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Data modeling guide for real-time analytics with ClickHouse

This article was written as part of my services Querying billions of weather records and getting results in under 200 milliseconds isn’t theory; it’s what real-time analytics solutions provide. Processing streaming IoT data from thousands of sensors while delivering real-time dashboards with no lag is what certain business domains need. That’s what you’ll learn at the end of this guide through building a ClickHouse-modeled analytics use case. You’ll learn how to land data in ClickHouse that is

Data Modeling Guide for Real-Time Analytics with ClickHouse

This article was written as part of my services Querying billions of weather records and getting results in under 200 milliseconds isn’t theory; it’s what real-time analytics solutions provide. Processing streaming IoT data from thousands of sensors while delivering real-time dashboards with no lag is what certain business domains need. That’s what you’ll learn at the end of this guide through building a ClickHouse-modeled analytics use case. You’ll learn how to land data in ClickHouse that is

Data engineering and software engineering are converging

TL;DR: · If you’re an engineer building realtime analytics or AI-powered features, you need the right data infrastructure coupled with the right developer experience (DX). · A great DX for data infrastructure should empower both software devs and data engineers, while taking inspiration from the best of modern web development (git-native, local-first, everything as code, CI/CD friendly, etc). · MooseStack by 514 offers a fully open source implementation of a DX layer for ClickHouse. Da

How to ingest 1B rows/s in ClickHouse

After seeing the engineers at Tesla talk about 1B row/s ClickHouse ingestion, I wanted to see if I could do it myself. A few weeks ago, I saw a talk from Tesla claiming they were ingesting 1B rows per second using ClickHouse. I'm a petrolhead but I don't have any reason to think they are lying :). One (American) billion rows per second might feel like a lot, so let me try to explain how you can achieve that using ClickHouse. I'm not sure what ClickHouse flavor Tesla uses, but I don't think that

ClickHouse matches PG for single-row UPDATEs and 4000 x faster for bulk UPDATEs

TL;DR · On identical hardware and data, ClickHouse matches PostgreSQL for single-row UPDATEs and is up to 4,000× faster in our tests for bulk UPDATEs. · Why it matters: Bulk updates are common in OLTP workloads, and ClickHouse’s columnar design + parallelism make them far faster. · Caveat: PostgreSQL is fully transactional by default; ClickHouse isn’t. Results compare each engine’s native execution model, not identical transaction guarantees. PostgreSQL is the most popular open-source

Does OLAP Need an ORM

TL;DR · ORMs have proven to be useful for many developers in the OLTP/transactional stack (Postgres, MySQL, etc). · OLAP/analytical databases like ClickHouse could potentially benefit from ORM abstractions. · Existing transactional ORMs probably shouldn’t be extended to OLAP due to fundamental differences in semantic meaning between OLTP and OLAP. · Moose OLAP (part of MooseStack) is an open source, MIT-licensed implementation of an ORM-like interface for ClickHouse, inspired by tran

Load Test GlassFlow for ClickHouse: Real-Time Dedup at Scale

Load Test GlassFlow for ClickHouse: Real-Time Deduplication at Scale By Ashish Bagri, Co-founder & CTO of GlassFlow TL;DR We tested GlassFlow on a real-world deduplication pipeline with Kafka and ClickHouse. It handled 55,00 records/sec published by Kafka and processed 9,000+ records/sec on a MacBook Pro, with sub-0.12ms latency. No crashes, no message loss, no disordering. Even with 20M records and 12 concurrent publishers, it remained robust. Want to try it yourself? The full test setup

Scaling our observability platform by embracing wide events and replacing OTel

TLDR # Observability at scale: Our internal system grew from 19 PiB to 100 PB of uncompressed logs and from ~40 trillion to 500 trillion rows. Efficiency breakthrough: We absorbed a 20× surge in event volume using under 10% of the CPU previously needed. OTel pitfalls: The required parsing and marshalling of events in OpenTelemetry proved a bottleneck and didn’t scale - our custom pipeline addressed this. Introducing HyperDX: ClickHouse-native observability UI for seamless exploration, correlatio

ClickHouse scales beyond 100 petabytes of logs

TLDR # Observability at scale: Our internal system grew from 19 PiB to 100 PB of uncompressed logs and from ~40 trillion to 500 trillion rows. Efficiency breakthrough: We absorbed a 20× surge in event volume using under 10% of the CPU previously needed. OTel pitfalls: The required parsing and marshalling of events in OpenTelemetry proved a bottleneck and didn’t scale - our custom pipeline addressed this. Introducing HyperDX: ClickHouse-native observability UI for seamless exploration, correlatio