(upbeat music) >> It's theCUBE live in Las Vegas, covering AWS re:Invent 2022. This is the first full day of coverage. We will be here tomorrow and Thursday but we started last night. So hopefully you've caught some of those interviews. Lisa Martin here in Vegas with Paul Gillin. Paul, it's great to be back. We just saw a tweet from a very reliable source saying that there are upwards of 70,000 people here at rei:Invent '22 >> I think there's 70,000 people just in that aisle right there. >> I think so. It's been great so far we've gotten, what are some of the things that you have been excited about today? >> Data, I just see data everywhere, which very much relates to our next guest. Companies realizing the value of data and the strategic value of data, beginning to treat it as an asset rather than just exhaust. I see a lot of focus on app development here and building scalable applications now. Developers have to get over that, have to sort of reorient themselves toward building around the set of cloud native primitives which I think we'll see some amazing applications come out of that. >> Absolutely, we will. We're pleased to welcome back one of our alumni to the program. Shinji Kim joins us, the CEO and founder of Select Star. Welcome back Shinji. It's great to have you. >> Thanks Lisa, great to be back. >> So for the audience who may not know much about Select Star before we start digging into all of the good stuff give us a little overview about what the company does and what differentiates you. >> Sure, so Select Star is an automated data discovery platform. We act like it's Google for data scientists, data analysts and data engineers to help find and understand their data better. Lot of companies today, like what you mentioned, Paul, have 100s and 1000s of database tables now swimming through large volumes of data and variety of data today and it's getting harder and harder for people that wants to utilize data make decisions around data and analyze data to truly have the full context of where this data came from, who do you think that's inside the company or what other analysis might have been done? So Select Star's role in this case is we connect different data warehouses BI tools, wherever the data is actually being used inside the company, bringing out all the usage analytics and the pipeline and the models in one place so anyone can search through what's available and how the data has been created, used and being analyzed within the company. So that's why we call it it's kind of like your Google for data. >> What are some of the biggest challenges to doing that? I mean you've got data squirreled away in lots of corners of the organization, Excel spreadsheets, thumb drives, cloud storage accounts. How granular do you get and what's the difficulty of finding all this data? >> So today we focus primarily on lot of cloud data warehouses and data lakes. So this includes data warehouses like Redshift, Snowflake (indistinct), Databricks, S3 buckets, where a lot of the data from different sources are arriving. Because this is a one area where a lot of analysis are now being done. This is a place where you can join other data sets within the same infrastructural umbrella. And so that is one portion that we always integrate with. The other part that we also integrate a lot with are the BI tools. So whether that's (indistinct) where you are running analysis, building reports, and dashboards. We will pull out how those are, which analysis has been done and which business stakeholders are consuming that data through those tools. So you also mentioned about the differentiation. I would say one of the biggest differentiation that we have in the market today is that we are more in the cloud. So it's very cloud native, fully managed SaaS service and it's really focused on user experience of how easily anyone can really search and understand data through Select Star. In the past, data catalogs as a sector has been primarily focused on inventorizing all your enterprise data which are in many disciplinary forces. So it was more focused on technical aspect of the metadata. At the same time now this enterprise data catalog is important and is needed for even smaller companies because they are dealing with ton of data. Another part that we also see is more of democratization of data. Many different types of users are utilizing data whether they are fully technical or not. So we had basically emphasis around how to make our user interface as intuitive as possible for business users or non-technical users but also bring out as much context as possible from the metadata and the laws that we have access to, to bring out these insights for our customers. >> Got it. What was the impetus or the catalyst to launch the business just a couple of years ago? >> Yeah, so prior to this I had another data startup called Concord Systems. We focused on distributed stream processing framework. I sold the company to Akamai which is now called ... and the product is now called IoT Edge Connect. Through Akamai I started working with a lot of enterprises in automotive and consumer electronics and this is where I saw lot of the issues starting to happen when enterprises are starting to try to use the data. Collection of data, storage of data, processing of data with the help of lot of cloud providers, scaling that is not going to be a challenge as much anymore. At the same time now lot of enterprises, what I realized is a lot of enterprises were sitting on top of ton of data that they may not know how to utilize it or know even how to give the access to because they are not 100% sure what's really inside. And more and more companies, as they are building up their cloud data warehouse infrastructure they're starting to run into the same issue. So this is a part that I felt like was missing gap in the market that I wanted to fulfill and that's why I started the company. >> I'm fascinated with some of the mechanics of doing that. In March of 2020 when lockdowns were happening worldwide you're starting new a company, you have to get funding, you have to hire people, you