So Hadoop compatible file system architecture and so on and some of the recent enhancements that we have made in S3A file system connector and the second half I would like to talk more about the Hive access patterns and the performance benchmarks that we have carried out with Hive test bench which is nothing but a subset of TPCDS queries for doing the benchmark on cloud. So first question is like why do we even need Hadoop in the cloud right? So if you look at it Hadoop does not have any or like cloud does not have any upfront hardware cost associated with that. So what it means is that within a matter of minutes you would be able to bring up a lot of nodes and then set up your Hadoop cluster with no delay or so. So and also especially you do not need to have administrator who is going to look after the Hadoop cluster 24 by 7. So there is no monitoring cost involved as well. Cloud supports elasticity. So what I mean by that is you will be able to add or delete nodes in a lot more easier fashion in the case of cloud and cloud also brings up a lot of interesting deployment models. Just to give an example let's say you have a use case wherein you would want to use the cluster or bring up and use the cluster only for couple of days or for couple of hours. You would be able to do that easily in the case of cloud environment and bring down the cluster pretty quickly. So you are going to pay only for the amount of time that you are going to use the cluster for.  Or based on use case you will be able to run a very long running cluster as well. And that brings an interesting point the reason is business units now will be able to go in their own speed as well as budget. So this slide talks more about the evolution of various different integration patterns with cloud storage on Hadoop. So there are couple of diagrams over here. The first one is an integration pattern where the entire data set is available in HTFS and the applications read the data from HTFS directly, process them and then store it back onto HTFS directly. At some point in time the management or the administrator might choose to say that hey I have too much of data I don't need the cold storage to be available over here. So cloud storage is going to provide me a lot more cheaper options so in which case they might choose to transfer some of the data from the HTFS onto the cloud storage. So they can do that or they can transfer the data from HTFS onto cloud storage in a lot more easier fashion with tools like Hadoop DCP. So you can have with DCP I guess most of us know that where you can have the source you are all pointing to HTFS and the target you are all pointing to cloud storage in terms of transferring the data and if you have to restore the data back you can use the similar tools in terms of transferring the data. The second integration pattern is moving more towards or a step closer to the cloud storage itself wherein the application directly reads the data from the cloud storage itself processes them but the storage is going to go through two hop approach wherein the first hop is going to be writing onto HTFS and later point in time using a DCP tool it will be transferred from HTFS back to the cloud storage. The reason for doing this is that there are certain cloud vendors it's quite possible that the cloud vendors do not have the similar kind of semantics that HTFS expects. Just to give an example Amazon provides eventual consistency right but HTFS expects a lot more strong consistency so you would want to eliminate those factors if that is the case you will have to go through writing the data onto HTFS and then move towards or copy the data onto the cloud storage. The last diagram talks more about the end goal that we would like to go or head towards wherein the data is directly read from the cloud storage by the applications process them and directly write the data back onto the cloud storage itself right but still we would like to retain HTFS as a storage medium for storing the intermediate data. So just to give an example let's say you have a very large pipeline of ETL job or so which spans across some 10 intermediate stages or so you don't want to store the intermediate data again and again onto the cloud storage environment because the performance characteristics in terms of transferring the data might be a little different. So in those situations you might want to store the data onto HTFS itself and store the final data onto the cloud storage. So what are the kind of problems that we have in terms of moving towards the goal that I mentioned earlier slide right so if you look at it cloud object stores are mainly designed for scale, cost, geographic distribution, availability and so on. However there are certain challenges associated with that for instance how do we even make sure that the Hadoop apps that are being developed are going to seamlessly work in both HTFS as well as the different cloud object stores. So just to give an example of problems take an example of consistency. HTFS offers a lot more strong consistency model however in the case of certain cloud vendors like Amazon it provides eventual consistency and if you look at the older model HTFS and MapReduce were sitting on the same node and the compute was pretty much pushed onto the data where it was located. But in the case of cloud it's totally different right where the storage is completely separated out and the compute is available in EC2 or like different cloud vendors. So the performance characteristics might vary a lot and cloud storage are basically not designed for file system like APIs or like usages and there are certain limitations with the APIs themselves. Just to give an example in the case of HTFS a rename is an atomic operation but in cloud vendors it may not be. Again taking an example of Amazon the rename is a copy and delete operation so it may not be atomic over there. So there are certain semantic differences between HTFS and what the cloud vendors might end up offering. So what is the goal right I mean if you look at it the primary goal is to be able to integrate with the unique functionalities that are provided by different cloud vendors onto Hadoop itself and to be able to optimize each of these storage connectors that we have with different cloud vendors and also not only at the connector level but also you'd like to optimize at a higher levels like you could have optimizations in high, big or spark or like MapReduce or in cascading and all those places right. So we'd like to optimize at a higher level as well so that they'll be able to make better use of the connectors. We'll talk about the performance optimizations at later sections. So this slide talks more about the Hadoop compatible file system architecture. So if you look at the different applications right I mean with MapReduce or like Hive or Spark and so on they do not directly talk to the file system level implementations. So they go by an abstraction layer which are a set of interfaces as well as abstract classes like file system, file context, abstract file system and so on and beneath that you have the strong or the implementation details like HTFS and so on or the concrete classes basically. I'll cover the HedgeBase part a little later but if you look at the underneath layer we had HTFS but now we have a set of different providers like Wasbi which is from Microsoft which is a blob object store from Microsoft, ADL which is Azure Data Lake again from Microsoft for big data analytical workload. You have S3 from Amazon and GCS from Google Cloud Storage. These are some of the cloud storage connectors that are already available in the market. So as mentioned earlier Wasbi is from Microsoft and it's a blob storage connector or blob storage platform where it provides a really strong consistent model and it also offers very good performance that you can even run HedgeBase on top of it and strongly consistent as well. The reason why I mentioned I'll cover HedgeBase earlier, I mean later point in time was that HedgeBase has got some strict semantics that needs to be adhered with the HTFS. So Wasbi offers that. So it's really strongly consistent as well as offers good performance. Similarly ADL is from Microsoft, Azure Data Lake offers strong consistency as well as it's tuned for big data analytical workload. S3A is from Amazon, offers eventual consistency model. Hortonworks has an external zero open for making it an optional consistent model and a lot of performance improvements are in place and it's very actively developed in the Apache world. EMRFS again is from, it's a proprietary connector from Amazon for their EMR cluster and it offers an optional consistency model or strong consistency model at an additional cost. Google offers GCS, provides multiple configurable consistency policies, offers good performance as well as they are planning to open so it's pretty soon in Apache. I'd like to take a case study with S3A and talk about more about its functionality as well as the