've done that. But first, the third of this three-step summary of the scientific method is demonstration. Proof. All scientists require proof and I am no different. I don't study Christian science because somebody told me I had to. In fact, I tell people that in my case the interesting question is not how I started studying Christian science because I grew up in it. It's not very interesting. The interesting question for me is why I stay with it. I stay with it because I've seen it work. I see its relevance and its effectiveness in my life. Or to say it in slightly more scientific speak, I have my own evidence that supports what Christian science claims to be. For example, one time I was baking cookies. And while I was baking cookies, I was thinking about a friend of mine but I'm sorry to say, not in very complimentary terms. I was pretty annoyed with my friend. And while I was in the midst of all of this mental turmoil, I went to take a fully baked tray of cookies out of the oven but I forgot to use an oven mitt. Now I got those cookies almost all the way to the counter before I realized what I'd done. And by this time, my hand was seriously burned and I was in a great deal of pain. What I did in response to this acute health crisis was the most natural thing in the world for me. I called a Christian science practitioner. A practitioner is someone who is devoted full time to helping others through prayer. So I told this experienced Christian scientist about the situation and she reminded me of my spiritual identity. She said to me very firmly, the flame shall not hurt you. I recognized those words as being from a hymn, an old Protestant hymn and there are a couple of arrangements of that hymn in the Christian science hymnal. Also realized what she was doing was stating a spiritual law and her point was this law applied to me. As I say her words, the flame shall not hurt you. Reminded me of something Jesus said. Jesus said God is spirit. And I knew that I, as a child of God, as the offspring of spirit, had to be like God. I was reminded that my true identity is spiritual and not material. And furthermore, I realized that a spiritual being could not in truth be touched by a material flame. I could see that in a very real sense I had a choice to make. I could either accept that the physical law of the oven flame governed that situation or I could rely on spiritual law which supersedes and annals physical law as Jesus proved every day in his ministry. And because of the many times I have relied on spiritual law to good effect, I chose spiritual law. We got off the phone, the practitioner agreed to pray with me about this and I went and I looked up that hymn in the hymnal and I'll just read the full verse. When through fiery trials your pathway shall lie, my grace that is God's grace all sufficient shall be your supply. The flame shall not hurt you. I only design your dross to consume and your gold to refine. Well, this certainly seemed like a fiery trial as the hymn put it. And I was reminded of that story in the Bible about the three Hebrew men who were thrown into the burning fiery furnace. Their trust in God was so great. Their understanding of their inseparability from divine life was so strong, so clear. Not only were they not burned, their clothes were not burned, the smell of smoke didn't even pass on to them. I found this very encouraging. For the next 15 or 20 minutes I just held tenaciously to this concept of myself as a spiritual being, as a spiritual idea of divine mind of God. And after about 20 minutes I was so grateful to be able to call the practitioner back and tell her that there had been significant progress. The hand which had seemed quite abnormal in both appearance and function was rapidly returning to a more normal state. And the pain had also diminished somewhat although it wasn't gone. I was talking with the practitioner the second time. I started berating myself about those unloving thoughts I had about my friend. The practitioner interrupted me and she said, again, very firmly, you never had an unloving thought. Well, that was in complete opposition to what I've been thinking when I was baking cookies. And yet I realized again what she was doing was stating a spiritual law. And her point was this law applied to me. It was logical to me that God, who is love and only love, could never hate. He could never create a child who could hate. I thought about this hymn again. I only designed your dross to consume and your gold to refine. You know, the dross is that waste material that's sloughed off in the refinement of precious metals. And so I reasoned that in this case, the dross to be consumed, the dross to be thrown away, was a false concept of myself as less than loving. And the gold to be refined was the right concept of myself and of my friend as well. I didn't want to leave my friend out of my prayers. The right concept of both of us as loved, as loving, and as lovable. And as I thought about these things, all of that mental turmoil I've been feeling just simply washed away and right along with it, the pain diminished until it was gone. The next time I really looked at my hand was maybe a couple of hours later and it was perfect. There wasn't a mark on it and I was able to use it normally in every way. I also mentioned that my relationship with my friend was restored immediately, restored like new, just like the hand. Now some of you may wonder that I was so willingly turned away from the physical evidence like that. I'll tell you the truth. The most