Key Trends in Databases with Nikita Shamgunov, Founder and CEO at Neon
09/19/23 • 50 min
This episode features an interview with Nikita Shamgunov, legendary founder of MemSQL (now SingleStore). His latest endeavor, Neon, offers serverless Postgres as a fully managed multi-cloud database that separates storage and compute, with auto scaling, branching, and bottomless storage.
Nikita is also a Partner at Khosla Ventures, where he is incubating Neon and raised $104M to date. He is passionate about deep tech, data infrastructure, and system software. Prior to Neon, Nikita co-founded MemSQL (now SingleStore), a unicorn data and analytics company valued at over $1.3 billion. He served as a founding CTO, and then CEO, successfully scaling the company to over $40 million in ARR. Prior to SingleStore, he worked as a senior engineer at Facebook and at Microsoft on SQL Server. Nikita earned a Ph.D. in computer science from the National Research University in St. Petersburg, Russia.
In this episode, Nikita recounts the founding stories behind both MemSQL and Neon, and elaborates on the key trends driving database technologies today, from serverless and generative AI, to open data and the convergence of transactional and analytical workloads.
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Key Quotes
Amplitude and Mixpanel, they basically are a time series database underneath with the UI. Time series data tends to be, you know, ‘write once’, most of it. And so, you need to take advantage of those techniques that data warehouses are basically born with, right? They are in the business of storing data relatively cheaply. And every enterprise, unless it's not like an archaic enterprise, should have a data warehouse. So it makes only too much sense to put this into a data warehouse rather than either a custom database, you know, like a platform like Datadog, Mixpanel, Amplitude. Plus you have additional benefits from it because you can cross reference that data with the rest of the business data." - Nikita Shamgunov
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Episode Timestamps
(01:41) Founding stories behind MemSQL and Neon
(03:39) Addressing new challenges for databases
(09:20) Criteria for evaluating databases
(12:36) HTAP and zero ETL between transactional and analytical applications
(19:07) Evolving standards around table formats
(24:07) Thoughts on Generative AI and LLM-native in the data warehouse
(26:38) Warehouse centric approaches to data storage
(29:45) Open source for data warehouses
(33:54) Potential for new applications to be built around real time applications
(38:10) Managing large volumes of data
(40:59) Serverless Postgres is as easy as Stripe
(45:40) Takeaways
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Links
This episode features an interview with Nikita Shamgunov, legendary founder of MemSQL (now SingleStore). His latest endeavor, Neon, offers serverless Postgres as a fully managed multi-cloud database that separates storage and compute, with auto scaling, branching, and bottomless storage.
Nikita is also a Partner at Khosla Ventures, where he is incubating Neon and raised $104M to date. He is passionate about deep tech, data infrastructure, and system software. Prior to Neon, Nikita co-founded MemSQL (now SingleStore), a unicorn data and analytics company valued at over $1.3 billion. He served as a founding CTO, and then CEO, successfully scaling the company to over $40 million in ARR. Prior to SingleStore, he worked as a senior engineer at Facebook and at Microsoft on SQL Server. Nikita earned a Ph.D. in computer science from the National Research University in St. Petersburg, Russia.
In this episode, Nikita recounts the founding stories behind both MemSQL and Neon, and elaborates on the key trends driving database technologies today, from serverless and generative AI, to open data and the convergence of transactional and analytical workloads.
-----------
Key Quotes
Amplitude and Mixpanel, they basically are a time series database underneath with the UI. Time series data tends to be, you know, ‘write once’, most of it. And so, you need to take advantage of those techniques that data warehouses are basically born with, right? They are in the business of storing data relatively cheaply. And every enterprise, unless it's not like an archaic enterprise, should have a data warehouse. So it makes only too much sense to put this into a data warehouse rather than either a custom database, you know, like a platform like Datadog, Mixpanel, Amplitude. Plus you have additional benefits from it because you can cross reference that data with the rest of the business data." - Nikita Shamgunov
-----------
Episode Timestamps
(01:41) Founding stories behind MemSQL and Neon
(03:39) Addressing new challenges for databases
(09:20) Criteria for evaluating databases
(12:36) HTAP and zero ETL between transactional and analytical applications
(19:07) Evolving standards around table formats
(24:07) Thoughts on Generative AI and LLM-native in the data warehouse
(26:38) Warehouse centric approaches to data storage
(29:45) Open source for data warehouses
(33:54) Potential for new applications to be built around real time applications
(38:10) Managing large volumes of data
(40:59) Serverless Postgres is as easy as Stripe
(45:40) Takeaways
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Links
Previous Episode
Enterprise Data & AI Strategies with Awinash Sinha, CIO at Zoom and Bask Iyer, Former CIO at VMware, Dell, Juniper Networks, and Honeywell & Advisor at Zoom
This episode of The Analytics Edge, sponsored by NetSpring, features an interview with Awinash Sinha, CIO at Zoom, and Bask Iyer, former CIO at VMware, Dell, Juniper Networks, and Honeywell & Advisor at Zoom, an all-in-one intelligent collaboration platform that makes connecting easier, more immersive, and more dynamic for businesses and individuals.
