All right. Hi, everyone. Welcome to my talk on unveil the magic with Houdini without Houdini transfer machine learning pipelines with Apache Houdini. I'm Nadine. I'm leading one house of developer initiatives and I've also previously have been at rock set and bows. And I'm passionate about bridging engineering product and marketing to help drive developer adoption. You can find me on LinkedIn at in slash Nadine Farah, or you can also find me on Twitter at and Farah 86. If you're enjoying today's talk, I would love to hear from you. You can tag and follow the Apache hoodie LinkedIn channel at company slash Apache dash hoodie, or you can follow us on Twitter at Apache hoodie. The QR code is a link to the hoodie community. So if you want to follow up a sink, you can find me there. I'll hang out outside the conference room to talk to help answer some of your questions. So for the agenda today, we'll go over the medallion architecture and then I'll talk, then that will lead us into a hoodie overview from there. I'll talk about the incremental processing framework and then we'll do a go ahead and do a case study. So let's go over the medallion architecture. When we think of machine learning pipelines, it kind of looks something like this at a high level. You have you ingest your data, then you have some sort of data management, you process the data, then you create your models and then you deploy it. So in this talk, I'm actually going to focus more or less on the lines of ingesting, managing and processing data. So more often than not, when companies are usually employing the medallion architecture at some level, they use it to help refine and process the data. So let's take a look at what the medallion architecture looks like. So with the show of hands, how many of you have heard about the medallion architecture? No hands? How many? Okay, cool. So this is a typical view, what you may have seen around some content around the medallion architecture, but let's take a, let's walk, let's like do a quick walkthrough. So when data is ingested, it will be unprocessed and stored in the data lake. And typically in the raw layer or bronze layer, you'll have data duplication, change logs, raw event data and more. And data typically here is unstructured. From there, the data will graduate to the silver layer where you'll perform data duplication, you'll validate the data, orchestrate and manage it. For example, do data cleaning, you might file size and more. And then finally, you'll write some joined queries where you'll kind of join all these different silver tables in order to create a fax table also known as like a gold table that can be used by downstream applications like AIML applications, analytics and more. But if you look at this architecture, it seems pretty easy to do, right? But what does it take to really implement something like this? So generally, people approach the medallion architecture with a sample diagram I have shown, and you can see it seems a little bit more complex when you're trying to implement such structure. So in the raw layer, you'll first ingest raw or unprocessed data into the data lake, and you'll create the raw or bronze layer, and I'll use the raw and bronze layer interchangeably throughout this presentation. And then from there, you'll do a full table scan to grab all the data, including new updates and rewrite the entire silver table with augmented data. In this process, you might use SQL or PySpark or something like this to deduplicate the data, to manage the data. You might do cleaning or clustering in that sense. And augmenting the data with the examples I gave is a very manual process. You have to manage and orchestrate all the processes in order to ensure there are no concurrency or write conflicts that can lead to data corruption, data loss, slow reads and more. From there, with that, you'll do a full table scan and join the silver tables. You join will happen and attempt table that you might create in Spark. And then from there, you'll output the results into a parquet file and then create your gold layer or a fact table. The query engine you use will do another full table scan to execute the query and return the results that can be used for analytics and applications. You can see that there's a general theme here of scanning the full table and doing full table rewrites. Also, this whole architecture in itself is very manual. You have to manually file size your data or to avoid the small file problem, or you'll have to clean all data and so much more. And the technologies available in the market kind of encourage this type of approach of building the medallion architecture because they lack a few key things. So let's see what this looks like. And the diagram, each of the zones have the same services repeated, but I'll go over what each of these services are and how it affects the medallion architecture. So let's talk about automated table services. So many technologies don't offer fully automated table services that can automatically help manage your data and maintain your table's health. For example, Spark, you might have to run manual compaction, which is merging smaller files to larger ones, so you can improve query performance, and also cleaning the data to ensure you run faster analytics and compliance. But if you run these two services together at some point, you would have to implement your own optimistic concurrency control mechanism in Spark. And here, if two services are trying to modify a record, one will have to fail or be blocked until the other service has actually completed its task. The incremental framework, the next thing I want to talk about is incremental framework and indexes. So the incremental architecture prides itself in incrementally updating data sets without reprocessing the entire data over and over again. And a key capability that aids to achieve incremental processing is to handle data mutations at record level. This helps avoid reprocessing the non-changing data. Also, adding an indexing mechanism can further help to quickly process these record mutations because indexing helps locate records in the data lake faster and more efficiently. But by not having this, you have to constantly do full table scans and table rewrites. And at terabytes, petabytes, and exabytes scale data, this inefficiency becomes really prominent. You'll have to throw a lot of compute to your application, and at some point it's just not a viable solution. So what's an intuitive architecture that might be more efficient? Let's take a high level look. In the sample architecture with Apache Hootie, you can ingest data into the raw zone. And from there, you can actually just do an incremental pool to pool just the new data and update the silver table. Here Hootie automatically manages all the data cleaning and file sizing and other table management services. And in this case, it becomes a little less operational than what you may have to do at Spark. Now, to build a goal table, you'll still create your timetable where you can perform the join operation. But the changes are incrementally updated to the goal table, so you're not doing a full table scan or a full table rewrite. And then you can use a query engine to do an efficient lookup. But now that I'm introducing Apache Hootie, you're probably like, well, what is that? This brings us into the overview of what is Apache Hootie. So previously, we looked at the bottlenecks of the medallion architecture. But what if we just flipped it and you actually had these features or services enabled? So Apache Hootie is a data lake house problem that provides database-like features to your data lake. And with Hootie, you can have these fully automated table services that continually schedules and orchestrates the clustering, the compaction, the cleaning, the file sizing, indexing and so much more in order to ensure your tables are always up and ready. In addition, you can replace old-school batch pipelines with this incremental framework on your data lake. And Hootie allows for quick updates and delete data with a fast, plug-able indexing mechanism. And this includes support with streaming workloads with full support for out-of-order data, bursty data, and data deduplication. But since Hootie provides an indexing mechanism, you can take the data updates from an upstream database and apply role-level changes downstream to downstream application. And as a result, the incremental framework allows for faster ingestion, lower processing times for your analytical workloads. But Hootie's features and services enable for faster performance. We can go from hours to days on updating downstream applications to just minutes. So let's take a bird's-eye view of what this platform actually looks like. So a lot of times when you think of lake house formats, you think they're just a table format. But Hootie is actually a fully comprehensive platform that's designed for data ingestion and processing. And it's equipped with a wide range of features and services aimed at maximizing the efficiency of both writing and reading data. So at the foundational level, you have your data lake storage. This is while your data resides in some format like Parquet or Avro. But upon this, you have the transactional database layer. And this is where Hootie's true power shines as it offers multiple services, including table services, indexing, concurrency control mechanisms, and others. These services collectively offer significant enhancements to data and table management. And so table services facilitates the handling of data at scale. Indexing helps with the trivial operations and concurrency control inserts at this consistency of data across multiple operations. After the data has been successfully ingested and managed through the transactional layer, Hootie offers the ability to connect with the process data with popular computations like Presto. So by facilitating these integrations, Hootie allows for efficient and advanced querying of data. But now that we have this bird's eye view of what Apache Hootie is, we're going to dive into a little bit of what the table kind of looks like. So a Hootie table consists of file sizes. And each file size contains a base file, which is Dart Parquet. Parquet might produce at some certain commit time or instant time, along with a set of log files that contains inserts or updates to the base file since the base file was last produced. And as you can see in the top half of the diagram, a group of file sizes is known as a file group. So when the writes come in on the top half of the diagram, the records are written to the file sizes, and each record has a key that is mapped to a particular file group. So let's talk about the advantages of having this particular file layout. From the right side, if you have multiple table services running in the background, the services don't block each other because Hootie has multi-version concurrency control. And from the read side, the file layout facilitates the query engine to query a table at a particular point in time. And when we talk about the incremental framework, this becomes a really an important point. Now on the bottom half, when records get written into a file group, Hootie's timeline records the commit action that was done. And the timeline structurally, if you create like a Hootie project, it's located in the . Hootie folder. And it's essentially an event log. And there are different actions that can be recorded to the timeline. For example, if there's a clustering event or a compaction event, basically things that you do to the table get recorded into the timeline. And there are time stamps associated with every action, along with some metadata about it. Now following the timeline, there's the metadata table. The metadata table is structurally different from the timeline in it that it's an internal merge on read table. The metadata table is a central place for all the files metadata. And when a commit happens, the metadata table gets equally updated as well. And you can just think of the metadata table as like this big index. So this brings us to a really good point that Hootie stores state. So when we talked about the timeline in the metadata table, this is how Hootie stores state. So if a record has an update, Hootie checks the record's key to see if the record exists in the file group. And if it does, it updates that particular file size depending on where the record is located. But equally, the timeline will get updated as well. And since Hootie maintains a timeline of when an action or write occurs to a Hootie table, you can essentially find out what changes occur in that time range. And then from there, you can update downstream applications or table with just that data using Hootie's incremental framework. So