performance characteristics. So as mentioned earlier, one of the main objective was to be able to integrate with the unique functionalities that are provided by different cloud windows and then bring it onto cloud itself. A classic example in S3A is that it offers multiple authentication models, almost like five or six of them. So it offers basic authentication model wherein you provide the access key as well as the secret key onto Hadoop configuration file itself. And at runtime, Hadoop will be able to pick up those details and then connect to the S3 buckets. Disadvantage of this particular approach is that the moment the configuration files are exposed to anyone, they'll be able to directly read the keys and there is a possibility of accessing the buckets later point in time. Or you could have the EC2 metadata itself where Amazon or AWS directly writes the credential information onto the EC2 instances itself and using APIs, you'll be able to directly read those credentials and then process your data in the S3. It's a lot more secure option. The reason is you're not exposing any of the credentials anywhere in the configuration files or so. It also supports an option of environment variables where you can set the environment variables and then to provide the credential details, a lot more or less secure option, but might be useful for some applications. And it also offers session credentials where you could temporarily create a credential from the token service of Amazon and which will be valid only for some amount of time. So that's a lot more secure option if you have to reduce the amount of credential leaks that are going to happen. Or else if you have public data available, you could even have anonymous login. So different type of authentication models are there in S3 which is supported now. Also it offers encryption that is in the server side. You could transparently encrypt the data which is going to be stored on the S3 with AES-256 cipher. So these options are also supported now. In terms of performance improvements, if you look at CEC is one of the most expensive column, the earlier implementation of S3 file system. So whenever you have a CEC, what used to happen is that it breaks the existing connection with S3A, reestablishes the Https connection, and reopens the file and lands upon a specific place or location. So that's a lot more expensive call because as you know, reestablishment of the Https connection is a lot more expensive. Just to give it in perspective, it's as expensive as reading some 300 to 400 KBF data from S3. So the problem gets more pronounced the moment you have something like a positional read, where the read doesn't change the offset within the file. So what I mean by that is positional read is internally implemented as CEC followed by a read followed by CEC. So you CEC to a specific location, read a set of bytes, and then CEC back to the original location. So there are two CECs involved, and the earlier implementation is going to about two times, the connections two times. And any input formats which are going to make use of this particular API is going to suffer a lot in S3 as compared to the earlier implementations of Https and so on. So in the recent implementations, we have fixed this problem where we made CEC as a no-op call, and we reopened the file only on need basis. That reduced the number of connection about problems by almost 50%. And the second problem that we had was with respect to the backward C. So some file input formats like ORC or beta parquet, initially they read the data which is located at the end of the file. So basically they read the footer information first, and then head back to the beginning of the file in terms of reading the realistic block details. That becomes a lot more expensive because the moment you do a backward C, the connection was getting terminated. So in the recent versions, we have fixed this problem where we introduced an effort-wise variable which can be set to random mode or in sequential mode. The moment you set it to random mode, we are going to exactly request for the amount of bytes that we are going to read from S3, thereby reducing the number of connection about. Let's look at an application like HI, which is built on top of this layer, and try to understand how it impacts the performance. So if you realistically look at the, at a very, very high level, the kind of access patterns that are going to be in HI are the ETL or the admin-related activities where you bring in the data from the external sources, create tables, analyze the tables, or do some transformations so that it can be exposed to end users, or it could be computing the column stats, or if you have lots of partitions, you might end up running some tools like, or commands like MSCK and so on. Basically the admin-related activities. Once the data is exposed to the end user, they're going to run a set of queries in terms of mining the data, and they might end up writing the data onto some specific location. Even the ETL data can write the data onto some location, basically the read and write access patterns. So if you already have the data in S3, you would end up creating an external table in HI in terms of exposing the data to the end users. And if the data set is already partitioned, the partition-related information will not be directly populated onto the metastore automatically. So you'd have to run a tool like MSCK, which is again provided in HI itself, in terms of populating the, or scanning the directory within the table, and it's going to populate the metastore information one at a time. So the reason why this can be expensive is that if you have, let's say, some thousand partitions, it's going to scan each one of the file in a sequential fashion and then update the metastore. That's going to be a lot more expensive call. In the recent versions, we have fixed this problem where we reduced the number of calls to the metastore as well as paralyzed this particular activity, and got almost like three x improvement in terms of the response time. For a Hive query to execute in a lot more efficient way, it's extremely important to have the accurate table as well as the column-level statistics. So you can pretty much gather the table statistics in an automated way by turning on Hive auto stats gather equal to true. But in order to compute the column statistics, you'll have to run an explicit command like Analyze Table, compute column statistics whenever you update the data set. And that turned out to be a lot more expensive call because of the number of calls to the metastore. In the recent versions, we have fixed this problem and saw almost like three x improvement in terms of the response time. The reason why these things are important are that, as I mentioned earlier, if you have to spin up a formal cluster, you would want to bring in the data from the external sources as quickly as possible and be able to expose the data onto the end users and let them complete the query as fast as possible. Then only you'll be able to make the best utilization of the cluster. So that's one of the reasons why we concentrated on all these areas. So in terms of other performance considerations, if you look at the way that the high queries work, the moment you submit the query, the first and foremost thing that's going to happen is the split generation. And if you have lots of data to be processed, the split generation itself might take a lot of time. So certain file formats like ORC and Parquet, they support a set of thread pools, which can be made use of in terms of computing the split generation. So at a higher level, it provides a set of files to be scanned, and these threads can be made use of in terms of communicating with the S3 and then get the information. So you might want to tune or set the appropriate number of ORC, I mean, thread pool based on your use cases. And ORC also has different split strategies. For instance, it supports ETL strategy, it supports VA strategy, it supports hybrid mode and so on. The moment you choose the ETL strategy, it's going to read the footer information first for doing some of the split pruning activity. And that can turn out to be a lot more expensive. The reason is it's going to read the footer information from S3 at the time of split generation itself. The good news is that it'll be able to cache all those details onto the local JVM itself. So when you run the similar query or the same query, again, it's going to be a lot more faster because it's not going to read the data from S3, and it'll be able to look it up from the local cache itself. This will be a lot more useful in scenarios where, let's say, you have one year worth of data to be analyzed and gradually narrow it down to one month. So the first one year worth of data is going to be a little expensive because it has to read the footer information from S3. But the moment it is available in cache, the rest of the queries are going to be basically fast. And once the