Responsible for the company’s information technology, data science and analytics, and business application organizations, Awinash brings to Zoom over 20 years of experience in enabling business outcomes and scaling fast-growing companies. He has completed an executive program in Business Administration and Management from Stanford University Graduate School of Business.
Having served as an advisor and mentor to Zoom since 2016, Bask is a renowned technology executive, leading enterprise-wide transformations in digital systems at numerous companies in Silicon Valley. He is the CEO of BaskMind.com, an experienced team offering hands-on digital transformation and operations excellence services to traditional companies and advisory services to high growth technology companies. Iyer holds a Master’s degree in Computer Science from Florida Institute of Technology.
In this exclusive episode, Awinash and Bask dive deep into the impact of AI at Zoom, and provide advice for data leaders on generative AI, how to enhance the customer experience in product-led companies, and the importance of hiring “outcome enablers."
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Key Quotes
"In subscription business you have to really closely follow the life cycle of customers. And it's a infinity loop, right? Even existing customers, you may be looking at feature adoptions, you may be looking at churn, you may be looking at how we can create more value, make them aware about new products and offerings coming together. And internally, for the sales department, marketing department, or customer support department, providing the insight, both coming from product. Basically with this cloud architecture we have an ability to look at product telemetry data. As well as business transaction data and intersecting them. The magic is, the real insights are when we intersect these two data, join these two data and do a cohort analysis. Cohort analysis at product level, at segment level, particularly for large enterprise companies our size and bigger, will have a customer segmentation, will also will have some flavor, some in geodes dimensions or other dimensions. Once we connect the data around product and then customer lifecycle from business transactions, it could be slice and dice from many different perspective, and that's where the insights come." - Awinash Sinha
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Episode Timestamps
(01:39) Approaches for CIO’s and Data leaders to consider regarding AR and VR
(03:31) Zoom’s strategy for AR and VR collaboration
(06:29) AI-powered innovations improving user experience on Zoom
(07:49) Zoom IQ explained
(11:25) AI's role in addressing COVID-driven challenges for engagement in online education and business interaction
(16:59) Advice to C-level executives on generative AI strategy
(22:05) Approach of starting initiatives despite data quality concerns
(27:29) Future of the "data scientist" title amid AI techniques and evolving roles
(30:54) Warehouse-centric data approach and its impact on data leaders
(31:57) Advantages of data warehousing being on cloud
(37:03) Strategies for product-led analytics leaders to boost customer experience
(39:52) Value of information and time on data
(42:38) Consumer-centric approach and its impact on business
(45:05) Call to action for data leaders
(51:58) Takeaways
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Links
Next Episode
Warehouse-Native Data Architecture with Soumyadeb Mitra, Founder and CEO at RudderStack
This episode of The Analytics Edge, sponsored by NetSpring, features an interview with Soumyadeb Mitra, Founder and CEO at RudderStack, the leading warehouse Customer Data Platform that’s purpose-built for data teams. RudderStack is an open-source, enterprise-ready platform for collecting, storing, and routing customer event data to your data warehouse and dozens of other tools.
After founding the company in 2019, Soumyadeb led RudderStack to 100+ employees and a $56 million Series B funding round in 2022. Prior to RudderStack, he co-founded Mariana, a VC-funded B2B martech startup, which was later acquired by 8x8 in 2018. Soumyadeb earned his PhD in Computer Science from the University of Illinois Urbana-Champaign.
In this episode, Soumyadeb talks about the founding stories behind RudderStack, the evolution of Customer Data Platforms, and the significant impact that a warehouse-centric CDP approach has on business.
Key Quotes
“We want to look at product funnels and customer journeys, but then combine that with Salesforce data, right? I mean, I want to look at funnels separately for enterprise customers and customers who closed versus customers where we are competing with a specific vendor and so on. And this is a very standard thing I would imagine. I mean, we see that across all our companies, but it was surprisingly hard to do with a lot of these cloud product analytics tools, right? They're amazing tools, but then they're only designed to ingest a specific kind of data. And if you want to combine other data sources, it becomes really fragile and complicated to set up those data pipelines, right? So yeah, I think Warehouse-native enables that and kind of unlocks that set of use cases. Plus there are all these challenges around data privacy, which again, it's not so much for a company like us, but at scale, it becomes a problem, right? I mean, you're centralizing your data in a data warehouse. Why do you need to ship everything to another vendor to do specific parts of your analytics? It just does not make good sense..” - Soumyadeb Mitra
Episode Timestamps
(01:11) Founding story behind RudderStack
(02:50) The evolution of CDP
(06:50) Business challenges CDPs are trying to solve
(08:06) Packaged vs. composable debate
(10:55) Benefits of warehouse-native CDP
(17:14) Analytics on customer data
(18:47) Data activation and reverse ETL
(21:10) Real-time personalization
(26:05) Achieving customer 360 view
(28:08) Business impact with a warehouse-centric CDP approach
(30:17) The future of CDPs
(34:48) Takeaways
Links
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