if you look in the slide here, you can see an incremental query. There's a T1, T minus 1 and T. You can specify the time range that you want to see when the updates have happened and just grab those changes. So before we double click into the incremental framework with the CDC feature for Hootie, let's see how Hootie is being used in the ecosystem. So Hootie is proven at Massive Kill, both Uber, Walmart and GE use Hootie for their mission-critical apps. In particular, ByteDance uses Hootie at Exibite scale for a single table. And even this, Hootie is able to bring that analytics from days to minutes. But one of the ways that Hootie brings it down is through its incremental processing framework. Recently, we introduced the CDC feature with the incremental processing framework, and this brings us into the next section of where we're going to talk about the incremental processing. So to recap, though, the Mendelian architecture represented a straightforward and more simplistic approach of constructing your bronze, silver and gold tables. And the standout feature that spares you from conducting gold tables can is Hootie's incremental framework. Using this framework, only the changes are streamed to downstream tables. So to realize an end-to-end incremental processing, Hootie provides a Hootie streamer to efficiently pull changes from the source and support mutable data and record-level changes and conveniently write the data to downstream syncs all the way from the source to bronze to silver and the gold layers. Here's an example of how you can use Hootie streamer to construct an incremental processing end-to-end. A common use case is streaming the changelogs from a database like Postgres through Debezium and Kafka. Each message has the before and after images reflecting the changes. The schema is registered to the schema registry. In the first step, the Hootie streamer gets the new data from the last checkpoint and bulks inserts them into the bronze layer. The bronze table contains exact raw events from the Kafka source for further processing. Next, another Hootie streamer is constructed to do any kind of data cleaning and augmentation. For example, users can transform their data by flattening fields, selecting relevant fields within projections, and any other custom transformations that they want to do. But once that's done, the data's are upserted to a silver table, which is a clean data set. Once the new changes are landed to a silver table, the subsequent Hootie streamer job conducts a more complex operation with business logic using the SQL provided by you, like joining the dimension tables and other data from multiple tables. But after the complex business logic is applied on the changes, the records are upserted to a gold summary table for data analytics. But one key functionality here is supporting mutable data and incremental processing. So let's take a deeper look at how Hootie takes the changes, handles the mutations, and streams the changes downstream. So when we look under the hood, there are quite a few steps between taking the incremental changes from source and streaming from the Hootie to the downstream. So to enable mutable data at record level, Hootie provides built-in support on locating the records and the record payload and merging so that the user can customize their inserts, updates, and delete logic. As I mentioned earlier, there needs to be consistency between the index and the data. So metadata can be used for reading and writing the table. Hootie provides automatic metadata management on the Hootie timeline and the metadata table. Besides managing the data and metadata, Hootie automatically optimizes the data layout on storage with a small file handling and table services like compaction and clustering so that the query engines can read well-sized files and improve the query performance. Alongside the incremental processing, there could be concurrent writers, for example, backfill jobs to rewrite old data or jobs to delete selective data. So Hootie provides optimistic concurrency control and multi-version concurrency control for different use cases to efficiently handle multiple writes. So now you may wonder how Hootie handles record level mutation which is necessary for incremental processing. Hootie provides a payload merge API for inserts, updates, and deletes so that users can customize what they need. So let's walk through this example. Let's say you have a table to store bank accounts. Each entry has the UUID, the name of the account, and the last updated timestamp in balance. But just like other databases, Hootie requires the primary key field to be specified by the user to identify the unique records. And the primary key field here in this example is the UUID. For each incoming batch, Hootie looks at the primary key, the UUID to identify whether an input record is an insert, update, or delete. In incoming batch one, we have one insert and one update. During the insert operation, the table is updated by inserting the row with UUID 3 for me and updating the existing record for Ethan's account. For the next incoming batch, the first record is marked as delete, and Hootie deletes this entry. The second record is another update for the XYZ account. Now if you look at the results on the insert operation, the balance for Ethan's account is not changed, and this is because we want to honor the balance of the latest timestamp. In this case, Hootie looks at the ordering field and makes sure to ignore the later arriving data from the application's perspective, so my account won't get $20 from nowhere. Hootie has built-in support for event time ordering, which is prevailing in streaming and incremental processing. But once the data mutation is done, Hootie attaches metadata such as the commit time and the file name to each record and also updates the metadata in the Hootie timeline, and these essentially serve as a state for streaming changes for Hootie table. So while the record level mutation, while for record level mutation, the primary key is required, or in some use cases like event log ejection, which inserts your data, Hootie supports automatic primary key generation so you don't actually have to specify primary keys, and this is a feature that we've released recently. So aside from the existing incrementing pools, Hootie provides a new CDC mode for incremental processing, and this provides a debesium-like change logs with the before and after images, and here in the sample code, you can read the incremental data in the CDC format. For inserts, the before image is null, and the after image is the new value. For update, the before and after images show the values for the before and after the changes, and for deletes, the before image is the record, and the delete image that comes after is null. In the CDC feature, you can use this data to further transform and process both before and after values. So let's do a case walkthrough on what this kind of looks like if you were to build an application. So when we talk about the machine, what this machine pipeline kind of looks like, I'm going to focus on the ingestion tables, the optimal and processed and refined customer's data, and then from there, once you get the data, you can deploy it. So here's a sample architecture that we'll walk through, and I'm going to take each portion and walk through it. In the first section, you'll have a person signing up for an account, making purchases, clicking on stuff, and updating their cart. From there, the data is either sent to a transactional database or a streaming source like Kafka, and then dumped into a data lake creating the raw layer. When data is inserted into the raw layer, it'll be indexed for whatever index you want to use, and here, yeah, you can pick whatever index you want to use. And then from here, we have the clickstream data. So you can see the clickstream data has a couple of fields, like session ID, URL, and description of what that is. You have the sample purchase schema, which includes the purchase ID, the quantity, the purchase price, and there is a description for what that is. You might have also a sense of cart activity. You might need the customer ID, the product IT, the activity type, whether they add it or remove the assets and the quantity that they want, how much they want to purchase it and much more. And then you have the customer schema, which is typically, you know, the customer's metadata. So to create the silver layer, the data is incrementally pooled where you have row-level updates occur. And Hoody only applies updates to records if there's new data and basically avoids the full table scan. From there, you can create a temp table and join the various silver tables to build a gold or fact table. So then after you perform the join, Hoody will only incrementally pool the updates to the gold table and update their appropriate records without doing a full table scan. And if you want to write a simple join query, this is what it exactly looks like. You can get the customer's first name and last name, the clickstream URL, the time step and product information, and here we're correlating the user's activities with purchases. From there, you can perform a left join on the clickstream via the customer ID and you can also do another left join on the purchases based on the customer ID. You can filter through this time range and then you can order by the time step and purchase date. Once you get the results, you save it to a gold or fact table and the query engine can be used to read the data and results can be used to populate downstream applications. You can move it to a model and then from there to play it. So this wraps it up. I'm going to talk a little bit about Hoody's roadmap really quickly. We recently released 1.0.0.beta1 and this release has a couple of game-changing features. One thing that's pretty cool is we have non-blocking concurrency control for high-streaming rights. So multiple rioters can operate on the table with non-blocking conflict resolution and this can kind of reduce the wait times or the bottlenecks that you might have and it's actually ideal for high-streaming rights because transactions can proceed independently and concurrently leading to increase in throughput and overall system responsiveness and this is really good if you have CDC workloads, you can link in much more. The other thing that's pretty cool is that Hoody now also supports functional indexes and it's built on top of Hoody's multimodal indexing subsystem. It's essentially an index on a function applied to a column and it enhances access speeds and integrates partitioning into the indexing process. You can easily manage these indexes through SQL syntax so you can create index if it doesn't exist on some table using some column name and then you can probably provide some of the best use. If you want to learn about the One Beta release you can visit hoody.apache.org or when these slides are available you can check out the RFC69 which has more details on this. You can come and build with the community and if you want to learn more about how you can use Apache Hoody for your machine learning pipelines there's a lot of resources here. You can follow us on LinkedIn and Twitter. You can also scan the QR code to follow us on Slack. We host weekly office hours that will be great to come through that and me and a PMC member are usually there to help answer questions. I think that's it for me. I want to thank you for attending my talk and I'm happy to take some questions now. Thank you. Oh cool. The compaction kicks in? Yeah. Long running jobs. Okay. So this requires a lot of more questioning. Are you on the hoody Slack? Okay. And have you posted the questions on the general? Okay. I'm usually responding. Maybe I missed your question, but usually one of the things I want to find out is the error message of where compaction is failing. So I need the full stack error to kind of see what is going on. Some things that could be a resource issue. I don't know if you have multiple services running together and maybe there might be a conflict there. I need to look at the convicts and see what is going on there. So I would recommend shoot me a message on Slack, tag me and tell me that you're at the session and I'll make sure to get to it sometime today. Because I need more, I need to know the hoody version. I need to know more things about what you're doing to kind of debug the compaction failure. Yeah. Let's follow up on Slack. I feel like I need to triage it more. But thank you for your question. Any other questions? Like machine learning in general? Or... Okay. How are you dealing with some of the machine learning pipelines? Or what are you using? Oh. Redis? Okay. Got it. Sounds good. All right. Thanks, guys.