split generation is computed, the second thing that happens is setting the number of tasks and letting the tasks proceed. So the split information is given onto the task side, and the task reads the files in terms of computing the query or performing the query or so. And at the time of what happens in the case of a task side is that the moment it receives the split load, it reads the footer information of the ORC again because it needs to read the details so that it'll be able to locate the data blocks. So the data is read again in the task side for understanding the footer information. Ideally, just now we read the footer details in the app master side. So what could have been done is that you could have passed on that particular footer details along with the split payload onto the task side itself. So that optimization has been made. And it was available earlier itself, but it was occupying a lot more memory in the AM side. So those problems have been fixed now. So that helps in terms of reducing the number of S3 reads in the task side. And Hive uses Thase as its default execution engine. And the moment split information is computed in Hive side, it gives it on to Thase for doing further optimization. And Thase does a lot of activities based on the number of min max split groups that you have got, the location details, and so on. The interesting observation is that S3 always provides local host assets location information. So if you have a very small table, Thase is going to aggressively optimize and provide only one task. So that's going to be a problem because let's take an example of an item table in TPCDS data set which has got some 52 files. So if it is less than a min threshold, what's going to happen is Thase will aggressively optimize it and provide only one task. And that task is going to open up all the 52 files in a sequential fashion and then decode the data within that. So that's going to be a lot more expensive operation. So in the recent versions, this problem has been fixed where Thase doesn't aggressively optimize if it doesn't know about the location details in advance or enough details about the location details. And we also observed that in AWS, the container launches were slow when we used capacity scheduler. So that problem has been addressed the moment we realize that AWS doesn't support any of the rack locality and so on. So we can turn off the node locality delay to zero, and that helps in terms of speeding up the container launch rates. With all these things in place, we ran high test bench, which is a subset of our TPCDS queries. And we used 200 GB scale in TPCDS, and the data was stored in S3. We used M44X large nodes, which are like general purpose nodes. We had some five nodes in place. And we compared with the previous version of HTTP with the latest one on the cloud. Interesting observation is that lots of queries like query number 15, 17, and so on, they didn't even run in the early versions of HTTP. The reason was the Amazon was, sorry, AWS Connect, timeouts were happening. But with the recent versions, this has been fixed. All the queries run. And also we get an average speedup of 2.5x. If you want to speed up the queries a lot more faster, then you can make use of a high one LLAP, which is in tech preview mode. So it provides a lot more performance where it reduces the amount of data that needs to be read from S3. And we have seen almost like forex improvement in terms of the performance. In terms of best practices, if you have large volume of data, you might want to tune the multipart setting, which helps in terms of checking up the data on the local environment and uploads the data in parallel fashion. And if you're using capacity scheduler, you might want to turn off the node locality day. And if you're using high, you might want to turn off the storage-based handlers and so on, or storage-based authorization provider. And if you have really large data set, you might want to tune the number of ORC threads that are available. So with that, I'm pretty much done with the talk. So open for Q&A. Actually, we've run out of time. I'm afraid for Q&A. So yes, maybe you can take it offline. Sure. Thank you.


I think anything is earned, respected, and it's an effort, you know, especially the positive energy, because not everybody has it every day. You know, you don't wake up going like, yeah, today's going to be a great day. You know, you've got to get deep and be like, okay, what can I do to make this a great day? You know, and it's getting in that mindset. Like I said, some days are good, some days are bad. You don't know what the other person next to you is going through that day, whether it's death, fighting at home, kids sick. You don't know what that person has dealt with. And when they snap, you know, bring them back. You're like, I know you're having a rough day, but how can we bring this together? Because the mental side of life is where all the stress comes in. You know, the more stress you have in the life, the more you start to pleat, the more fatigue you get, the more angry, the more tired you are. So bring that positive energy and that positive light. And whatever you need to do, it's like sometimes you have to take it till we make it. You know, and you almost have to teach yourself in a way how to be positive and how to think of others before yourself.


Hi everyone, Joe for jazby's casebreaks.com coming at you with 2019 Bowman Draft Baseball Sapphire Edition, one box random player break number seven. Also giving away a spot in that transcendent tennis break as well. And all the spots in this break, the original 20 spots, will be part of that break credit promo too. And at the end of the break we'll re-randomize, or we'll randomize the original 20 names, name on top, gets the transcendent tennis spot. Now the break itself, one spot gets you 10, a lot of great prospects here, pretty great product here. So let's times this by 10, there's 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10. Let's roll it, randomize it, 10 times, 10 the hard way, 5 and a 5, 1, 2, 3, 4, 4, 5, 6, 7, 8, 9, and 10th and final time. After 10 we've got Drew all the way down to Brian, a couple of Brian's, 5 and a 5, 10 times for the players, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 10, 10, 10, 10, 10, 10, Good luck, penton final time. We've got Lazaro Armenteros all the way down to Wander Franco, nice. Alright so I'm not going to read off all these names, but I'll show you the list as is, just so we can get it on the recording. Put it on wax as they say, then I'll alphabetize the list by your first name, so you can see your group of, see your group of players. All right, so Brian a lot it's a lot of players there Chad John Samuelson Dan Tyson Derek E drew Evan Jeremy Jesse Joe John Thomas There you have it any Any trades anyone want to trade players? Just one box right there. All right looks like no trades TWC trade window closed. All right, so as you know by now just two parallels inside here, but all card chip And it's worth when you if you're lucky enough to get a hit out of here or get a card out of here be sure to Be sure to look look up the player on the secondary market Because you'd be surprised at how well some of these might go even just these base cards And we'll sleeve and top load all these two and we've got an gold Estevan Floreal nice nine out of fifteen on that one It's a nice low number Might be worth looking that up seeing seeing what that's gonna go for Estevan. Where are you going? Goes to drew there you go drew and then we've got raised prospect Moises Gomez And that's 99 and that goes to Thomas Thomas are with the Moises Gomez Blue there's Alec bomb there too. That was pretty cool Tristan Casas Some nice stuff here and there you go All right now let's see who let's flip back to the other screen here now. Let's see Who's gonna win that spot? There's the original 20 right there? New dice roll. Let's roll it randomize it six times one and a five name on top gets it After six and six and final time after six congrats Joe and Driesen You've got that tops transcendent tennis spot. I think we'll have like One or two more breaks to do and then we can open up that tops transcendent tennis I'm pretty excited about that congrats again Joe everyone else including Joe You still have a shot at that break credit too. That's coming up a little bit later tonight as well Different video Joe for Jasper's case breaks calm. I'll see you next time. Bye. Bye


Allah Akbar. Some will translate that as, God is great. Perhaps a better translation of that Arabic phrase is, Our God is greater. This is a declaration that the demon and demons working through these people consider themselves to be greater than Jesus Christ, the God of Abraham, Isaac and Jacob. This is so extremely crazy. But let's rewind here. Allah who Akbar. This simply means, God is greater. God is the greatest. That's what it means. Allah is not our God. No, it simply is the Arabic term for God.


