A Multipurpose Database For Transactions And Analytics To Simplify Your Data Architecture With Singlestore
Data Engineering Podcast
00:00
Is Store the Best Data Base Engine?
We are committed to building seamless elasticity, consumption oriented, seamles elasticity. The value adition is much more than just scaling or sort of trans habilitis. So things are changing fast as you continue to iterate on the product and try to grow option. We're announcing a couple of features pretty soon. Yu, i talked about wasson, which is basically running the web simly, run time, natively. Inside the engine, we are doing work spaces, which a basically compute into compute isolation,. meaning you can bring in different kinds of computes on the same data.
Transcript
Play full episode
Transcript
Episode notes
Summary
A large fraction of data engineering work involves moving data from one storage location to another in order to support different access and query patterns. Singlestore aims to cut down on the number of database engines that you need to run so that you can reduce the amount of copying that is required. By supporting fast, in-memory row-based queries and columnar on-disk representation, it lets your transactional and analytical workloads run in the same database. In this episode SVP of engineering Shireesh Thota describes the impact on your overall system architecture that Singlestore can have and the benefits of using a cloud-native database engine for your next application.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
- When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!
- Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription
- So now your modern data stack is set up. How is everyone going to find the data they need, and understand it? Select Star is a data discovery platform that automatically analyzes & documents your data. For every table in Select Star, you can find out where the data originated, which dashboards are built on top of it, who’s using it in the company, and how they’re using it, all the way down to the SQL queries. Best of all, it’s simple to set up, and easy for both engineering and operations teams to use. With Select Star’s data catalog, a single source of truth for your data is built in minutes, even across thousands of datasets. Try it out for free and double the length of your free trial today at dataengineeringpodcast.com/selectstar. You’ll also get a swag package when you continue on a paid plan.
- Data teams are increasingly under pressure to deliver. According to a recent survey by Ascend.io, 95% in fact reported being at or over capacity. With 72% of data experts reporting demands on their team going up faster than they can hire, it’s no surprise they are increasingly turning to automation. In fact, while only 3.5% report having current investments in automation, 85% of data teams plan on investing in automation in the next 12 months. 85%!!! That’s where our friends at Ascend.io come in. The Ascend Data Automation Cloud provides a unified platform for data ingestion, transformation, orchestration, and observability. Ascend users love its declarative pipelines, powerful SDK, elegant UI, and extensible plug-in architecture, as well as its support for Python, SQL, Scala, and Java. Ascend automates workloads on Snowflake, Databricks, BigQuery, and open source Spark, and can be deployed in AWS, Azure, or GCP. Go to dataengineeringpodcast.com/ascend and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $5,000 when you become a customer.
- Your host is Tobias Macey and today I’m interviewing Shireesh Thota about Singlestore (formerly MemSQL), the industry’s first modern relational database for multi-cloud, hybrid and on-premises workloads
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what SingleStore is and the story behind it?
- The database market has gotten very crouded, with different areas of specialization and nuance being the differentiating factors. What are the core sets of workloads that SingleStore is aimed at addressing?
- What are some of the capabilities that it offers to reduce the need to incorporate multiple data stores for application and analytical architectures?
- What are some of the most valuable lessons that you learned in your time at MicroSoft that are applicable to SingleStore’s product focus and direction?
- Nikita Shamgunov joined the show in October of 2018 to talk about what was then MemSQL. What are the notable changes in the engine and business that have occurred in the intervening time?
- What are the macroscopic trends in data management and application development that are having the most impact on product direction?
- For engineering teams that are already invested in, or considering adoption of, the "modern data stack" paradigm, where does SingleStore fit in that architecture?
- What are the services or tools that might be replaced by an installation of SingleStore?
- What are the efficiencies or new capabilities that an engineering team might expect by adopting SingleStore?
- What are some of the features that are underappreciated/overlooked which you would like to call attention to?
- What are the most interesting, innovative, or unexpected ways that you have seen SingleStore used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on SingleStore?
- When is SingleStore the wrong choice?
- What do you have planned for the future of SingleStore?
Contact Info
- @ShireeshThota on Twitter
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Closing Announcements
- Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
- Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
- If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com) with your story.
- To help other people find the show please leave a review on iTunes and tell your friends and co-workers
Links
- MemSQL Interview With Nikita Shamgunov
- Singlestore
- MS SQL Server
- Azure Cosmos DB
- CitusDB
- Debezium
- PostgreSQL
- MySQL
- HTAP == Hybrid Transactional-Analytical Processing
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
Remember Everything You Learn from Podcasts
Save insights instantly, chat with episodes, and build lasting knowledge - all powered by AI.