A question arises that what amount of cash a firm should distribute among its shareholders or it should retain in the business. Now before going into the answer of this question, one must think that what purpose of XX cash the firm can have with itself. We know that the firm can use XX cash in projects or other financial instruments. Now under perfect market conditions, once a firm takes all the positive NPV projects, it becomes indifferent between the XX cash it can save or it can pay out to the shareholders. However, with certain market imperfections, there is a trade-off that is the XX cash retained by the firm can reduce the cost of issuing new capital in the future, but it also increases the taxes and the agency cost of the firm. Now we see that the new projects are financed by the cash retained by the firm with itself and the positive NPV projects undertaken by the firm are responsible for creating the shareholders value whereas the negative NPV projects do not create any value for the firm. However, once the firm takes all positive NPV projects, it becomes indifferent between the saving of the XX cash with itself or paying it out as dividend. This means that under perfect market conditions, the XX cash has no effect on the value of the firm. This means that retention versus payout decisions is similar to the dividend versus share repurchase decisions as both have no relation with the firm value because these are irrelevant to the value of the firm. Now to understand this relationship, let take an example in which we have a perfect market condition. The firm has access cash holding of 100,000. Now the firm has two options of using this access cash. The first option is that to buying off one year treasury bills and earn 6% rate of interest thereon retain it and pay to the shareholders after one year as the dividend. The second option is that the firm can immediately give out this access cash to the shareholders as dividend and these shareholders will be at liberty to invest this amount at their own. So, what will be the best option for the shareholders under perfect market condition? You see that the present value of option 1 using the proceeds of tables by the firm has an amount equal to 100,000 whereas the immediate dividend payment to the shareholders is also equal to the amount of 100,000. So, we see the implication of this access cash under perfect market conditions that this access cash holding or its immediate payment to the shareholders has nothing to do with the firm value. Now we have another example with the imperfect market condition and that imperfect market condition is the existence of the corporate taxes. Again we will be repeating the same example with the addition that now there is the corporate tax rate of 35%. So, what will be the best option for the shareholders under this imperfect market conditions and we assume that this shareholder is a pension fund investor. Now our example solution says that the future value of the first alternative is equivalent to 100 and 3000 whereas the present value, a future value of the second option in which the immediate amount is paid to the shareholder and the shareholder invest that amount at the rate of 6%. The future value of second option is equal to 106,000. Now in the conditions of imperfect market in the form of existence of taxes, we see that now shareholder is at the benefit as he is earning more future value than the company itself. What is the effect of investors' taxes on the firm value? See that the decense on dividend payments versus cash retentions may also affect the taxes paid by the shareholders. Now let's see an example. We have a firm who has only one asset of $100 and that is the cash. We assume identical tax rates for the investors. Now the firm has two options on this $100 cash. At first, the firm can immediately pay this amount as dividend to the shareholders and second option is that the firm can retain this cash and earn interest to pay a dividend to the shareholders next year. As a first option, if the firm pays dividend immediately and shuts down, then the share price will be equal to the price where we assume the X price is equal to 0 because the firm is shutting down. So the firm value will be equal to 0 plus 100 as dividend into 1 minus tax on dividend over 1 minus tax on the capital gain. Now tax on dividend or the TD is basically the tax that shareholder is paying on the dividend. Whereas TG is the tax credit allowed to the shareholders on the capital loss due to the shutdown by the firm. The second and the alternative option with the firm is that the firm can retain this cash and earn interest there on through the investment in T-bills. And after paying the corporate tax on the interest earned, the firm can pay the rest amount to the shareholders as a perpetual dividend every year. So in this case, the dividend that is going to the shareholder is basically the after tax cost of interest to the firm. This means that the shareholders or the investors cost of capital in this case will be equal to investing in T-bills at her own. And if the investor is investing this amount at its own, her cost of capital will be equal to after tax interest cost of capital which in this equation TI is the tax on the interest income earned by the investor. Now in these conditions, how the firm value can be determined to see that the price of the firm then will be equal to the combined effect of the corporate tax rate and tax on the dividend in relation to the tax on the interest income earned by the shareholder. And that relationship is basically representing the effective tax disadvantage to the shareholders. Now we see that the interest on cash returned by a firm is taxed twice as we have seen in our earlier equation. It takes in two ways. At first, the firm pays tax on the cash retention at the rate of tax C or the corporate tax and then the investor pays tax on the increased value of the firm at the rate of G or the capital gain tax. The earnings made by the shareholders at their own are taxed once, so there is the difference between the earnings by the firm and the earnings by the shareholder, the cash retained by the firm and the firm is earning interest there on and then it distributes the amount to the shareholders. The firm is earning, the firm is paying tax twice whereas if the shareholder is alone investing the amount and paying tax, he is paying tax only once, so the cost of retaining cash depends upon the combined effect of the corporate tax, tax on dividend and the tax on interest earned by the shareholder. Let's see an example to understand this effect. We have a corporate tax rate of 35%, dividend tax rate of 39.6% and the capital gain tax of 20%. The effective takes disadvantage in this case for the shareholder on retained cash is equal to 13.9%. So we see that after adjusting for the investor's taxes, there is still a large tax disadvantage for the shareholder of the firm and that is due to the excess cash retained by the firm.