don't have a team in place presumably. So you have to build that as free to core. How did you do all that? (Shinji laughs) >> Yeah, that was definitely a lot of work just starting from scratch. But I've been brewing this idea, I would say three four months prior. I had a few other ideas. Basically after Akamai I took some time off and then when I decided I wanted to start another company there were a number of ideas that I was toying around with. And so late 2019 I was talking to a lot of different potential customers and users to learn a little bit more about whether my hypothesis around data discovery was true or not. And that kind of led into starting to build prototypes and designs and showing them around to see if there is an interest. So it's only after all those validations and conversations in place that I truly decided that I was going to start another company and it just happened to be at the timing of end of February, early March. So that's kind of how it happened. At the same time, I'm very lucky that I was able to have had number of investors that I kept in touch with and I kept them posted on how this process was going and that's why I think during the pandemic it was definitely not an easy thing to raise our initial seed round but we were able to close it and then move on to really start building the product in 2020. >> Now you were also entering a market that's there's quite a few competitors already in that market. What has been your strategy for getting a foot in the door, getting some name recognition for your company other than being on the queue? >> Yes, this is certainly part of it. So I think there are a few things. One is when I was doing my market research and even today there are a lot of customers out there looking for an easier, faster, time to value solution. >> Yes. >> In the market. Today, existing players and legacy players have a whole suite of platform. However, the implementation time for those platforms take six months or longer and they don't necessarily are built for lot of users to use. They are built for database administrators or more technical people to use so that they end up finding their data governance project not necessarily succeeding or getting as much value out of it as they were hoping for. So this is an area that we really try to fill the gaps in because for us from day one you will be able to see all the usage analysis, how your data models look like, and the analysis right up front. And this is one part that a lot of our customers really like and also some of those customers have moved from the legacy players to Select Star's floor. >> Interesting, so you're actually taking business from some of the legacy guys and girls that may not be able to move as fast and quickly as you can. But I'd love to hear, every company these days has to be a data company, whether it's a grocery store or obviously a bank or a car dealership, there's no choice anymore. As consumers, we have this expectation that we're going to be able to get what we want, self-service. So these companies have to figure out where all the data is, what's the insides, what does it say, how can they act on that quickly? And that's a big challenge to enable organizations to be able to see what it is that they have, where's the value, where's the liability as well. Give me a favorite customer story example that you think really highlights the value of what Select Star is delivering. >> Sure, so one customer that we helped and have been working with closely is Pitney Bowes. It's one of the oldest companies, 100 year old company in logistics and manufacturing. They have ton of IoT data they collect from parcels and all the tracking and all the manufacturing that they run. They have recently, I would say a couple years ago moved to a cloud data warehouse. And this is where their challenge around managing data have really started because they have many different teams accessing the data warehouses but maybe different teams creating different things that might have been created before and it's not clear to the other teams and there is no single source of truth that they could manage. So for them, as they were starting to look into implementing data mesh architecture they adopted Select Star. And they have a, as being a very large and also mature company they have considered a lot of other legacy solutions in the market as well. But they decided to give it a try with select Star mainly because all of the automated version of data modeling and the documentation that we were able to provide upfront. And with all that, with the implementation of Select Star now they claim that they save more than 30 hours a month of every person that they have in the data management team. And we have a case study about that. So this is like one place where we see it save a lot of time for the data team as well as all the consumers that data teams serve. >> I have to ask you this as a successful woman in technology, a field that has not been very inviting to women over the years, what do you think this industry has to do better in terms of bringing along girls and young women, particularly in secondary school to encourage them to pursue careers in science and technology? >> Like what could they do better? >> What could this industry do? What is this industry, these 70,000 people here need to do better? Of which maybe 15% are female. >> Yeah, so actually I do see a lot more women and minority in data analytics field which is always great to see, also like bridging the gap between technology and the business point of view. If anything as a takeaway I feel like just making more opportunities for everyone to participate is always great. I feel like there has been, or you know just like being in the industry, a lot of people tends to congregate with people that they know or more closed groups but having more inclusive open groups that is inviting regardless of the level or gender I think is definitely something that needs to be encouraged more just overall in the industry. >> I agree. I think the inclusivity is so important but it also needs to be