Hello, my name is Shannon Kemp and I'm the Executive Editor of Data Diversity. We would like to thank you for joining today's Data Diversity webinar, Big Challenges in Data Modeling, State of the Union of Data Modeling, and this series is moderated by the esteemed Karen Lopez. Just a couple of points to get us started, due to the large number of people that attend these sessions. You will be muted during the webinar. For questions, we will be collecting them via the Q&A section, or if you like to tweet, we encourage you to share highlights or questions via Twitter using hashtag BCD Modeling, 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 additional information requested throughout the webinar. Joining us today are two great panelists, Sue Goinsen and Anne Raismith, C. Sue, I am housing it there. Anne Raismith, Ph.D., is an information management professional and consultant with broad experience across industries. She is a certified data management professional, CDMP, and is a frequent speaker and author on data management topics, especially data governance and enterprise data management program development. She is the primary author of several sections of the Data Management Body of Knowledge, and she writes regular column in the Data Administration newsletter, TDAN.com. She has a love affair with data for almost 18 years, now starting with being handed a stuffy disk with a list of builders and told, this is yours now. The love affair has led down multiple data paths, and she has worked for and with many of the largest organizations in South Africa. Sue is currently president of DAMA Southern Africa and the most recently elected president of DAMA International. Things that she believes have been amazing achievements of which she is most proud include becoming the first CDMP in Africa, starting up one of the first ever data governance programs in South Africa and being asked to appear on Karen's panel. And with that, I will turn it over to Karen to get us started. Hello and welcome. Hey, Shannon, how's your day going today?  Excellent. Thank you. Thanks, Shannon, and thanks to Dataversity for sponsoring our monthly webinars on big challenges in data modeling. We've been putting together this year's sort of editorial calendar coming up. We have a whole bunch of exciting topics, but I'm always looking for input on people you'd like to hear chat about on one of our panels or events and topics you'd like to hear about. So feel free to put those in the chat or the Q&A so that we can take those forward. I also wanted to thank my lovely panelists, Sue and Ann Marie, and as well as all of you out there who have logged into this or may be listening to it after the fact, I consider you our third panelist, our other panelist. So unlike other webinars and broadcasts, we actually encourage you guys to chat with each other in the chat feature over on that sidebar on the right. So we try to keep an eye on that while we're multitasking and talking about these things. But if you have a formal question for the group or for Shannon, a logistic thing, please put those in the Q&A section because it's easier for us to find those and answer them for them. A final note is there are three slides. This isn't a lecture or a tutorial on something. The slides we have are what you're seeing right there. So you can sit back and listen, grab your favorite beverage, whatever that is, and do some chatting and interact with us. Oh, I forgot to ask Shannon. Shannon, do we have polls today? We have some notes, certainly. Okay. If you can build those. I did send you an email, I think. So if you can build those. And if we don't get to them, that's fine, too. Let's talk about what the topic is today. So today we're going to talk about Stay the Union. Well, why Stay the Union? Well, because in the U.S., this is Stay the Union's speech time, where Shannon talks to people about where he might see the United States and where we're going for. And so I thought this would be a great time to have a Stay the Union for data modeling. So a lot of, like my panelists who have a lot of experience and like a lot of you out there, a lot of us have been doing this for a long time. We've seen things that have changed over time, things that have stayed the same. But I think maybe I'd start with just a little, I'd give a brief overview of where we've come from. And one of the things we're at as a profession is putting together sort of a published history of everything that's happened. I think maybe that would be a great task for people to have. Maybe that's something we can do at the upcoming Enterprise Data World is get some people together during one of the evenings and try to collaborate and crowdsource a brief history of data modeling and data management. I think that would be great. But I'll start with maybe where my career was is I started data modeling back in the 80s when I was in school. And we had the ERDs and everything, but what we did were data flow diagrams. We created data dictionaries. We created file layouts specifically for FAT files or VSAM files or ISAM files. And then on that same time through the advent of relational databases came along. And I was fortunate to be studying database technologies at a time when Ted Codd had published his papers and vendors were beginning to build out relational features either as a link on top of their pre-relational DBMSs or brand new ones. And there was a lot of contention on there about how relational is a relational database. So that was an interesting time. But the data modeling I did then was part of using those green templates, mostly in green bar paper. I really dated myself here. But Sue and Anne-Ree, what's your name from in data modeling? When did you get into those things? Karen. Mm-hmm. I had a similar experience with you. I had a modeling career in grad school when I was introduced to modeling data flow diagramming format for a grad school course. And right after graduation, I went to an insurance company where one of the people in the organization for whom I was fortunate enough to be told the word was insist on that I learn relational data modeling. So I was a profession and she was a data world as an attendee. And she was one of the perfect people. So we're seeing capabilities for the history of data modeling because she's been doing it since the beginning. Yeah, so we should make this happen. We should so make this happen, I think. I think it would be a wonderful idea. I'm sitting here actually writing talk to Sue about Karen's idea where I did it. And from there, we built one of those cruciatingly detailed to use John Zachman's phrase and her program models. It's a segment and paper and the original case tools to build what was normally normalized on my God, normal form, conceptual data model and all the metadata associated with it. That's how long ago it was, back in the 1980s. And what I wanted to do right in a lot of things I would never do again. Yeah, I'm missing myself because the planes are still coming over. So I'm the real baby here because it was the 6th of February 1996, probably at about 9 a.m. in the morning, that my new boss handed me the Stiffy disk and said, yeah, you need to look off to these people. This is your problem. So I subsequently put the Stiffy disk in the PC that was on the desk and I looked at it and I went back to him and I said, this needs a database. He says, well, go buy one. Here's my credit card. So that is how I felt about it. And I really did. I went out there and the only database I knew of was Access. So I brought it back to the office, copied all of these things out of the Lotus 123 and pasted it into Access. That's how much I knew what I was doing. There were about two weeks, I guess, of really typing in pieces of information every time I got a new builder and it suddenly occurred to me that I was being a bit silly and then I started looking at the data and going, but that looks like that and that looks like that. And I think by the end of the year, I had six developers working for me and we'd moved from Access into SQL Server 6.5. So I started data modeling. It was no training, nothing. I just didn't have a choice because I couldn't live with typing in about 100 columns across an Access table. You know, that is the most common way. When I do my presentations at EDDO, I usually ask people, how did you get started and all this and those things. And I mean, maybe 90% of people in the room got EDDO. I fell into data modeling, which I always think is hilarious. Or they were pushed into it and, you know, they were finding a better way. Today, I'm not sure that's how that happens and that's what we're going to talk about in just a little bit. This is something that I started with green templates and green bar paper. And we