SummaryDatabases are the core of most applications, whether transactional or analytical. In recent years the selection of database products has exploded, making the critical decision of which engine(s) to use even more difficult. In this episode Tanya Bragin shares her experiences as a product manager for two major vendors and the lessons that she has learned about how teams should approach the process of tool selection.AnnouncementsHello and welcome to the Data Engineering Podcast, the show about modern data managementIntroducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack (https://www.dataengineeringpodcast.com/rudderstack)You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it’s real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize (https://www.dataengineeringpodcast.com/materialize) today to get 2 weeks free!This episode is brought to you by Datafold – a testing automation platform for data engineers that finds data quality issues before the code and data are deployed to production. Datafold leverages data-diffing to compare production and development environments and column-level lineage to show you the exact impact of every code change on data, metrics, and BI tools, keeping your team productive and stakeholders happy. Datafold integrates with dbt, the modern data stack, and seamlessly plugs in your data CI for team-wide and automated testing. If you are migrating to a modern data stack, Datafold can also help you automate data and code validation to speed up the migration. Learn more about Datafold by visiting dataengineeringpodcast.com/datafold (https://www.dataengineeringpodcast.com/datafold)Data projects are notoriously complex. With multiple stakeholders to manage across varying backgrounds and toolchains even simple reports can become unwieldy to maintain. Miro is your single pane of glass where everyone can discover, track, and collaborate on your organization's data. I especially like the ability to combine your technical diagrams with data documentation and dependency mapping, allowing your data engineers and data consumers to communicate seamlessly about your projects. Find simplicity in your most complex projects with Miro. Your first three Miro boards are free when you sign up today at dataengineeringpodcast.com/miro (https://www.dataengineeringpodcast.com/miro). That’s three free boards at dataengineeringpodcast.com/miro (https://www.dataengineeringpodcast.com/miro).Your host is Tobias Macey and today I'm interviewing Tanya Bragin about her views on the database products marketInterviewIntroductionHow did you get involved in the area of data management?What are the aspects of the database market that keep you interested as a VP of product?How have your experiences at Elastic informed your current work at Clickhouse?What are the main product categories for databases today?What are the industry trends that have the most impact on the development and growth of different product categories?Which categories do you see growing the fastest?When a team is selecting a database technology for a given task, what are the types of questions that they should be asking?Transactional engines like Postgres, SQL Server, Oracle, etc. were long used as analytical databases as well. What is driving the broad adoption of columnar stores as a separate environment from transactional systems?What are the inefficiencies/complexities that this introduces?How can the database engine used for analytical systems work more closely with the transactional systems?When building analytical systems there are numerous moving parts with intricate dependencies. What is the role of the database in simplifying observability of these applications?What are the most interesting, innovative, or unexpected ways that you have seen Clickhouse used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on database products?What are your prodictions for the future of the database market?Contact InfoLinkedIn (https://www.linkedin.com/in/tbragin/)Parting QuestionFrom your perspective, what is the biggest gap in the tooling or technology for data management today?Closing AnnouncementsThank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning.Visit the site (https://www.dataengineeringpodcast.com) 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 [email protected] (mailto:[email protected])) with your story.To help other people find the show please leave a review on Apple Podcasts (https://podcasts.apple.com/us/podcast/data-engineering-podcast/id1193040557) and tell your friends and co-workersLinksClickhouse (https://clickhouse.com/)Podcast Episode (https://www.dataengineeringpodcast.com/clickhouse-data-warehouse-episode-88/)Elastic (https://www.elastic.co/)OLAP (https://en.wikipedia.org/wiki/Online_analytical_processing)OLTP (https://en.wikipedia.org/wiki/Online_transaction_processing)Graph Database (https://en.wikipedia.org/wiki/Graph_database)Vector Database (https://en.wikipedia.org/wiki/Vector_database)Trino (https://trino.io/)Presto (https://prestodb.io/)Foreign data wrapper (https://wiki.postgresql.org/wiki/Foreign_data_wrappers)dbt (https://www.getdbt.com/)Podcast Episode (https://www.dataengineeringpodcast.com/dbt-data-analytics-episode-81/)OpenTelemetry (https://opentelemetry.io/)Iceberg (https://iceberg.apache.org/)Podcast Episode (https://www.dataengineeringpodcast.com/tabular-iceberg-lakehouse-tables-episode-363)Parquet (https://parquet.apache.org/)The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/)