Hello everyone and welcome back to day two of AWS re-invent 2021 theCUBE's continuous coverage. My name is Dave Vellante. I'm here with my co-host, David Nicholson. We've got two sets. We had two remote sets prior to the show. We're running all kinds of activities and we've got AWS executives, partners, ecosystem technologists. Scott Weber is here as the director and an AWS partner ambassador from PwC. Scott, it's good to see you. Nice to meet you guys. Thanks for letting me be here. So your expertise is around application modernization. It's a hot theme these days. If you're a company with a lot of legacy debt, you've got a big complex application portfolio. I would think, especially with the four smarts to digital over the last year and a half, two years, now is really a time, probably too late, to really start thinking about rationalizing your portfolio. What are you seeing in the space? Yeah, definitely. We're seeing the customers that have reached that point. I view modernization as sort of the second wave of cloud that's coming. So you had your first wave, the early adopters that lifted and shifted into the cloud. We still have people looking at getting into the cloud. But for those that went early, now they're saying, how do I get more out of the cloud? How do I get closer to cloud native? And that's what we're starting to see around this modernization move, is I want to start to utilize those higher level services from AWS and the cloud providers. I want to get a better return. I want to stop worrying about running infrastructure and hardware.  And then of course, the dot com crashed. But today it's different. It's really about the business impact, the business outcome that you can drive and transforming your digital business. So, how do you, as a technology agnostic, you know, consultant, help a company understand what they should leave alone or sunset, what they should aggressively migrate. What's the process that you use to do that? You know, in some ways we go back, we can reuse sort of those six R's that maybe got a customer to the cloud or as they're on that cloud journey, right? And you really want to focus on where can you optimize ROI, right? And you're going to come across those things that you're going to be like, look, it's maybe it's a vendor-caught solution, right? There's not a lot we can do there. You're just going to have to continue down that path unless we can look to move that to a SaaS service. Maybe the vendor's gone to a SaaS offering, right? Or we get into looking at, they've done development in-house. But that development is still monolithic running on virtual machines, either in the data center or in AWS. But it's a critical system to that business. Maybe it's become fragile. How can we now modernize that? Because that's where there's going to be a great return on investment for that customer. And it's also going to allow business agility for those customers, right? As we can get them to microservices and Lambda and function as a service, the blast radius for changes becomes smaller. It allows the customer to move faster in what they're doing. So it's the rationalization becomes what's driving the business forward? What's critical to the business, but what's holding them back as well so that the customers can start to move faster? So it's a formula of, okay, what's the business value of those applications essentially? You can kind of rank that, but then it's a formula of, there's a cost equation. That's pretty straightforward to figure out what the AS is and the 2B. But then there's a speed. Like an ongoing time to value from a developer standpoint. And then I guess there's risk, right? Have you got your core jewels? You maybe don't want to touch those yet. Is that kind of your algorithm? It is, and on that sort of cost and value piece, that's where we can really see some interesting things happen where as we get customers away from license to OSs, proprietary databases, the return on investment can be huge. So we've helped customers migrate from running .NET applications on top of a typical Microsoft Windows stack and SQL server stack all the way taking those workloads all the way either to Linux containers or all the way to serverless. If we're going to take all the steps to rewrite, you can drive 60, 70, 80% of the cost of operating that platform out of it. Then you start this flywheel effect of reinvesting that money back into the next project to help the customer move forward. Quick follow-up, I know you want to jump in. Yeah, yeah, yeah. Why wouldn't a customer that's a Microsoft customer just run that on Azure? Why AWS? I mean, that's a good question and that sort of gets into a lot of philosophical like discussion we've talked about for a long time. The fact of the matter is, the majority of your Windows workloads still run on top of AWS to do today. I would argue AWS has some pretty superior things in their underlying architecture, their Nitro architectures and things like that. But I think it's also choice, right? And the whole move of .NET to Linux, Microsoft started that. They put the ability to, you can run SQL Server on top of Linux. Well, if I run SQL Server on top of Linux, I take out 20% of my cost right there. They put the support in for .NET Core to be able to run on Linux or on containers, but that's to help the developers move faster. That's to help us get to microservices. So that cloud provider choice, I think it becomes a bigger discussion, but a lot of people are choosing AWS because they're not just doing Microsoft workloads, right? And again, we could get very deep into trade-offs on why one over the other, but customers are choosing AWS for a lot of these work. Diversity and better cloud, better infrastructures. Yeah, and philosophical is an interesting way to look at it when it becomes a hostage negotiation. I'm not sure there was a lot of philosophy involved when Server and SQL 2008 were being end-of-support life and people were told, move it to Azure and we'll take care of you, don't move it to Azure, you're on your own. But something on the subject of ROI, ROI is typically measured over time. How do you rectify and address the sort of CIO dilemma, which is that if ROI is being delivered fantastically in four years, but the average tenure of a CIO is 2.7 years, how do you address that? What is the sweet spot for time frames that you're seeing for people to actually implement when you consider, as was mentioned today, the keynote, that somewhere around 15% of IT spend is in cloud today, which leaves 85% of it on premises. So what do we do about that? Yeah, no, that's a great question. So I think I like to get small wins. So find a very big pain point for that customer. How can we start to get them some small wins and start that flywheel effect going of like you save money here, now can we reinvest and start to show some wins? But we've engaged in projects where we've completely rewritten a whole application stack that was the core service for a business in a year and a half. And we took them from a run rate of somewhere between 40 and $60,000 a month. Had they been running that in AWS, they were running it in a data center today. So that was our estimate to less than $5,000 a month to run that application on a serverless platform inside of AWS. So when you talk about modernizing an application environment, that's typically not thought of as low hanging fruit. So does that mean that all the low hanging fruit has been consumed? Are all the net new things that are developed in a cloud native format? Have they already been done? Is this the only frontier for opportunity now? No, no, it's not the only frontier. I mean, there's a lot of customers that are still just trying to get into the cloud. A lot of traffic is out there. Yeah, yeah. And you look at things like mainframe as well, right?  We saw it in the keynote today of a new sort of service offering around helping customers rationalize how to start to do things with the mainframe. So, but sometimes you can get those easy wins. Like we find a scalability issue, right? You know, and we can inject scalability and pull back costs very rapidly, right?  The provision for max capacity that may happen 10% of the year, now they're vastly overpaying, right? So we can still get some easy wins with slight tweaks to the platform while we help them rationalize those longer build times. I think the other thing we're starting to see is a shift in CIOs that are coming more from a software background too, right? That aren't from the peer infrastructure background. And as we see those software-based CIOs start to come in, we're starting to understand the gain that can be had of making the investment in the software and those upgrades to the software. And they're 10 years elongating, because, you know, CIO career is over was the joke. Now you're losing CIOs because they're going on to bigger and better. They're getting more options. I mean, they're becoming rock stars again. I want to ask you just as a side about that mainframe compatible runtime that they announced. Because it sounds like you've got some experience in converting the frame. Because I've always been a skeptic. And I, you know, we've seen this movie before where people have to freeze code. They've got to freeze code for 18 months. It takes 24 months or they, but now it's cloud. Adam Salipsky said we can cut migration time, which is critical here by two thirds. Right. Because that's the key. If you can reduce the time in which you have to freeze the code or maybe not even freeze the code. Again, I'm a skeptic, but what are you seeing with practical experience? At PWC we're seeing a lot of customers start down this path. And, you know, the ROI is pretty amazing when once you get in and you really start to dig in of what it can be if to go down this path. And there's a lot of tools out there. There's a gentleman on our team that's a real genius with this. And he's helped multiple customers go down this path. You know, there's tools that can start to do code conversion for you. I mean, we all get a little skeptical on those things because you never know what the machine's going to try to make the code look like. But it's a starting point, right? But there is more. Like a pre-wash. Yeah, there's more and more design patterns coming out to help us down those pathways. But it goes back to agility for the business because a lot of these customers running mainframes today are looking at a six month release cycle if they want to make any changes to their environment, right? If we can get them into an agile mindset to a microservice, they can get to two weeks or less for release cycles. So it's a big win for the company overall. Yes, there's a risk, right? But I think you can take, you can try to de-risk it as much as you can. You don't take the core, the absolute core critical piece of that mainframe. You start to pick away around the edges and you get comfortable with what you're doing. Right. Yeah, and going back to the concept of ROI, specifically in the mainframe space, there have been some not-so-subtle nudges from the marketplace that changed the dynamics associated with staying on your mainframe. Because if I tell you that the tax to stay on your mainframe is going to triple, or quadruple over the next several years, that changes the balance. Yeah. So you have the old guard in the software business who will remain nameless, jacking up the prices because they feel like, you know what? What are you going to do? Right. What are you going to do other than write me a check? And the answer is, we'll move. Right. Right? Yeah, it's reached the point that the companies are moving and what I think companies start to see too is when we talk about purpose-driven databases, Adam was talking about that in the keynote today, too. We've seen that with customers. When we've done builds, what's the right database for this data? And now you can start to get things moving even faster and you unleash new ways of thinking.  And the companies aren't happy about it. Well, yes. But look, we're talking about Oracle in particular. No, well, no. Okay. Yeah, so that's all right. That's one of them. But Oracle invests in its database and it's two different theories, right? Adam, today, I think it's over the right job. APIs and primitives and Oracle takes the kind of Swiss army knife approach, but they do invest. If you have hardcore mission critical, recovery is everything, there's a risk factor involved there.  You're going to go to AWS. But so, but it gets to my question. Having done a lot of TCO analysis, it used to be labor was always two thirds of the cost. With automation, especially in Oracle environments, software license costs are the dominant component. It's maybe less true for SQL server, certainly true for DB2. I remember the early days of the flash, we used to tell customers, install flash, you're going to be able to consolidate, reduce your Oracle licenses when they come up. So that was a preferred strategy. But what are you seeing in terms of the ability, first of all, is that a correct premise that software licenses are still a big component or an increasingly large component? And how do you unshackle from that? Yeah, so definitely software licensing costs for the OSs and for the databases are huge.  And the numbers are similar for Oracle, right? And I talked to a lot of customers are like, but we don't know Postgres. But it's not really that different, right? It's still data modeling. And when you get to these managed services platforms like RDS and Aurora, you free up those DBAs to do the higher value things, right? The ROI of a DBA is not managing memory and disk and babysitting the servers. It's helping the developers build better data models, right? And those sorts of things that are higher value. So it is a big thing. And we're seeing customers saying like, help us reduce this licensing cost, right? And help us be more efficient because the open platforms now, especially in the relational database area, are on par in a lot of ways with the Oracle and the SQL servers, right? So then you start to say, well, what am I gaining by paying and being sort of held hostage to these numbers, right? So we definitely see customers making this transition. Yeah, I mean, the point about Postgres is a good one because you're going to get enterprise class recoverability. Now, but even EDB would say, okay, don't start with your mission critical core, pick around the edges, there's what he's saying over and over time, you're going to become more cloud native and get to the point. You know, can you get to that point where everything's cloud native, everything is a service, maybe not a hundred percent, but a large part of your application portfolio can get there, right? Yeah, but you're going to find those, that goes back to doing that application tiering and evaluation and ROI. So, you know, we have a case study that we did with Constellation Brands where they really needed a B2B type ordering portal solution. And they looked at sort of the typical vendors and that, you know, packaged solution, if you will, a COTS type solution. And we proposed doing a full custom solution, soup the nuts and building it natively in AWS. And it was built completely on top of platform services. There was no servers in that environment when we were done. We were using AWS Fargate to run their containers on top of, we were using RDS Postgres, we were using Lambda in some places, we were using DynamoDB for holding in flight orders. And so, the whole environment is deployable from one CloudFormation template. You know, so it completely changed how we even went through the testing of the thing because you ran the same CloudFormation template to deploy to a different environment and you knew you were getting the same exact thing. And so, they went from, they no longer had to worry about securing the underlying compute, secure the containers, run on top of Fargate, use a platform service for your databases and it was a beautiful solution for them.  We heard that from NASDAQ this morning. I mean, amazing story. She said our first Amazon bill was 20 bucks. I bet it's higher now, but first hits free kind of thing. But the point is, if people talk about the AWS bill, et cetera, no question, you should try to optimize that. But at the end of the day, it's about the business value, Scott, isn't it? Yeah, it is. Hey, thanks so much for coming to theCUBE. It was great, great perspectives. I always appreciate having you guys on. Yep, thank you very much. Thank you. All right, and keep it right there, Dave Nicholson and I will be right back. You're watching theCUBE's coverage of AWS re-invent 2021.