intentional. We've done a lot of chatting with women in tech lately and we've been talking about this very topic and that they all talk about the inclusivity, diversity, equity but it needs to be intentional by companies to be able to do that. >> Right, and I think in a way if you were to put it as like women in tech then I feel like that's also making it more explosive. I think it's better when it's focused on the industry problem or like the subject matter, but then intentionally inviting more women and minority to participate so that there's more exchange with more diverse attendees in the AWS. >> That's a great point and I hope to your 0.1 day that we're able to get there, but we don't have to call out women in tech but it is just so much more even playing field. And I hope like you that we're on our way to doing that but it's amazing that Paul brought up that you started the company during the pandemic. Also as a female founder getting funding is incredibly difficult. So kudos to you. >> Thank you. >> For all the successes that you've had. Tell us what's next for Select Star before we get to that last question. >> Yeah, we have a lot of exciting features that have been recently released and also coming up. First and foremost we have an auto documentation feature that we recently released. We have a fairly sophisticated data lineage function that parses through activity log and sequel queries to give you what the data pipeline models look like. This allows you to tell what is the dependency of different tables and dashboards so you can plan what your migration or any changes that might happen in the data warehouse so that nothing breaks whenever these changes happen. We went one step further to that to understand how the data replication actually happens and based on that we are now able to detect which are the duplicated data sets and how each different field might have changed their data values. And if the data actually stays the same then we can also propagate the same documentation as well as tagging. So this is particularly useful if you are doing like a PII tagging, you just mark one thing once and based on the data model we will also have the rest of the PII that it's associated with. So that's one part. The second part is more on the security and data governance front. So we are really seeing policy based access control where you can define who can see what data in the catalog based on their team tags and how you want to define the model. So this allows more enterprises to be able to have different teams to work together. And last one at least we have more integrations that we are releasing. We have an upgraded integration now with Redshift so that there's an easy cloud formation template to get it set up, but we now have not added Databricks, and power BI as well. So there are lots of stuff coming up. >> Man, you have accomplished a lot in two and a half years Shinji, my goodness! Last question for you, describing Select Star in a bumper sticker, what would that bumper sticker say? >> So this is on our website, but yes, automated data catalog in 15 minutes would be what I would call. >> 15 minutes. That's awesome. Thank you so much for joining us back on the program reintroducing our audience to Select Star. And again, congratulations on the successes that you've had. You have to come back because what you're creating is a flywheel and I can't wait to see where it goes. >> Awesome, thanks so much for having me here. >> Oh, our pleasure. Shinji Kim and Paul Gillin, I'm Lisa Martin. You're watching theCUBE, the leader in live enterprise and emerging tech coverage. (upbeat music)

(bright music) >> Welcome back to the Cube. Our continuing coverage of Snowflake Summit 22, day two, lots of content as I've said, coming at you the last couple of days. Dave and I, Dave Vellante, and Lisa Martin are here with you. We have an exciting guest here next to talk with us about data discovery. Please welcome Shinji Kim, the founder and CEO at Select Star. Welcome to the program. >> Thanks for having me. >> Dave: Great to see you. >> Excited to be here. >> Talk to us about Select Star. What do you guys do? And then we're going to uncrack data discovery. >> Yeah, why'd you start the company? (Shinji laughing) >> Sure. So, Select Star is, on fully automated data discovery platform, that helps any company to be able to find, understand and manage their data. I started this company because after I sold my last company, Concord Systems to Akamai, I started working with a lot of global enterprise companies that manages a lot of IOT devices like automakers or consumer electronics companies. And it became very clear to me that companies are not going to stop anytime soon about collecting more data, more often, and trying to utilize them as much as they can. And cloud providers, and all the new technologies like Snowflake has really helped them to achieve that goal. But the challenges that, I've started noticing, from a lot of these enterprises, is that they now have 100s or 1000s of data sets that they have to manage. And when you are trying to use that data it's almost impossible to find which specific field which specific data sets that you should use out of 1000s and 100s of 1000s of data sets you have. So, that's why I felt like this is the next problem and challenge that I would like to solve. Also because, I have a background of working as a software engineer, data scientist, product manager, in the stages of creating data, transforming data and also querying data and trying to make business decisions on data. Having a right context about the data, is so important, for me to use that data. So, for us, we are trying to solve that challenge around finding and understanding data, and we call that data discovery. >> Wow. That's music to my ears here because I can't tell you how many meetings I've been in, where somebody presents some data and I say, okay, what's the source of that data? What are the assumptions used to derive data? I have different data, you know, and then it becomes this waste of time. My data's better than your data, or