Akshay Nanavati and Poom Chiarawongse, Members of Technical Staff at OpenAI, share how the OpenAI observability team scaled ClickHouse as ChatGPT adoption, and log volume, skyrocketed.

Session recording from Open House, the ClickHouse user conference.

[https://clickhouse.com/openhouse](https://clickhouse.com/openhouse)

Scaling ClickHouse to petabytes of logs at OpenAI

Maruth Goyal, Member of Technical Staff at Anthropic, shares how ClickHouse is used to improve observability.

Session recording from Open House 2025, the ClickHouse user conference.

[https://clickhouse.com/openhouse](https://clickhouse.com/openhouse)

How ClickHouse helps Anthropic scale observability

Salim Syed, VP, Head of Engineering and Syd Mehmood, Director of Software Engineering at Capital One Slingshot share how they cut infrastructure costs by 50% and reduced average dashboard load time from 5+ to under 500ms with ClickHouse Cloud.

Session recording from Open House roadshow NYC 2025.

https://clickhouse.com/openhouse/nyc

Engineering leaders at Capital One share how they cut infrastructure costs by 50% and reduced average dashboard load time from 5+ to under 500ms with ClickHouse Cloud.

How Capital One cut infrastructure costs by 50%

Alon Tal, Senior Staff Software Engineer at Tesla, talks about Comet, Tesla's internal system built with ClickHouse for ingesting, storing and querying metrics at massive scale.

Session recording from Open House, the ClickHouse user conference.

[https://clickhouse.com/openhouse](https://clickhouse.com/openhouse)

Tesla-scale metrics with ClickHouse

Using ClickHouse as a GitHub webhook

Presented by Ottavio Hartman- Platform Team Lead @ Moment at our New York Meetup. 

Come join us at one of our amazing meetup groups, this presentation is from our New York meetup on January 27th:
https://www.meetup.com/clickhouse-new-york-user-group/

#programming #clickhouse #database

Don't forget to give us a ⭐ on Github! https://github.com/clickhouse/clickhouse

Presented by Ottavio Hartman- Platform Team Lead @ Moment at our New York Meetup. 


Clickhouse Meetups NYC Moment

Presented by  Ryan Adami @ Gliss.sh at our New York Meetup. 

Come join us at one of our amazing meetup groups, this presentation is from our New York meetup on January 27th:
https://www.meetup.com/clickhouse-new-york-user-group/

#programming #clickhouse #database

Don't forget to give us a ⭐ on Github! https://github.com/clickhouse/clickhouse

Presented by  Ryan Adami @ Gliss.sh at our New York Meetup. 

Clickhouse Meetups NYC Gliss.sh

Presented by Paul Noble Tech Lead Audiences @ Clay at our New York Meetup. 

Come join us at one of our amazing meetup groups, this presentation is from our New York meetup on January 27th:
https://www.meetup.com/clickhouse-new-york-user-group/

#programming #clickhouse #database

Don't forget to give us a ⭐ on Github! https://github.com/clickhouse/clickhouse

Presented by Paul Noble Tech Lead Audiences @ Clay at our New York Meetup. 


Clickhouse Meetups NYC Clay

This talk explores how to turn database changes into real-time analytics without the complexity. ClickPipes, ClickHouse Cloud's managed ingestion platform makes CDC pipelines straightforward, reliable, and cost-effective.

Topics include:

- Stream real-time changes from Postgres, MySQL, or MongoDB directly into ClickHouse in just a few clicks.
- Eliminate the overhead of managing external tools like Debezium or Fivetran.
- Accelerate analytics on your operational data without complex migrations or custom scripts.

Marta Paes, Senior Product Manager at ClickHouse

CDC ClickPipes: The Fastest Way to Replicate Your Database to ClickHouse

This talk was delivered live at the ClickHouse Melbourne meetup on 24 February at State of Grace, by Nik Cubrliovic, Software Developer & Data Engineer @ nineprimes.

The full agenda can be found at https://www.meetup.com/clickhouse-mel...

Talk Title: Open Electricity: Lessons learned migrating from Postgres and TimescaleDB to ClickHouse

Talk Description: As energy data grows in volume and velocity, traditional relational and time-series systems can start to show their limits. In this talk delivered lived at the ClickHouse Melbourne meetup, Nik shared their journey migrating Open Electricity from Postgres and TimescaleDB to ClickHouse. We’ll cover the technical challenges they faced, key architectural decisions, performance benchmarks, cost considerations, and the unexpected lessons learned along the way.

Slides can be accessed from https://github.com/ClickHouse/clickhouse-presentations/blob/master/2026-meetup-melbourne-1/Talk%202%20-%20Melbourne%20Meetup%20-%20Open%20Electricity.pdf


Nik Cubrliovic, Software Developer & Data Engineer at nineprimes

Open Electricity: Lessons learned migrating from Postgres and TimescaleDB to ClickHouse

A walk through of Managed ClickStack end to end: what it is, why we built it, and how it changes the economics of observability.

Managed ClickStack: Live Demo

**Interviewer:** Zoe Steinkamp, Developer Advocate, ClickHouse 
<br />
**Guest:** Haroen Vermylen, Co-Founder & CTO, Luzmo

---

**What does Luzmo do?**

Luzmo is an embedded analytics platform. We offer reports, data visualization, conversational analytics, and dashboards that our clients — typically software companies — can embed inside their products to give their customers insights into all the data that's been captured on them.