did have a model tool accelerator, which I think was primarily data flow diagramming tool. And this is all seen as new, new, new, right? Because at that time, we would have had DOS based machines. Gosh, I feel like I'm granting people how I walked uphill both ways in the snow to school. But this year, just as I was getting data, you know, the modeling tools, the case tools were coming out. There was IEWF and IEW, which became ADW. And there were things like Bachman data modeling tools and everyone, and S Designer, which became, I think, Power Designer. And then there was Silver Run and Oracle tools. What's left out here that people worked with? Back then. I'm not sure. It's a prize architect. And prize architect. And system architect. And yes, of course, ER Studio now. Yes. I mean, so the newer tools that came out, like the Oracle Designer, ER Studio, Erwin came out with just a little nobody tool, and then ER Studio came out. Also another one. Erwin was built in New Jersey, not far from where I live. Yes, I remember that. And also at the same time, when these tools came out, then the big question became, what notation should they use? So there was notation. And then the United States government, mostly the Air Force, got together and came up with IDF 1x and all the IDF 0s and all the other notations. And about the same time, we had what is sometimes called Martin notation, sometimes called Informational Engineering notations. But basically, there's depth in the crow's feet and all of those things. Also interesting, at the same time, when the national things were coming out, so the organizations that eventually became DAMA, came out. I think DAMA recently celebrated a birthday. Is it 40 years, you guys? I can't remember. Yes, 40 years. 40 years as an organization. Yes, as an organization, I think. And then there's our own chapter. Our own chapter, ERMAC, is even older than that. So I think we're 45-plus years. So going on all those things. And then we kind of passed forward in just a few years ago. DAMA came out with the data management, the guide to the data management body of knowledge and also worked with ICCP to come up with the certification. And I think that's kind of covered sort of the tools, the approaches, the notations, all those things. Which thing major that I left out? I think. The frustration factor. So that's also a whole other webinar that I love. It's my favorite topic. One of those things about sort of the conflicting points of view and how we reward people differently to be architects and designers versus builders. A whole other topic that's still one of my favorite ones, though. But yes, the frustration of figuring out where formal data modeling methods fit within other tools. So I'm going to go ahead and segue to where we are. Okay. So I can't remember in Method 1. I remember, oh, the methodologies. I so should have mentioned that because I worked for big methodology companies as well. So that was the other thing that came along with the case tools was the methodology wars. So to the point where what are the methodologies I was asked to implement for a client had something like 400 deliverables for each producer. It was internal deliverable. It was internal deliverable. And so there was that whole, and a lot of them were tagged as information engineering methodologies. Talking about that, because most of us have tried to ban the word methodology because it got so much hype and there weren't that many people that worked by following a methodology without following their brains. So all of that stuff. So here we are now. So out of all those tools that I mentioned, a lot of them still exist somewhere in some form. I know that Silver Run was put into open source. At one point I know that Power Designer is still going strong. Irwin ER Studio. I know technically I think I have an ADW still exist somewhere in the CA world of tools. Oracle has a SQL developer tool that Oracle Designer is no longer supported. There's lots of the current tools. Vizio, I forgot to mention Vizio, is probably the second most popular data modeling tool right after the number one data modeling tool which tends to be either Excel or PowerPoint. So that's something to hear. Yeah, and I have a blog post about that. I'll try to look that up and share that in the comments. It'll definitely go out into the post notes about what makes for a good data modeling tool. And the other thing I missed in all this is sort of the advent of repository model marks, collaboration modeling. To me that was a big change in the modeling world where we came from. So where we're at though is that what I found from the field is that one of the biggest frustrations, one of the biggest challenges that people have is trying to make this formal data modeling methods work within the constantly changing workflow and methods that are happening in the software engineering world. So of course the next player there is Agile Scrum, other newer things, plus other very specific things like Code First and all of those. And I've talked about those on other webinars too. So where are we going to see this? Sue, where do you think we are now with the way of thinking about how we develop models fit within the new and the changing role of software engineering methods? I don't feel like I've done a whole 360. You know, actually I really did it. It was extremely agile. I kind of stuck stuff together, made sense of it, spoke to the developers and got them to understand what I was doing and we went ahead. I don't think I even understood the methodology of build your conceptual one, then go to your logical, then do this and then do that.  And I do feel that sometimes we've gone all the way back to the beginning. I have to say I'm not quite sure that I agree with it because I now sit at the end of having done a bit of agile data modeling with one of the developers and today I spent the whole day trying to make sense of millions and millions of rows of data that I feel that I made a mistake on because I designed the data model. But I did it agilely because I had to do it quickly and I didn't take into account things like how many records there would be, the fact that the developers would go behind my back and make changes that I didn't know about. You've met those guys, have you? What's different about all the other architects on our projects? Do they suffer from the same thing? I have heard so. You know what? I mean, I see that there's a gentleman and he's from the South Africa as well and I actually met him and spent some time chatting to him and I have had him say very similar things to me as well that we seem to all suffer from the same problem but we don't quite know how to resolve it. One of the things is like the architects I work with are either enterprise architects, which means they're responsible for a whole bunch of models sometimes done in enterprise architecture tools and they usually have more cloud or authority and sometimes data architects report up through them because data architecture is a subset of the enterprise architecture. They suffer from this whole sort of impedance match of what the architect and what development technicians want to put into place. I'm wondering, they're just maybe I'm doing the data architecture as well that I feel like there's much more resistance from those that are quite fair to be heard in them, compensated and measured to do things that break our architecture. We have them on performance and getting things done fast and we don't measure them on data quality, data integrity, or flexibility for far future versions, usages of the data. That's breaking so much from this. Is it us, should we be looking at ourselves or is it a combination of things, Anne-Marie? I think it's a combination of those things, Karen. I think that one of the things I've seen in the assessment you do for enterprise data management is that I include an organization of an enterprise data architecture group. In that kind of recommendations is that most organizations don't implement their enterprise architecture team to include data architecture properly. They do implement their technical effects, their infrastructure architects, etc. But to do that, they don't break a data architecture. They don't do the imposition of the use of data governance, the organization of master and reference data into an architectural approach. They tend to do things fast. And then there's an enterprise data strategy that looks at data as a corporate asset and provides this value to enterprise architecture. It's a management strategy to be effective. And for your enterprise architecture, I think I can't even tell it all. You're doing great. Tongue-twistedness. I think you get a challenge on both areas. Everything is different, but I need this