What is ClickHouse and how does OLAP differ from OLTP?

In the episode, we sat down with ClickHouse Co-Founder Yury Izrailevsky to unpack how one of the fastest open-source databases in the world became the analytics engine of choice for 2,000 customers including Harvey, Canva, HP, and Supabase. From its Yandex origins to powering AI observability, Yury shares how ClickHouse balances open-source roots, cloud innovation, and a remote-first culture moving at breakneck speed.

ClickHouse's Series C valued the company at $6.35B earlier this year, and just yesterday they announced an extension to that round, just months after it was raised.

In this episode, we dig into:

• Origins & Founding Story

  • ClickHouse began as an internal project at Yandex to power a Google Analytics–style platform, focused on performance and scale.

  • Open-sourced in 2016 - rapid global adoption laid the foundation for ClickHouse the company.

  • Yury first discovered ClickHouse while at Google; impressed by its speed, he later co-founded the company in 2021 alongside Aaron Katz (ex-Elastic) and the original creator Alexey Milovidov.

• Why ClickHouse Stands Out

  • Column-oriented, open source OLAP database designed for massive-scale analytical processing.

  • Excels in performance, efficiency, and cost - ideal for large data volumes and real-time analytics (and now AI workloads).

  • Architectural choices:

    • Columnar storage = better compression and faster execution.

    • Separation of compute and storage enables elasticity, scalability, and resilience in the cloud.

• Open Source vs. Cloud

  • Open-source version offers freedom and flexibility.

  • Cloud product delivers much lower total cost of ownership and fully managed experience.

  • Architectural parity between the two ensuring no vendor lock-in for customers.

  • Customers can run the same queries on both; most stay with cloud due to simplicity and cost efficiency.

• Use Cases & Ecosystem

  • 4 main use cases:

    1. Real-time analytics

    2. Data Warehousing

    3. Observability

    4. AI / ML Workloads

• Company Building & Culture

  • Fully remote from day one.

  • Prioritized experienced, self-sufficient engineers over early-career hires.

  • Built and launched GA version in less than a year - insane pace of innovation.

• Innovation & Community

  • Monthly release cadence.

  • Hundreds of integrations and connectors.

  • Strong open-source and commercial community

• Advice for Founders

  • Focus on what matters most

  • Hire mature, independent thinkers.

  • Move fast but maintain quality; ClickHouse Cloud achieved production-grade quality in record time.