What's up everybody, Jason here for Jasper's Case Breaks.com. We're giving away 10 Father's Day packs, guys. It's Father's Day weekend, so happy early Father's Day to everybody out there. So with this break here, this is a Father's Day pack promotion break where just for buying into the break, you're automatically gonna get 10 Father's Day packs, which have chances at relics, autographs, base card, number cards, all that good stuff. And this is a 2022 Panini Donneress UFC, excuse me, five box half-case break. Random division number one. So again, all card ship, everybody gets a random weight class, and every spot bought in this break gets you 10 Father's Day packs ship sealed, which is an amazing value. And again guys, there are 11 total spots, so we're gonna go with the divisions, of course, men's Bantamweight, all the way down to women's Starweight, plus all other cards not listed. So everything will ship, guys. And here we go, guys. So here's the dice roller, here's the customer names, and the class is right there. So let's roll it. Full 12 times, six and a six. One, two, three, four, five, six, seven, eight, nine, 10, 11, 12. Perfect. Logan at the top down to Anthony, and we got 12 here. One, two, three, four, five, six, seven, eight. Six, seven, eight, nine, 10, 11, 12. Full 12, 12 times. Starweight, all other cards listed down to women's Flyweights. So Logan, you have the Starweight for women's, and all other cards not listed. We have Logan has the Flyweight for men's, Austin with men's Lightweight, Howard with men's Bantamweight, Chris with Featherweight for men's, Howard men's Walterweight, Michael Robinson men's Middleweight, Simon with women's Featherweight slash Bantamweight, Joseph with men's Light Heavyweight, Aaron you have men's Heavyweight, and then Anthony with women's Flyweights. So just to remember, guys, you guys can potentially make any trades if you guys want. Cameras that are screen share are fuzzy FYI. It looks perfect on my side, Doug. So most likely it's probably your internet connection. I usually like to keep my screen open on this side. But yeah, remember sometimes too, in your settings on YouTube, it automatically just puts you to auto. So depending on your connection or everything, remember to change the quality. You can have it as high as like 1440. 1440 P60, even 1080p HD. So I usually like to personally look at myself just in case there is some technical difficulties, but I'm connected to some pretty strong wifi and some strong hardwires. So if it looks good for us, I'm most likely it's just internet connection for the customers. Yeah, you most likely had it on auto. So it was just gonna connect to whatever your connection was. And it'll just drastically sometimes get better, sometimes it'll get worse. All right, well, I don't think there's gonna be any potential trades, right? So just set this up and close it out. All right, so it seems like there's five boxes on top, five boxes on the bottom here. So that's probably how I'll distribute them on which ones we're gonna do. Top or bottom. So these would have been the bottom or this is about left or right, I should say. All the top boxes would be on the left or right side. And then all these other boxes here would be on the so that way they stick together. All right, let's see what we can roll here guys. Good luck. One, two, three, four, five, six for the right. Try that again. There you go, that's a little bit better. Actually still on the same one. One, two, three. Right there. Four, five, six over here. We'll be for the second half.  There's everybody there. And we also write this down right here too. All right, also two guys. It seems like that hip parade is baseball selling out. I think we sold out of the Stratospots. Now we just have to sell out that pack for it. It's at 21. So just a little FY. All right, here we go guys. So I believe Donner's gives you one autograph and one relic per box on average. Oh, that you basically loaded? Nice, yeah. Yeah, so it looks like you're just gonna sell out that pack filler, man. The pack filler is right next to it. It's a 2021 Prism sell-out. Also two guys, if you guys didn't check out the break schedule, I put in the time for the giveaway today. It's gonna be around four o'clock. So it'll be either the middle of this break or it'll be right after the break. All right, here we go. And I think the weight classes are gonna be in the back. So everything that's shipped, but we'll kind of skim through the base. It's also kind of like in a little pattern. Chef Schenkel marbles, and then we got a crunch time Habit. Bruce Buffer announcer, we got Jair. Paul Felder, octagon royalty, a Billy, Ricky. Stipe, crunch time Robert Whitaker. Crunch time there, Kamaru Usman. Rosario, Morono. Got a numbered Brad Tavares of 75, which is a middleweight for men's, men's middleweight, which would be Michael. Max Holloway, Marvel, Carl Rosa, and Giuliana, Giuliana Peña. That's a signature series autograph there. And what is our weight class again? I don't think it says it on the autograph. Look for a base of hers. Bantam weight, all right, thanks Anthony. Yeah, I'll double check that too, but I trust Anthony as well. So it says Bantam weight, that's Bantam weight women. So that'll be Simon who has women's featherweight slash Bantam weight. The team will also go through the checklist as well, so. Nice, Daniel Cormier to 49. That's a blue right there for heavyweight. What's up, Tommy? There she is right there, Bantam weight. So thank you, Anthony, appreciate it man. Purple press proof. You got a Peter Yan, crunch time with Gregor. Bruce Buffer, all righty guys. One box down, let's go with the next one. Let's actually keep these separate. All right, next one guys. We have a number here of Darren Elkins, 299, Whitaker, Valentino Soschenko crunch time. We have a patch maybe? Wow, look at that, that's a number to 10. 10 of 10, octagon royalty, Claudia. That is a straw weight. Which would be Logan with women's straw weight plus all other cards not listed.  I think it says like respect patch in there, that's cool. These are great cards to get autographs on too. I mean the gloss part is gonna be kind of tough, but at least just like the photo of it, I'm saying I guess it's just more easier. Islam signature series, which I'll have to look, that went up too. If I find this base part, I'll look it up right now. Nice to be, I'm all gonna head off to 99. That'll be lightweight. What's up Robert? I know, right? Today's technically my Thursday, but it is Friday. Tomorrow will be my officially my Friday. Lightweight Anthony, appreciate it. So that'll be men's lightweight Austin Caruso, Michael Bisbee, Mangano, Jan, Peter Yan, Whitaker. I think that's been the only one, Robert. Hopefully some more tonight potentially. I think that's been the only one though. That is a Tony Ferguson lightweight and Whitaker press-proof crunch time. We have a Kevin Holland number 275, middleweight. Our box three, Santiago signature series. Bonsini-Bible, that one's number to 99. Both featherweight, Alexander Volkonowski and Brian Ortega, featherweight. Men's featherweight is Chris and then Anthony, thank you, that's welterweight for Santiago. So welterweight, Howard for men's welterweight. Nice color, they're at a 25, nice Holly Holm. Bantamweight, which is Simon. Tracy Cortez, that's flyweight, which is Anthony. I was gonna say, where's Robert Whitaker, octagon royalty, that is middleweight, which is Michael. We have a Aleski, 2.99, heavyweight, which is Aaron. Connor McGregor, Dustin Portier, he has marbles. Little chip on that bottom, top left corner, that is Raquel Pennington to 49, which is a Bantamweight. All right guys, well, if you guys haven't seen the break schedule, I'll be doing the giveaway around four o'clock guys, so that means that for the next 15 minutes or so, don't forget to chat to get your entry into the giveaway. Yeah, that's what I was gonna say, Anthony, who's in your flyweight? Cause I feel like Bantamweight is a very popular one besides obviously the bigger, heavier ones, but I was trying to think of like where's Rose? I'm gonna do this where, I forget what weight class she's at technically, but I'm trading and Chef Shane throwing that. I used to be like really into UFC when I was younger, like watching like T-Door T's and, you know, Shuffle Down, Alistair Overeem when he was fighting everywhere. And, but I kind of fell off it. It's more like I just don't, I'm working here on Saturdays with you guys too, so like, I don't really get to watch the pay-per-views, so I'm like kind of just kind of, I don't know bases though, it's somewhere over there, but at the end of the day, we're still giving away something, right? I think it's a box, the box or a pack. Yeah, there's just so many new names now dude, like it's crazy. And again, that's just how it is with any sport that people don't follow as much, right? Kind of just like it was track.  