continually to make it sound like I'm on a little soapbox because I say it all the time. Not only not, but I can tell you I've either been on panels or attended panels for the last five years, and we're still having the same discussion. Yes. I know it's going to be a little bit disruptive, but we've got our polls, so I want to go ahead and do the poll really quickly. So the first poll I'm going to give you is, who are you guys, you participants? Can people see the polls? They should be. Yes. I'm going to give about 10 more seconds. Get voting. We have more people than this. Okay. We're closing in 15 seconds. I can never tell. I forgot to look at an attendee this time. I'll give you the results. So basically, we have about 50% of you guys are architects, 8% business analysts, other analysts, 4% DBAs, devs, other techs, 3% of you are architects, 5% other, and 20% of you who have no idea what you are or are busy finding someone on Twitter. So there's one. How long have you been actively data modeling? Now, by actively data modeling, it means either consuming or working with, and I assume that if you're here, you're actively working with it, even if you're looking them. So we'll go ahead and open that poll. I'll give you some music here. Some sort of weight music. Exactly. I'm assuming that E is going to be the big one. On my bio slides, I always say I've been doing this for 20-plus years because I've mostly stopped counting. My bio slides is the same thing, 20-plus years. Yeah. Back to saying 10-plus, I think. It's like the more interest I get, the more I have to skew the data results. I'm still waiting. So when I get to 20 years, can I also do the same thing? Definitely. Yeah. I'm still waiting. Yeah. Very common. Very common, right? Yeah. So let's see. Yeah. You know, a dozen people or so, they've been doing data modeling for zero to two years, and 10 to about the same amount doing three to five years, and 13% doing six to 10. That makes it sound like we're doing prison terms, doesn't it? Yeah. 32% of you are doing 10 to 15 years, and almost 30% saying I stopped counting. Yeah. So this is both a plus and a minus, I think, sometimes on my project team. So because I'm so experienced, a lot of people on my team assume that I'm really stuck in the old ways, because most of us are, or that you're doing projects, I'm coming in to help fix a data model and a database design. And so a lot of people there, their experience with data modeling is very light, and very light as far as depth and breadth of their experience. So now we can go back to some of our questions. So where we are now. So the other thing I wanted to talk about in this State of the Union where I see things is while I've deemed 2013 as the year of data, and now I'm going to still call 2014 the year of data, because data is just so prevalent in the news with big data, with big websites not being happy, with data integration projects making world news, with data just being a big deal. I know I was impacted by the U.S. one last week, and it's been very annoying as I reveal my credit cards. But I think with the question now to my panelists, let's go back to Sue. So most of the data modeling tools these days work either with IE notation or IDF-1X notation, but they really haven't been updated in a long time. Are they still meeting our needs? So not the tools, but the notations we're using, like ERD, like IE, IDF-1X. What do you think? I'm not sure that they are. I think they have been, but I think there's this real change to agile and this real change of the way we think about data. And I really believe that we might need to revisit these notations and actually take a good look at them and say, okay, does this really work? Is it giving us what we want? Is it actually meeting our needs? And I would probably bet on it. I would actually put some money on it, but it's actually not the case. And that we should be really revisiting this kind of thing. Do you have any examples of things you'd like to see it notations and support that they don't support now? I know I'm not on the spot here. I know. I think for me it's about understanding the relationships between data and the context of data. So we have this little notion where we've got three little lines pointing that way and one line pointing the other way and all of these things. And traditional data modelers, and there are a lot of them around, they understand that, they get it immediately. But what I do see is a lot of, is that there aren't so many traditional data modelers as there are people who are, as you said earlier, falling into it and are doing it because there's nobody else around to do it and they've got a bit of a passion for getting the data right. And they have absolutely no idea what they're doing and they can look at a book and it then goes, hmm, so what's a crow's feet notation? And unfortunately, unless you've seen Alex's little dance about crow's feet, you always really know. So I'd like to see, if I say I'd like to see it in a more English language way, I think where it's more visual, more visual than the crow's feet or more visual than the other things. Again, you're right, you did put me on the spot, that's mean. No, no, that's good. Yeah. And I see a comment there from Ben talking about, this is an above 40 crowd. And I think that's also very true because what it means is we are all more traditional. The younger generation who are coming into data modeling are kind of challenging the tribe and trusted and some of their challenges are actually valid. Necessarily are lacking to use them what the model and the business and the developer want to represent that's lacking. Time and again, a less experienced modeler didn't understand what an experienced modeler was trying to convey, abilities, ending up going from Philips, Emix to Dallas, and then finally to Austin. Yeah, that's always going to be an issue. I've opened up a poll on this thing about audience feels about this. And I think that one of the ones we use right now is actually at least in the major tools is a combination of Philips 1X didn't have the crow's feet normally. It was an IE notation or a Martin notation. But the 1X was the one that had all the attributes like a table. It looked like a reverse engineered table and all that stuff. I think though that because a lot of modeling tool vendors you definitely want to meet standards that you can get government contracts when the governments have that as a standard or organizations do. And that standard as far as I know isn't actively maintained anymore. I think it's an active group of people modeling it. And I'm finding there are features that I want to see in the notation. So things like the most common one with IE F1X is it doesn't support ARCs, which means actually exclusive relationship like this entity is related to only one of these other entities at the same time and IE F1X has another way of doing that with subtyping, but I think it's not really the right way of conveying that information. And I know there are people who've been doing a lot of work to try to come up with new notations. I know even Harry Ellis who was responsible for architecting a lot of the original Barker notation and people have worked on extending things and there are some new notations that are sometime put around. But I'm wondering, you know, we can't really do a profession that this sort of way of conveying data about our data has not changed in 25 years, that there aren't new ways of thinking about how we want to explain data and the underlying metadata for them. So it works. So one of the questions in the forum is what about UML? So UML is a modeling approach to methodology and notation and unfortunately not a great deal of success conveying to business users. I have better success with UML notations with more technical people. I originally designed four technical people but David Hayes has done a lot of work of making UML notation work with how to make programs work better in the business side. But I've solved many of the problems that I have of wanting to have a notation that meets both audiences and maybe that's the issue. I'm not sure. Any other comments on this about that? What I'm seeing amongst the chats going on here is that it seems that most people use a bit of a combination of bits and pieces to get their points across. But what I don't see is one comment I thought was pretty good. I'm just looking back up at there from Tom. Okay. I think that's Bill C. Something like that. I'm talking about the model is a picture of data and we create them to talk better to our clients, both business and IT. The web has definitely advanced us to a more graphic level of expectations. We'll pass this now. We want to see more use and acceptance by the business if we made the model