real moments of my life, the most compelling moments, have not been revelations of physical science or a brilliantly executed piece of software code, although those things are rewarding. They are. After 11 years on the Hubble Space Telescope project, I still love all things Hubble. But the most rewarding, the most compelling, the most real moments have been revelations of divine science. I'll give another example about that in a minute. It is true that in the practice of Christian healing there are no thermometers or wind gauges or blood pressure measurements. But Christian scientists do gauge thought. Jesus said to the man at the pool of Bethesda, will you be made whole? I've often thought he was asking the man to measure where his thought was. Are you willing to be made whole? Often when Jesus healed someone physically, he would say something to them like, go and sin no more or be not afraid. First and foremost, he was addressing thought. The connection between thought and physical healing cannot be measured physically, but that's what people are always trying to do, trying to devise some sort of medical test in order to validate prayer. I contend that it can't be done because prayer and its effect can be discerned only through spiritual sense or what we might call the Christ Spirit. The person who best understood Christ was Jesus. In fact, Mary Baker Eddy called Jesus the most scientific man that ever trod the globe. She called him this because she said that he plunged beneath the material surface of things and found the spiritual cause. Jesus said, as I hear, I judge. He told his disciples, blessed are your eyes for they see and your ears for they hear. He was talking about hearing and seeing spiritually. Jesus is the perfect example for us because he taught that this Christ, this sense of the power and presence of God that he relied on to heal and save others 2,000 years ago. He taught that this Christ is eternal. One time Jesus was deep in prayer. He was praying for himself or his disciples for their ministry. And he said, I'm not praying just for these alone but for all those that shall believe on me through their word. That includes all of us. Paul wrote about this to the Corinthians. He said, I have not seen nor ear heard. Neither have entered into the harm of man the things which God hath prepared for them that love him. But God hath revealed them unto us by his spirit. For the spirit searcheth all things, the deep things of God. The spirit that Paul is talking about here is the Christ spirit. The Christ is what enabled me to overcome the fear of severe weather and enabled me to learn how to love my friend better. Christ can enable us to rise above the fear of contagion. If this seems esoteric, you might think about it this way. We don't see wind or friction or gravity but we see and feel the effects from these things. The love of a child. Now that is real. It's tangible. But you can't measure that physically. Or think about music for a minute. Say that you have two pianists and they both sit down at the same piano at different times and play the same piece of music. Maybe they both get all the notes right, all the rhythm correct. Technically, it's perfect. One of those performances may leave you feeling unmoved while the other performance may absolutely uplift you, transport you. The difference in those performances is not the notes on the page or the piano. It's the inspiration of the musician. You can't measure that physically but you know. You know when it's there and when it's not. Again, what I love about science is how it asks us to look beyond what we can see. Along this line, there is a helpful definition in science and health of the biblical word profit. It's a short definition, so I'll just read it here. Profit a spiritual seer. Disappearance of material sense before the conscious facts of spiritual truth. This definition reveals to me that every prayer in Christian science is a kind of prophecy. It's exchanging a material view of things for the conscious facts of spiritual truth. And therefore, every healing in Christian science is the fulfillment of prophecy. It's looking into God's infinite law of infinite good and being a witness for the unfolding of that good. Let me just give you one more example which further illustrates this idea of seeing spiritually. One night, I was awakened with a stabbing pain in my side. It was more than annoying. It was a little alarming. And I tossed and I turned for a while and finally I got up and I opened a book on my bedside table. This was a book written by Mary Baker Eddy about Christian science called Unity of Good. I opened this book at random and my eyes fell on a sentence at the top of one page. It said, A lie has only one chance of successful deception to be accounted true. A lie has only one chance of successful deception to be accounted true. When I read that, I had a brilliant flash of spiritual illumination. The best way that I've been able to think to explain that experience is to say that I glimpsed absolute truth. What became so clear to me in that moment was that God's universe is the only universe there is. It is entirely spiritual and it is entirely harmonious without any bitterness or hatred or discord of any kind. In that moment, I could see that even the suggestion of pain, even the suggestion of in harmony could not be true. It couldn't be true.  