Who's this? You don't know who this is? What's wrong with you? What's up Aaron, how are you buddy? Pizzle in the Hizzo? I don't know, Gregor Press Peruv. Curtis Bladex, signature series. Looks like he's at least a light heavyweight probably. Over there, Johnny Walker. Heavyweight, okay. So I thought he was at least light heavyweight, but heavyweight, that'd be Aaron. All right, so we're not at 25, Bobby Green. Lightweight, which is Austin. Desanya, yeah, he's pretty good. I'm not even gone into. Yeah, anyway, what's going on man, how are you? Happy Friday man. Gain, that is another heavyweight, going to Aaron Salinas. We have a Dominic Reyes to 99. Light heavyweights. I kind of just skimmed through the bass, just bass parts. Yeah, yeah, the basses, yeah, just, you just have to chat. So by you just saying, what's the giveaway? Or how do you get into the giveaway? That's it. You could have wrote one letter, one sentence, one paragraph, it don't matter. Just actively chatting within the next 15 minutes gets you an entry. 42 out of 75, that's Rafael Desanyos, light weight. Yeah, very easy enough, very, very easy. All right, last box over there. Oh, nice. Did I pull you the Pitt Supernova, the White Contra? Or just another Supernova? Man, those like four straight cases where we just had at least a Cal Pitts autograph. You guys will have fun sorting this. It's by weight class, but everything ships so. I probably actually need the monster box. Do you mind giving me one? Tahiti Cutie, what's going on? Love it? Yeah, I just want it. Well, you can bring two of you on, but just one I need. Well, no, it's cause like, as of the last like week or so, two weeks, all the products that we've had have been like easy sorts, you know? Like to the point where like some sorters are just like kind of just doing like other projects in the store, you know? So it's like easy sorts, right? It takes like five, 10 minutes.  It's like, this isn't like back in the old days when I was just like the only sorter and sorting out like five case Bowman Breaks by player on eBay. You know, it took me like a week or two to sort it out. We're far from that. Alexa Grasso. She wants to pick it up. She said, okay, nothing. Whoa, look at that. Gold to 10 voices of the Octagon, Megan Oliva or Olivia. That was gonna probably go to all other cards not listed, which is Logan and Sterling is Banton Wayne. Is that Flyway Anthony for you? Nice. We have a Luke Rockhold to 49, which is a light heavyweight. Just base, base, base, base. And we have a Octagon canvas of Chris Daukis, which is a heavyweight. Heavyweight having a good break there. That is Aaron Salinas. We have Bobby Green to 99. Another lightweight. Kono Moguega. Randy Costa to 75. Banton weights. Nice one out of 99 crunch time. Justin Gaethje. That's gonna be lightweight, which is Austin Caruso. Caruso. I'm opposed to it right now, Anthony. Don't worry, man. All right, guys, well, there you go. That was the break. So the crunch time numbered, Relic, number to 10, autograph, Grasso, Blades, Santiago, Islam. Nice out of 10 right there of Claudia. Venya, and we had a couple of Relics here that I'll talk a little really quick. This one was Robert Whitaker, and this one was Paul Felder. And then you had all the numbered cards here, all the colors. And I just kind of used all the marbles and crunch times. So there you go, guys. So again, everybody also is gonna be getting 10 for all of these packs each. And everything does ship, guys. So I'll post the next one, guys.  Jasper's Case Breaks dot com.


SEGA DRUNK In the book, Console Wars, by Blake Harris, there's a significant portion dedicated to how Sega eventually became unraveled in the mid-90s. Sega of America president and CEO at the time, Tom Kalinsky, talks quite a bit about how Sega was just spread too thinly. Developers had to make games for the Genesis, Sega CD, 32X, the Sega channel, and the upcoming Sega Saturn. If Sega of Japan hadn't intervened and insisted on releasing the 32X peripheral, Kalinsky says that he would have rode the Genesis exclusively past 1995 and into 1996 so that the Saturn could be properly advertised as a big deal and so more games could be available at launch. And yeah, he's got a pretty good point. Sure, hindsight is 20-20 and all that, but developers were still cranking out really high-quality games for the Genesis as late as 1994. Take Pulse Man, for instance. Pulse was a game that was initially released for the Mega Drive overseas and was only available in the US via the Sega channel, but if this was given a proper release at the time, it could have gone a long way in keeping the Genesis relevant. I mean, this is like a top 30 or maybe even a top 25 game for the console, at the very least, and it was buried on the freaking Sega channel. Pulse Man was made in 1994 by Game Freak, yes, the same Game Freak that went on to make the Pokemon series, and one of only two titles they would make for the Genesis, the other being a game based on the manga Magical Tararuto. This game is an action-platformer that has a ton of things going for it. You play as Pulse Man, or is that Pulseman? If you pronounce it Pulseman, it makes him sound like a third-string running back for Georgia Tech. But instead, Pulseman is a teenager with electrical superpowers because he's the love-child of some digitized mother that exists in some virtual world and some regular-ass scientist who evidently got really lonely one day. But seriously, the story here is pretty bonkers. This dude fell in love with and procreated with some program he created.  Pulse Man has a health meter that can usually take three or four hits, and you get three lives and five continues to get through seven levels broken up into a few different stages. No saves or passwords here, but there is a stage select code. Just press A-B-C-C-B-A at the Sega logo on the second player controller, then go to the options menu. I always appreciate stuff like that because it gives you the option to see the whole game in a shorter amount of time. The C-button jumps, B is your melee attack, and when Pulseman gathers momentum and breaks out into a run and gets covered in lightning or static electricity or whatever that is, you can press the A-button to pinball all around the level reaching areas you couldn't otherwise reach while taking out enemies along the way. It's pretty cool. It reminds me of Sparkster's attack in Rocket Knight Adventures. There's all sorts of other stuff you can do as well, like down and B does a kick, up and B does a backflip. You can also use your charge to throw a fireball, and you can also dash by tapping forward twice. It's an easy temptation to compare this game to Mega Man, what with the whole man motif and having a stage select at the beginning and all that, but it's a lot closer to the first Sonic the Hedgehog with a dash of the aforementioned Rocket Knight Adventures with a vibe similar to the typical fictional futuristic cyberspace stuff you'd see in movies like Tron or Lawnmower Man. It all comes together as a really fun playthrough and something unique, and it definitely has that first party polish to it. This isn't just another action platformer, it's pretty clear the people that made this one were firing on all cylinders. For example, you come across these warp points that put you in some cyberspace world, and that's where the first boss fight takes place, where you have to dodge this wire hand that rips apart the ground beneath you. That's really cool. There's also parts throughout the game where you have to use your electrical charge to follow these wires, which is a lot trickier than it sounds. You got this crazy boss fight, which is like something out of a treasure game.  You even get bonus stages that briefly turn the game into super breakout. That's a nice touch. Just the overall look of this game is crazy. I mean, just look at this casino setting here. I feel like my TV is going to burst into flames trying to support all these colors at once. The music here is absolutely top notch. It's some of the best music on the Sega Genesis. I know it's a running gag for some people that make fun of how the Genesis relied on FM synthesis, which produced some not so desirable results, but man, just listen to this. Even the voices sound reasonably good here too. So yeah, the best compliment I can give Pulse Man is that this is the kind of game that rewards you for your practice. The more you play it, and the more you learn the levels, the more satisfying it is to rip through this game. I usually pick out one thing I really look forward to, the more I play through a game like this, but with Pulse Man, it's just everything. I wanted to see more boss fights, hear more of the soundtrack, see more of the backgrounds. It's just a great time. I will say this is definitely a long game for its kind. There's a lot of content here, so this is not a quick play-through by any means, but hey, more to enjoy. And definitely check this one out any way you can. All right, I want to thank you for watching, and I hope you have a great rest of your day.