**Can you give an example of a customer?**

We have clients across a wide variety of sectors. A typical SaaS use case is Selegens, a marketing analytics platform powering ROI dashboards and campaign impact reports inside their Marketing Cloud. An entirely different sector is Proximus, the largest Belgian telecom provider, who use Luzmo to visualize location intelligence data — anonymized movement data inside Belgium — and monetize it. All the user interfaces and reports are built on Luzmo.

**Where does ClickHouse fit in?**

We first discovered ClickHouse in 2017 — it's been eight years now, and ClickHouse has been quite infectious inside our company. We started with it as our internal warehouse for storing data that customers share with us. We're a federated platform — not all client data lives on our platform, but some clients want us to be responsible for storing it. We want to make it fast for them, which is why we settled on ClickHouse.

**When did you move from open source to Cloud?**

Quite quickly after it became generally available — 2022, I believe.

**What made you switch?**

Running clusters isn't our core business. We're in the business of building analytics and reporting, so that was a task we wanted to get rid of. ClickHouse Cloud seemed like the right offering to make that happen.

**Did Cloud enable wider adoption internally?**

Definitely. We're now also storing our query logs and plugin logs on ClickHouse — more of an observability use case. Within our platform, our software customers can review what queries are happening, identify slow queries, and trace which data source on their side is causing the issue. At which point we advise them to switch to ClickHouse.

**Most exciting features in the past year or two?**

I'm generally quite impressed with ClickHouse's speed of execution and interest in non-obvious use cases. Things like ANOVA tables available out of the box — I've never seen that in another database. More recently, we've started using ClickHouse as a vector database for storing embeddings of metadata and data. It's already there, it makes sense, and the implementation is quite good.

**What made ClickHouse stand out when you first evaluated it?**

We evaluated a number of players at the time, but ClickHouse was doing things differently. The big talk back then was distributed systems — you can handle big workloads but you need 100 servers, which isn't cost-effective and adds a lot of complexity. ClickHouse came out and it worked on my laptop. That was pretty amazing. The raw performance increase it gave for most of our workloads was the reason to keep following it.

**Any challenges on Cloud?**

A couple of gotchas, some of which have already been solved. Because we don't know our clients' data until we receive it, we have tens of thousands of different tables, and ClickHouse can sometimes struggle with that workload. We've also built our own query engine to efficiently optimize queries — ordering them correctly, pulling data from other systems in the best way. We can automatically group incoming queries and consolidate them into single operations, reducing pressure on ClickHouse. The new JSON data type is also very interesting — we're not using it to maximum effect yet, but we're watching it closely.

**Three words to describe ClickHouse?**

Extremely fast, extremely versatile, extremely cost-efficient.


"Extremely Fast, Extremely Versatile, Extremely Cost-Efficient" — Luzmo's 8-Year ClickHouse Journey

Parsing dates and datetimes is one of those things you'll do constantly in ClickHouse, and there are actually quite a few different approaches depending on your data format. In this tutorial, we walk through five practical methods for handling everything from Unix timestamps to
 messy string formats that you might get from APIs or legacy systems.

  * Converting Unix timestamps (seconds, milliseconds, and microseconds)
  * Parsing numeric date formats from legacy databases and mainframes
  * Handling datetime strings with known formats using MySQL and JODA syntax
  * Using "best effort" parsing for inconsistent or unknown date formats
  * Configuring session-level settings for automatic type casting can we make this to htnml

Parsing dates and datetimes in ClickHouse

Comparing cloud data warehouse costs is harder than it looks. Different platforms meter, scale, and bill compute in fundamentally different ways, which makes price lists misleading. Instead of arguing about list prices or pricing tiers, this webinar shows that efficiency is the real currency.

Tom Schreiber and Lio Palacin break down how Snowflake, Databricks, ClickHouse Cloud, BigQuery, and Redshift charge for compute, then share benchmark results at 1B, 10B, and 100B rows using our reproducible Bench2Cost method to translate runtimes into real dollars. 

This presentation offers a clear way to evaluate cost and performance as your data scales.

Breakdown of how the 5 major data warehouses charge for compute and benchmarked results at 1B, 10B, and 100B rows.

The True Cost of Speed: What Query Performance Really Costs at Scale

Arrow Flight is a high-performance RPC framework built on gRPC that's specifically designed for efficiently transferring large Arrow datasets between services. In this hands-on tutorial, we explore how to use ClickHouse as an Arrow Flight server, demonstrating both bulk data retrieval and streaming approaches. We'll work with a real dataset of over 30 million UK property records to show you the practical performance characteristics and capabilities you can expect.

The video walks through three different approaches to querying data via Arrow Flight. First, we look at a simple bulk query that retrieves all 30 million rows in one go, which takes around 7-9 seconds. Then we explore streaming the data in batches, which improves performance to about 4.7 million rows per second and completes in roughly 6.5 seconds. Finally, we demonstrate how to compute aggregates on-the-fly while streaming, calculating running statistics like average and maximum property prices as the data flows through.

Key Learning Points:
* Setting up ClickHouse as an Arrow Flight server with basic configuration
* Connecting to ClickHouse using the Arrow Flight client protocol
* Bulk data retrieval vs. streaming approaches and their performance trade-offs
* Working with Arrow Flight metadata and endpoints for distributed queries
* Computing real-time aggregates during streaming without significant performance impact
* Practical performance benchmarks with a 30+ million row dataset

Videos