It was just crystal clear to me. At once, the pain was gone. It was so sudden that it startled me. It startled me physically as well as mentally. It was a jolt. My concept of who I was and my place in the universe changed in an instant and this healed me physically, instantaneously. This change in consciousness resulted in a change in the body. That moment of divine consciousness of spiritual clarity that glimpsed of absolute truth stands out as a beacon to me in my life and I cannot ignore it. I confess that sometimes I wanted to. Sometimes it just seems easier to believe the way the world believes, but I cannot ignore it. It's evidence that is more real to me as a scientist than anything else I have ever experienced. Now, I have one more point that I would like to make before I begin to wrap things up here. If I'm having trouble applying the teachings of Christian science or not getting the results I'm looking for, I don't abandon the science, but I might question the application. It's like math. Sometimes just because I had trouble working problems in calculus or using imaginary numbers, and yes, there is such a thing as imaginary numbers in math, I didn't dismiss the whole of mathematics as foolish. Rather, I realized that there were other principles of math I've been able to prove. There were others who've been able to work these problems. And I knew that if I could just understand the principles that lay behind these problems better and practice applying them, that I too would be able to work the problems at hand. Similarly, these glimpses of absolute truth, along with the body of other evidence I've gleaned in my life through healing, through studying application of Christian science, along with the thousands upon thousands of healings I know about from others who have practiced Christian science over the last hundred and fifty years. All of these things tell me there must be something to this science. There must be. And it makes me want to learn more. Astronomers are always trying to find out more about the physical universe by looking farther and farther out into space. One of the reasons they do that is the farther out you look in space, the farther back in time you go. The farther out you see in space, the farther back in time you're looking. I think that is a cool and mind-bending concept. Those galaxies that were discovered by the Hubble Space Telescope were thought to be among the oldest in the universe, 12 to 13 billion years old. And we were able to see those because the Hubble telescope can look so far into space. But the measurement of time in space, even vast amounts of time, vast amounts of space, is still a finite proposition. I knew that even as much as I loved working on the Hubble project, I was never going to be able to find spirit through the lens of the telescope. But looking at the universe through the lens of Christian science reveals infinity and eternity. For me, that's the coolest concept of all. The discovery of Christian science introduced a whole new way of looking at the universe which makes other theories obsolete. That's why my study of meteorology didn't help me overcome the fear of severe weather. But my study of Christian science did. It gave me a whole new way of looking at what I was measuring as cause and effect. Now, I have been asked if one has to have a background in science in order to study Christian science. The answer is emphatically no. Christian science is for everybody, regardless of your profession or your background. Mary Baker-Eggen wrote that the understanding of Christian science is not intellectual. It's not the result of scholarly attainments. It's the reality of all things brought to light. She also wrote, regardless of what another may say or think on this subject, I speak from experience. And she did speak from experience, decades of experience of applying universal divine laws for the benefit of humanity. And I also speak from decades of experience that support what Christian science teaches. Let me leave you with this thought. One of the things I have always loved about Christian science for the time I was a little girl is that Mary Baker-Eggen respected me enough and respects all of us enough not to expect to take what she says at base value. She told her students that they should follow her only so far as she followed Christ. She said everything she taught was based on her understanding of the Bible. She wrote, You can prove for yourself, dear reader, the science of healing. And so ascertain if the author has given you the correct interpretation of Scripture. Proving it for yourself is what any good scientist would do. I'm so glad that all of you came today. I would encourage all of you whether you're here today in person or you're listening over the television or over the internet to go to the website Christianscience.com which is the official website of Christian science. And right there on the homepage of Christianscience.com there's a link which will take you to a bunch of resources that will help all of us pray about the current epidemic of the coronavirus and how we can help humanity rise above the fear of contagion and how we can rise above fear ourselves. Also, for those of you who are here in person when you walk out the door you'll find that there's a table with copies of the Bible and Science and Health as well as some other literature that you're welcome to look at and take with you. Also the Bible and Science and Health are available on Christianscience.com So I hope all of you will check out those resources and many more about what Christian science is about. I thank you all for coming today and have a wonderful day and a wonderful weekend.