Thank you for joining day two of the virtual Central Bank of the Future Conference. Our program will begin in five minutes with welcome remarks from Federal Reserve Bank of San Francisco head of supervision Tracy A. Bassenger. Good morning, good afternoon, and perhaps good evening in some places. Welcome to day two of the Central Bank of the Future Conference. For those of you who are just joining us today, I'm Tracy Bassenger, the head of supervision at the San Francisco Fed, and we're delighted to be co-hosting this conference with Michael Barr and Adrian Harris and their team at the University of Michigan. Our goal is to identify technology, business, and policy innovations that central banks can employ to better foster a more inclusive financial system and economy. Yesterday we had a great series of presentations focused on the role of monetary policy and economic inequality. The presenters explored how we might be bold and push the boundaries of today's monetary policy frameworks and tools to make a difference on economic inclusion. In particular, they challenged us to embrace innovation, to be fiercely impatient because the issues of inequity are so urgent, while also being patient and accepting incremental change given the magnitude of the issues. Our speakers also talked about the linkage between climate change and economic inequality, as well as the importance of consumer protection as we try to solve these problems. Today we'll shift and focus on payment systems and the global exploration of central bank digital currencies. We'll start with the keynote from Timothy Massade at the Harvard Kennedy School, who has a deep background in financial services. Then we'll have a panel on central banks evolving role in payment systems given technology advances, followed by a couple sessions on central bank digital currencies. The day will end with the fireside conversation between the director general of the People's Bank of China and Michael Barr. During these sessions you'll hear from a great lineup of speakers. Michael Barr will moderate the first panel that includes Tillman Airbag from Flourish Ventures, Andrea Dunlop from the Access Group, and Musajimo from the Central Bank of Nigeria. Daniel Gorfine from Gattica Horizons will then introduce us to central bank digital currencies followed by Christopher Brummer from the Georgetown Law School, moderating a panel that explores this topic. Participating in his panel will be Lisa Cook from Michigan State University, Christopher Giancarlo from Wilkie Farr Gallagher, Morgan Ricks from the Vanderbilt Law School, and Bejoi de Gupta with eCurrency. You can watch today's sessions live through the Engagement Hub or you can go to YouTube where you can pause at any time or view the sessions at a later date. The Engagement Hub is for registered attendees only and has the full agenda and additional resources for navigating the conference. There are also three breakout sessions for registered attendees that will be from one to two Pacific or four to five Eastern after our main session concludes for the day. Today's breakouts include a session on monetizing privacy, another one on the U.S. data infrastructure, and finally one on when financial inclusion falls short, a global perspective. If you haven't done so already, please do sign up for one of these sessions. As I mentioned, we'll start today with the keynote address from Timothy G. Massad, a senior fellow at the Harvard Kennedy School. Mr. Massad served as the chairman of the U.S. Commodity Futures Trading Commission from 2014 to 2017, where he oversaw that agency's implementation of the Dodd-Frank Act. Prior to that, he served as the Assistant Secretary for Financial Stability at the U.S. Treasury Department. Welcome, Mr. Massad. We look forward to your remarks. Okay. Good morning, good afternoon, good evening, wherever you are. Thank you, Tracy, for the introduction. And I also want to thank the University of Michigan Center on Finance Law and Policy, and the Federal Reserve Bank of San Francisco. And in particular, my former colleagues, Michael Barr and Adrian Harris, were inviting me to be part of this very important conference. It's really an honor to be here. I would guess that in the last few decades, the phrases Federal Reserve System and Financial Inclusion were not often used in the same sentence, but recent events make it quite appropriate to hold a three day conference to discuss how central banks can help achieve a more inclusive economy and financial system. Today, I want to talk about four things. First, a few words about the pandemic and the Fed's response to it, which has increased our attention to issues of inclusion. Second, I want to discuss the views of some of those I will call the ghosts of central banks, central banking's past, because I believe we can find some inspiration there. Then I will turn to new technologies, specifically digital currencies and how that can advance inclusion. And finally, I'll offer a few ideas for further exploration. My comments focus on the United States, but some of the ideas will be relevant for other countries. The Fed's response to the turmoil in financial markets last spring was pathbreaking. The Fed provided liquidity to a broad range of financial markets and bought or stood ready to buy financial assets in a broader range than even 2008. We will never know what might have happened without those actions, but we might have had a serious financial crisis on our hands. But the Fed's actions did not and could not prevent a severe drop in employment and income, particularly for those on the lower rungs of the economic ladder. Congress provided some relief in the form of the CARES Act, and it gave Treasury a war chest with which to support the Fed's interventions. Congress has not been able to provide further relief since March, and we have not contained the virus. So we have had the so called K shaped recovery. Where those of us at this conference and professional and white collar workers generally, assuming we in our families are healthy, are probably experiencing the pandemic as an inconvenience, but not really as an economic hardship. Frontline workers, service workers, blue collar workers, the unemployed, those without savings are suffering much more. The K shaped recovery aggravates longer term trends that have increased the gap between rich and poor and limited social mobility. It also adds to the challenges we face over racial justice. The unemployment rate for black and Latinx workers, for example, has significantly exceeded the rate for white workers throughout the pandemic. I am glad the Federal Reserve did what it did in the spring, especially as someone who helped fight the 2008 crisis. But it's hard not to think that the Fed's actions benefited those of us who hold financial assets disproportionately, and probably contributed to the gap between rich and poor. That makes the question of how does the central bank advance financial inclusion even more relevant? Let's consider small businesses. Business closures, especially for small businesses have been substantial and have been far worse among minority owners. From February to April alone, the number of active businesses, business owners declined by 22%. The declines among black and Latinx business owners were much greater 41% and 32% respectively. The Fed has attempted to address the credit needs of Main Street through the Main Street municipal lending facilities, but use has been quite limited. That's partly a demand issue for many businesses more debt won't bring more consumers. But it's also due to facility design problems. And these facilities have provoked debate. Some of question whether it should be the Fed's job to provide credit to businesses as opposed to liquidity to banks and financial markets. But in my view, Congress directed the Treasury and the Fed to do just that in the CARES Act. If anything, it has been the Treasury's reluctance to fully use the authority it was given and take losses that have hurt this effort. The Treasury has allocated only 195 billion of the 454 billion war chest. The actual investment to date is far less. So to invert a well known phrase, the 454 billion dollar war chest should have come with a label, lose it in order to use it. Now, it's admittedly challenging from an implementation standpoint for the Fed to provide credit to small businesses. But this is where I think we can find some inspiration at least from the ghosts of central banking's past. Consider William McCoon, the principal architect of the sub-treasury plan of the populist movement in the late 19th century. This was decades before the creation of the Federal Reserve. Farmers had great difficulty getting the credit they needed at harvest time to finance the shipment of crops due to an inelastic money supply that was based on the gold standard and dominated by New York banks. Farmers were forced to borrow at usurious rates and often couldn't repay those loans due to successive ways of deflation, which led to despair and failure for many. So McCoon proposed that the US Treasury should create federal warehouses and grain elevators in all agricultural areas at which farmers could store their crops and borrow from the government. The farmer would be paid in fiat currency, which would not be tied to the gold standard. The government would use its full faith and credit authority to increase the money supply at harvest time just when liquidity needs were highest. The plan was a radical democratic idea that was viewed as crazy by the establishment in its day. And the populist never mustered sufficient political force to advance it. But John Maynard Keynes recognized that it anticipated his own analysis. And the populists are generally remembered not for the sub-treasury plan, but for William Jennings Bryan, who collapsed that complex proposal for a more elastic money supply into a simple but eloquent opposition to the gold standard. Bryan was a colorful but unsuccessful democratic nominee for president. Yet his influence and the populist influence was important in the later debate over the creation of the Federal Reserve. In that debate, President Woodrow Wilson had to overcome the bankers who wanted a quote, bankers bank close book, quote, free from any political oversight. He had to persuade Democrats who feared the entity would simply support the money trust of J.P. Morgan and come to the rescue of the New York banks during times of financial stress. He succeeded by insisting on federal control of the board as well as the regional structure we are familiar with today. As well as by persuading his then Secretary of State William Jennings Bryan to publicly support the plan. So imagine if McCoon, Bryan or Wilson could be with us today. Would they