more graphical and photoshop-like. And I think that was probably something I was trying to get at, is that I really would like to see more actualization of the data rather than the old sort of like, here's a table, here's a line going here, here's a line going there. And you're right. It does miss some of the mutually exclusive relationships although this thing can be related to this and this but it can't be related to that. And even if you relate it to that and that, by the third one it shouldn't be related. Right. Another common request that I see and also that I've talked with Harry about is this, there's a concept of one domain but sometimes the mainness is just over time. Right. You know, I think quality and optionality and the relationships and the constraints that they end up generating are where our current notations are going to be able to say, you know, I don't want to be a customer in my business but at the time they can submit many but maybe only ever one active one. I mean right now in data modeling where do you put those more complex business models? You either, you know, have to just document them as in text. I'd like to be able to put that right into the model. So I want to be able to more engage in support and capture not as a text field, not as a drawing thing. I want to capture more stuff that are far more understood by the tool and I know they all, our tools have user different properties and attachments and different ways of extending the models. I get that. But I want to come up with a standard. I want the profession to come up with a standard in theory that all tools could support this and so that we could have a standard way of portraying that information. So my, I want, we've kind of stopped building and improving our notations and I see that as an issue. And I also want the sort of making it more visual and having more flexibility and realizations is important. And I know a lot of vendors doing a lot of work on that and I think that's a hard thing to do and I just want to keep seeing progress on that. 240, I wanted to move on to, I wanted to bring everybody at the attendees that you can ask formal questions in the Q&A. I've been trying to answer most of those and I am trying to keep track of them but you guys are doing a great job on chat. That's so much that I can barely keep track of it. But where do we want to be now? So my biggest thing over the last couple of years and it's come up on several of these webinars so I don't want to rehash all that stuff is where is this in 2014 of data and we've got all these non-relational data things to model.  What are we going to do in the modeling world to support that? Ann Marie? Now that's a great question for a panelist to answer with this comment, I wonder. Because we have several camps right now and we have the NoSQL camp that says do you want to build anything? Just do it. Definitions of what, quote, NoSQL means if you read different things by different members of the NoSQL Thought Leader community you'll get different versions of what that community means. So I'm not sure that data modeling could work now with the NoSQL Community perspective. You've already given several on how data modeling could help to put in some process some of the more careful ways agile behaves. I'd love to see data modeling done without spending 17 years contemplating the different parts of a data model in an agile world. I think we really find a lot of use for data modeling in an agile development. If you could work with the agile community to do that, not you, we. I think all the areas are, and I think it's a great way for Dama to get this plug for Dama to get in the forefront of being a Thought Leader in the data community by leading efforts to discuss these things. Do I have a choice? Of course you do. I absolutely do agree with you and I think this is a task that Dama should drive. We have chapters all over the world and we have some really great, some really eager, some really passionate people. And I think as Dama International we should really be driving getting people together in these kind of environments just to talk about it and to start identifying what are the things we should change, what are the things that are really good and still working, and what are the things that are just so hard that they're never going to work. I mean, I see Frida has a question about how are you depicting big data? She says, blogs with unformatted data in our tools. You know, and Dama really is. I mean, how are we depicting big data? And I think a lot of us are kind of just putting a little circle there or a little square there and going, okay, well, that's big data. Again, do we have a handle on it? Do we know the context of it? Do we know the context of our data? Because that for me is always very important in data modeling. So I really feel that this is a good topic for Dama International to take on as a... and I think Karen, you might be a good leader in that. You're always volunteering people. One of the things that makes me a president. I know. I'm very presidential. So one of the things about all of this is that I think one of the things in the data modeling community, and our big challenge, is that we're so overpitched and understaffed it keeps us from having the time to go learn... I mean, my to-do list, my to-learn list about all the other technologies that can help try to figure out where is data modeling going to fit on this? I know on one of the webinars I had last year on this whole modeling with NoSQL and big data, we have another one coming up this year as well, is, you know, everyone agreed, and it wasn't just data modelers on my panel, that there's some data modeling that needs to happen. It just needs to happen in a different way, maybe in a different time, and it may not be the same data modeling that we use now. It's just, who's going to solve that problem? It needs to be the data management profession participating in that, and the right type of people. The people who aren't, you know, are trying to try doing something differently, is to come in. There's a real problem. On the projects I'm on, there's a real problem that we have this big, big divide between traditional 1980s data modelers and people who are trying to get stuff done and solving new problems. And there's this whole sort of thing going on in our profession about big data is just a thread, and it's just something, it's nothing new. It's just the main frame. Well, if it was just the main frame, we'd be pointing data modeling tools at it and doing it that way. It is different. So, in another webinar, I think one of the biggest pain points and challenges with data modeling right now is all the things I need to model. I'm not just modeling databases. We're not just building new databases all the time. Most of my clients, it's all packages. And the modeling we're doing is this canonical model in the middle, or we're handling the packages that don't have a documented model, trying to sort of reverse engineer and reverse gas a logical data model and maybe a conceptual data model, then building something so that we can interface the data either through services or through direct integration or integration packages or through service buses and all those things. But the other things that are coming are support cloud databases because they're not quite the same as traditional relational databases, even the relational databases in the cloud like SQL Azure and all of those. They're slightly different. And some of my tools support that and some don't. Where do you guys think that the model space, the vendors, are they prepared for dealing with these new things? Well, I think maybe some of them. Ann Marie, what do you think? A couple of them? Most of them are not. I think most of them are still trying to fix the current ones. Never mind the new stuff where we're saying, listen, we need to model our data in a different way. We need to not be necessarily so inexorable and insist that it's done this way or we need to do some more graphical stuff so that when we show it to a user who has no idea what a database looks like, he or she doesn't run away screaming because there's that little square blocks with lines again. I don't think everyone's ready for it. I do, however, think that there's one or two organizations, vendors out there who are getting close towards this coming edge of let's try and do something different and satisfy the challenges that we have. Oh, okay. I think we're concerned that there are more than one or two vendors who are not far-sighted. I think most of them are stuck in the single releases behind priori might not be the right word but very protective that might be the better word, a program that would monitor the highway and know you can't share