everybody has an agenda. You cut through that. >> Yeah, so, data discovery, in a nutshell, we defining as finding, understanding, and managing your data. So, in Select Star, we will automatically bring out, all your, like the schema information. Where does data exist? We will also analyze the SQL query logs as well as activity logs that's generated by any applications and BI tools that are connected on top of your data warehouse, so that any time you're looking at a database any particular database table, column or dashboard, we will tell you, where did this data come from? Where did it originate from? How was this transformed? And which reporting table does this exist? Who's using this data the most inside the company? How are they using it? And which are the dashboards and reports that are built on top of this data set? So you don't have to go out and ask everybody else, "Hey, I'm looking for this type of data. "Has anybody worked with this?" This is actually something that I realize a lot of data analysts and data scientists waste their time on. So yeah, that's really the, what we call fully automated data context that we provide to our customers so that you can truly use all the data that you have in your data warehouse. >> And you do this by understanding the metadata? Or is it some kind of scanning? Or using math or code? >> It's both. So, first of all, we do connect and bring out all the metadata. So, that's all the information under information schema. And then, we also look at all the query history. So all your select SQL queries, all your create queries, create table queries, create view queries. And based on that, we will also match the metadata, where it exists inside those queries and logs. And based on that, we will generate first and foremost, what we would call column level data lineage. Data lineage is all about showing you the flow of data from where it was originated, how it was transformed, and where it exists now. And also, what we call popularity. Who's using what data? How are they using it? And in aggregate, you can also find out, which are the most important data sets in our company? Which are the data sets that can be deprecated because it was like a duplicate of other data sets and nobody's using it anymore? And we like put a, like a popularity score for every single data asset that you have in your company so you can see how that's being used. >> How do your customers take action on the information that you provide them? Do they ultimately automate it? Do they go through a process of sort of the human in the loop? >> Well, we do the automation for them. >> Yeah. >> And we do also provide them with a, really easy to use user interface so that they can add any semantic level data on top. So, that's like tags. Like whether you want to market as, this is a analyst approved table, or do not use table or if you want to put a PII classification of data you can do that on a column. And we will automatically either propagate those annotations throughout the platform. We will also automatically propagate any same matching documentation that you might want to use within the data warehouse. And we will also provide you with, more of a rich text documentation that you can also add on top as a business glossary or like a Wiki that business users can, get a better understanding of data concepts and models as well. >> How do they tag the data? Do they use another tool that does that or? >> No, they can tag it within Select Star. Any table or column has a little icon, tag icon, so you can click on it. Or, we can also give you a view of every database page will have all the tables in one place. You can add a keyword and bulk tag. >> So humans tag. >> Yeah. So humans tag. So in the beginning, humans tag, and then we will automate the propagation of that tag. So if you already tagged, let's say SSN field as a PII, then we will find all the other columns that may use the exact same data, and also tag the same, just as an example. >> Okay so you, once the human puts it in there then you automate the downstream. 'Cause humans sometimes aren't great at classifying and tagging and inconsistencies and I would think that you could use math to improve that. >> And we do have some plans to add more automated tagging system. For example, we are already, we don't necessarily tag them, but we give our customers filters on top of their search results to see, which are the data sets that nobody's using anymore? Which are the data sets that's being created very recently? And you can also filter by who created them or who are the owners. So these are some of the aspects of the data or even like when's the last time was this data updated? So these are the aspects of the operational metadata that we are starting to automate to put more automated annotation, I would say is more coming up towards the end of the year. But in terms of semantic level tagging, like is this data set around customers? Is this data set for marketing, sales, customer support? That is something that we are giving a really easy to use interface for the data team to be able to easily organize them. >> How are you helping organizations? We think of the all the privacy regulations and legislations. How is Select Star a facilitator of data privacy for your clients? Is it part of that play? >> So, I would say, one of the main use cases of data discovery, is data governance. So, starting this company and starting to work with a lot of fortune 500 companies, as well as I would say more like recently IPOed companies that have grown very fast in Silicon valley. Some of those customers have told us that they initially adopted Select Star because they needed a good data catalog and search platform for their data team. But as they are starting to use Select Star and starting to see all these insights about their own data warehouse, they are all kicking off their new data governance projects, because they get to see a really good lay of the land, of how the data is being accessed today. So, this is why we have a very easy to use and also programmatic API so that you can add