be shocked or at least disappointed by the fact that the Fed has bought trillions of dollars and bonds and other financial assets to support financial markets but struggled to help small business. Would they react to the subject matter of this conference by saying, well, yeah, it's about time. So as we discuss how a central bank should promote inclusion, our history can provide some inspiration. Let me turn to the technologies of the future, specifically digital currencies. Surely central bank digital currencies will be part of the future, even if as many have said it's not clear what problem they solve today. While there has been interest in CBDCs for a few years, Facebook's announcement of its intention to create Libra, a global stablecoin, really, as German Jerome Powell said, lit a fire under central banks. And the pandemic has intensified that interest because of the shift to greater digital commerce. How might digital currencies increase financial inclusion? Well, Facebook claimed its mission was to help the 1.7 billion people around the world who lack access to the financial system. And clearly, if Libra was operational and could simply lower the cost of cross border remittances, a $700 billion market, where it typically costs $7, maybe even $11 to send $100 overseas. Poor people, people on the lower rungs of the income ladder would benefit greatly. There has been great skepticism over Facebook's motives concern over its power concerns about the risks of global coin stablecoins generally. But I believe we should design a proper regulatory framework to encourage such private initiatives as I will discuss in a moment. As for CBDCs themselves, one of the most inclusive design possibilities is a direct retail system, in which everyone, not just financial institutions, has an account at the central bank. But at least as of today, there are significant disadvantages to that approach. In particular, there is the risk of disintermediation of the banking system that people withdraw money from commercial banks and park it at the central bank. That could adversely affect credit creation. And it could increase financial instability at precisely the wrong time. People might withdraw more from the banking system as stress increases. There is also the issue of whether we want the central bank to be responsible for the administrative burden associated with direct accounts. But I want to suggest in a moment a slim down version for your consideration. Meanwhile, China is rolling out a two tiered system, where the central bank issues a CBDC to commercial banks and those banks remain the primary interface with consumers. And two tiered systems would appear to avoid the disintermediation risk and administrative burden for the central bank. China's launch of a CBDC, however, appears to be motivated not by financial inclusion, but by a long term desire to reduce the importance of the US dollar in international markets, gain first mover advantages in a new technology, and perhaps exercise greater control over its very own private mobile payments industry. Now we can learn lessons from that industry from the dramatic growth of China's mobile payments industry, which has given people more, which has given more people access to two financial services. It should remind us that there are first technological initiatives we can take well short of launching a full scale CBDC that can advance inclusion. Like simply creating broad brand infrastructure and making sure everyone can afford a smartphone. I have also recently written about the lessons we can learn in how to create a regulatory framework under which private initiatives and digital currencies can proceed by looking at China. There are a number of things they've done that I think are would be relevant to our experience. So on that note, let me turn to offering five ideas for exploration as to how the Federal Reserve might advance inclusion. This is the whiteboarding part of the speech. I'll admit some of these are not fully baked, but I hope they'll provoke some thought. So first, while I support continued CBDC research and development, private stablecoins like Libra may be a faster means to advance inclusion in the short term. But they should only be allowed to operate under a proper and complete regulatory framework, which we do not have today. So could the Fed create that regulatory framework by creating a core CBDC operating system on top of which private firms can build stablecoin applications, much as people build applications to operate on your iPhone. The Fed would design the regulatory standards that must be met to issue a stablecoin back to solely by the US dollar. This would include consumer protection requirements, prudential requirements, standards on use and accessibility of data, privacy, interoperability requirements and financial inclusion goals or metrics. And I also acknowledge that we must address the power, the concentrated power of technological come of large technology platforms, but I'm going to leave that issue aside for today. In any event, I think it's important to try to harness private initiative with proper oversight to advance inclusion through digital currencies. Second, instead of creating Fed accounts for everyone, which raises some fears about disintermediation as well as the administrative burden for the Fed. Let's explore the idea of the Fed offering digital accounts for the underbank and unbanked, who represent 25% of American households. That statistic alone should move us to action. The accounts could have limited features so that those who can sustain a commercial bank account would not want one. This might include a maximum balance amount, maybe even a cap on the number of transactions per month. We talk about a public option when it comes to government provided health care. Well, this could be a public option when it comes to bank accounts. Because of the limited features that take up would be limited and the administrative burden would be minimal and the risk of disintermediation nonexistent. The goal would be to help the underserved gain access to the financial system and graduate over time to a commercial bank account. Third, let's extend the operating hours of the Federal Reserve wholesale payment services, the Fed wire and the National Settlement Service. Those who live paycheck to paycheck constantly face the risk that it can take days for a deposit to check to clear. That can make it hard to pay your bills. People go to check cashing stores because it's a faster way to get money. And while they pay high fees for that, those fees are often less than the overdraft charges they would otherwise incur at a bank. While the Fed has launched its FedNow initiative to achieve real-time payments, that won't be operational until 2023 or 24, even if it is on schedule. In the meantime, let's simply make payments faster, as George Sogan of the Cato Institute and Aaron Klein of Brookings have recently proposed. The Fed system has limited weekday hours and doesn't operate on weekends and holidays. If you increase those hours, interbank clearing of payments would be faster and banks could clear individual checks faster. Now, the Fed is scheduled to increase weekday hours slightly in March, but it could go further. And if the Fed did so, it could also consider mandating banks to clear checks faster. And that would also help those who live paycheck to paycheck.  Can the Fed use its regulatory power or the convening power of the regional banks to promote safe accounts? Now, these are the model bank account proposed by the FDIC that is responsive to the needs of the underbanked and unbanked. The account template involves a low balance requirement, a debit card for ATM withdrawals, point of sale transactions, and mobile payments, but no check writing privileges in order to keep bank costs low. And there are no overdraft fees. The FDI study found them to be very successful and having a bank account is a path to financial independence. So could the Fed use its power to promote these in some way? Could they be made part of the scoring for the Community Reinvestment Act, which the Fed has recently proposed to revise? Is there some other way to incentivize banks to provide more of these accounts? I hope we can explore that idea. And fifth, how about a new capital purchase program for small banks so that we strengthen their capital to weather the pandemic and increase their ability to lend to small businesses? Now, Congress would need to repurpose some of the funds allocated to Treasury in the CARES Act toward this use, those funds that it hasn't used to support the Fed. And the Fed would then assist Treasury in the implementation of the program. Small banks account for a disproportionately higher share of small business loans and micro loans, micro loans being those loans less than $100,000. According to a recent Fed study, banks with less than $1 billion of assets held 7.4% of all banking industry assets, but 26% of small business loans and 18.1% of micro loans. The report said this may be because smaller banks are more knowledgeable about local markets, which is important in making risky relationship dependent small business loans. Smaller banks face greater risks than the days ahead because of their disproportionately greater exposure to small business and to commercial real estate. So bolstering their capital might prevent failures or further industry consolidation. The capital purchase program from 2008 was one of the most successful programs in fighting the financial crisis because it provided the capital needed for banks to absorb losses. We also made a profit on it. We know how to implement such a program. We could design a new version that is exclusively available to small banks. The cost to the Treasury would be minimal, given low interest rates. Banks would ultimately pay back the capital and would pay a small dividend rate in the meantime. Capital could be used to offset losses, giving banks better ability to lend, and we could create restrictions so the capital isn't used for inappropriate purposes. Let me close with this thought. As we discuss innovative technologies, exciting new ideas and other intellectually stimulating proposals as to how to advance financial inclusion, let's not forget the relatively simple things that deserve to be prioritized. The Treasury Department distributed approximately 160 million CARES Act payments this past spring. About 35 million of those went by check, which meant the recipients waited at least one extra month and by some estimates up to four months before they had good funds. That is a long time when you are unemployed or lacking in assets. Surely with a financial system as sophisticated as ours, we can and must fix that. Thank you very much.