data from one or two months unless you let us do it for you and you have to buy everything our way. Yes, I think you're right. And I use, I don't have one specific tool that I use and say, okay, this is the tool of my choice. I will use that one or everything else. So I've tried quite a lot of them and sometimes I go, I wish they would all just be one, then maybe all the nice things I like in A and the nice things I like in B could all go into the C and then I'd get what I want a little bit more. So, you know, I think part of it is they're all trying to be, hey listen, use my tool because it's the best but they're probably not taking into account the people at the end who have to use it, which is us. And I think, I really wish that maybe the vendors would sit together and go, you know what, maybe we should just do one and we'll make it the best we can do rather than let's all have one and try and sell it but it's not really going to work for what you really and truly want. So that is mentioned on tools and price. So one of the issues that's a lot of my teams back who are looking in these new digital worlds where almost everything is free and open source, so Hadoop and all these other, they all have free versions of their TVBMSs is their position where they're going to be paying, you know, 10K a seat for the modeling tool. So one of the things that's kind of happened is our tools got better and more mature and they do all these amazing things and they have these great features that the real cost of ownership for them has gone up because they support so many things because they support enterprise modeling because they have a low blown repository with versioning control and everything like that. The team is used to using free software or open source software both for their development needs and for the implementation needs, you know, for them to understand why one might pay for enterprise design and development software. While there are some open source modeling tools out there, they definitely don't have the maturity that we've seen of 20-plus years of relief cycles for our modeling tools. Would you agree with that? I say that conditionally is because I think that we have the challenge of the why should I buy anything? Yeah. Drawing tools. Because drawing tools is relatively cheap or embedded in other tool suites. Yes. Another issue that I probably face here is that the organizations that I traditionally sort of tend to be supporting are generally the larger organizations and they don't do the let's buy open source or let's use open source. They're far more the let's throw lots of money at something because it can only work if we throw lots of money at it. Like that. But they never throw money my way. So what I see happening is that the big organizations will get some new VP of this or chief from... officer of that and they come along and they say now we're going to do this and we're going to make our data really great and to do that I need this tool and that tool and that software.  So that whole stuff gets shelved in the corner. The next guy comes along and does exactly the same but he doesn't want those tools because those tools were previous dudes stuff and I knew new stuff because mine is better. So in the organizations that I do seem to support there's seven different data modeling tools. There are six or seven different MDM tools. There are six or seven different data quality tools and you name it there's a tool for everything out there and the big organizations have them all. They don't talk to each other and then we fall down on the data models anyway. So it's a case of okay we can't read an Irwin model in that product. We can't read a Power Designer one in that product therefore we have to redo it and since we didn't really know what we were doing in the first place and the people who did design that are long gone. Ouch. Yeah. Yeah. There's again the same frustrations. So now to the last few minutes I have another poll for everybody and this one is so we've got to I think our March Webinar is going to be about data modeling dead or alive. It's kind of an update to our last few years is data modeling dead Webinar but I'm trying to use to gauge about sort of the life of data modeling out there is having sort of full-time equivalents of data models and I don't care whether you're an employee or a contractor or a consultant. In your organization are you seeing more full-time equivalents of data models whose primary responsibility is data modeling and data architecture. Are you seeing more of them or fewer of them in your shops? And I'd like to have a lot of people voting on this so that I can see how it's going. So even if you've pitched away and you are busy playing Candy Crush or something on Facebook if you could pop up and vote in this poll and look it's already starting to close get to your end and you've got four more and one thing is that wow that's not quite the graph not very many people voted but it looks kind of evenly spread that we have more 13% of you have more 10% have about the same 10% have fewer and an alarming 13% don't really have full-time data modelers at all. I think that I mean the reports from the field that there are fewer and fewer data modelers in organizations. What are you guys seeing? The modeling responsibilities given to other members in the organization and the accidental data modelers. Data modelers. Well the contract that I'm busy working on right now there is one data modeler in the whole organization and that's me. I see that quite a bit. I see that quite a bit. What's really scary is that I actually am not in to do any kind of data modeler. The specific thing that I'm supposed to be doing is actually there's no relation to please build me a data model or please tell me why this table doesn't look like it and what I should be looking at and I would say probably at the moment almost 30% to 40% of my time is doing answering those kind of questions or even if it comes to a piece of paper and quickly drawing a model and I'm the only person in the whole organization and I'm talking over 6,000 people. Yeah. And the Q&A is there's a shortage of them to find our company once them. I have a lot of experiences trying to help clients hire what I consider real professional data modelers or data architects. Not just people who have once reverse engineered a database or once took a class seven years ago but people who have several years of experience of doing that. I think that's a real problem for data. I think it's a real problem. I have people that love their data and I want there to be professional data managers, right? And the fact is is that when we go to events everyone looks very experienced. When we go to EDW I see very experienced people asking people how long they've been data modeling. It's queues into the double digits so we have a real problem here and we always invite people to stay over for about 15 more minutes with the recording turned off where we can talk about some of the things. Because we have more minutes I think I'll start the wrap up. I want to thank my lovely panelists Sue and Ann Marie and I always think when we get to the end of these we should just have more time for this. That's why we started doing the pre and after show. I want to thank Shannon for making all this stuff happening and we all get here and get logged in and the attendees get their support. I want to thank Dataversity for hosting these and I want to remind you that we these happen every month so the fourth Thursday is at the fourth. They happen every month you can go to dataversity.net. The other thing is I want to put a plug in for Enterprise Data World. You guys are both coming to Enterprise Data World, right? Yes, definitely. And it's going to be a lot of fun and it's the 18th year I go to it every year. I think I've only missed a couple of years due to constraints and it's great for learning from very broad leaders and you can go to dataversity.net to see it as well as it's a good sort of sort of way to get to the end of this. I want to thank you for having this sort of sort of way for data modeling by having these same discussions with other people and sharing useful tips. Speaking there as well I'm doing a workshop and I'm running a SIG so here are all the things we're going to do. So I'm going to turn this back over to you. Karen and Sue and Anne-Marie thank you so much for this fantastic discussion. I just love these webinars and I especially love all the attendees who just chat away and really get involved in the discussion that's just what makes this webinar so spectacular. I'm still just tongue-tied today. So anyway I hope everyone has a great day I will turn off the recording we'll get the post-show started and thank you everybody for your time.