tags, ownership, and set access control through a Select Star. We are actually just releasing a beta version of our, what we call policy based access control where you can use either role based and attribute based access control so that different roles of the users get to see different versions of a Select star when they log in. And this is just the beginning. Like PII is for example, any column that's already marked as PII. We will always strip out the value before it gets fully processed within Select Star. So even if anybody might stumble upon any sequel queries that other analysts have run, those values won't be available in Select Star at all. >> And you started the company right before the lockdown, right? That must have been crazy. >> Yes, March, 2020 is our, my incorporation of Select Star. It was a very interesting time to start the company. And in a way, I'm glad I did. We had a lot of focus time to really, go heads down, build out the product, and work closely with our customer. And today it's really awesome to get to, provide that support to more customers today. >> And so, what are you doing with Snowflake? >> So Snowflake has been a great partner for us. Lot of customers and Snowflake is really great for this. Basically building single source of truth of your data by connecting all your source of, databases, as well as like your ERP, CRM systems, ad systems, marketing systems, SaaS platform, you can connect them now all to Snowflake, that will all dump all the data inside. So that, allows data team to be able to actually join and crossmatch the customer data across so many different applications. And what we see from a lot of Snowflake customers, hence they end up with many different schemas and tens of thousands of tables. And for them now they are requiring or needing more of a better data discovery tool so that they can use and leverage Snowflake data that they have. So, in that regard so we are a snowflake data governance accelerator partner. And as part of that accelerator program, one of the things that we've integrated with Snowflake is, what we call Snowflake Tag Sync. So if you create any tags in Select Star, and you marked it as a PII, we will also replicate the same tag, to Snowflake. >> Yeah. Okay. >> And so everything is synced in there. And on top of that, a lot of our customers really like using our column level lineage, because we will show how all the data tables within Snowflake is connected to another. And actually last but not least, we actually just released this feature today, called the auto generated ER diagram. ER diagram stands for Entity Relationship Diagram. ERD is like a blueprint of your data model. When your engineers and data architects start creating tables in databases, this is a diagram that they will put together, to show how they are translating business logic into data models in the databases. And that includes, which are the fields for primary keys, foreign keys, and how are different like when you look at Star schema, how different tables are joined together. When all these tables gets migrated into Snowflake, a lot of them actually lose the, the relationships of primary keys and foreign keys. So, many analysts, what we found, is that they are starting to guess, how to join different tables, how to use different data sets together. But because we know how other analysts have actually joined and used the tables in the past, we can give them the guidance and really nice diagram that they can refer to. So that is the ERD diagram that we are releasing today. Available for all customers including our free customers, where you can select any tables, and we will show you the relationship that table has, that you can use right away in your sequel queries. >> And that will facilitate, that simplifies doing more complex joins, yes? Which is an Achilles heel of Snowflake. That's not really what they are about, but they have to rely on the ecosystem to help them do that, which has always been their strategy. The company founded in March 2020, amazing. And then relatively small still, yes? Or is it self-funded? I mean, you've raised a little bit of money, but what's your status? >> Yeah, we raised our seed funding when I first started the company. We've also raised another round of bridge round last year and we plan to raise our another venture round of funding soon. >> Great. Awesome. >> And we're going to be making those investments. What are some of the key parts of the business that you're going to use that funding for? >> There's a lot to build. (Shinji laughing) >> Dave: Yeah. Engineering. >> Obviously more automation features, but having, I would say right now, we have now built a really good foundation of data discovery and that includes fully automated data cataloging for metadata, column level lineage, and also building the usage model like popularity, who's using what, all that type of stuff. So, now we are starting to build really exciting features that leverages these fundamental aspects of data discovery, like auto propagation of tags. We also do auto propagation of documentation. So you write one column description once, and it will get replicated and changed everywhere throughout your data model. We have also other things that we have in store especially more for next year, are, package support for specific use cases like data governance, self-service analytics and cloud cost management. >> Nice, lots of work-- >> Dave: Impressive, I'm blown away. >> And you've accomplished this during a pandemic that's even more impressive. Thank you so much Shinji for coming on, talking to us about Select Star. What you're enabling organizations to do, really derive the context from that data taking a lot of manual work away. We appreciate your insights and your time and wish you the best of luck. >> Well, thanks so much for having me here. This has been great. >> Good. Thanks so much. For Dave Vellante, I'm Lisa Martin. You're watching the Cube's coverage of Snowflake Summit 22, day two. Stick around. Dave has an industry analyst panel common up next. You won't want to miss it. (soft music)