ClickHouse Archives | ChistaDATA Inc.

ClickHouse Query Execution Explained: 4 Proven Stages From INSERT to Distributed JOIN

ChistaDATA · ClickHouse query execution, stage by stage · September 2026 ClickHouse query execution has no magic in it. A statement is parsed and planned, parts are scanned in parallel, partial results are merged, and […]

Advanced ClickHouse Troubleshooting on 26.8 LTS: 9 Proven Techniques for Slow Queries, Stuck Merges and Memory Errors

ChistaDATA Inc.

The ClickHouse troubleshooting method ChistaDATA uses on 26.8 LTS and 26.9: nine techniques, the system tables behind each, the queries we run, and one disk-full incident from our own lab.

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ClickHouse 26.8 LTS feature map: cost-based optimizer, parallel GROUP BY, Parquet lazy materialisation, streaming HTTP API and background queries for real-time analytics

ClickHouse 26.8 LTS: The Advanced Features That Change Real-Time Analytics Performance

ChistaDATA Inc.

ChistaDATA · ClickHouse release engineering · September 2026 ClickHouse 26.8 LTS: The Advanced Features That Change Real-Time Analytics Performance ClickHouse 26.8 LTS shipped on 10 September 2026 with 98 new features, 128 performance optimisations and […]

ClickHouse workshop for CTOs and data architects: three-day agenda covering architecture and fit, measured cost, and migration and people decisions

Real-Time Analytics ClickHouse Workshop for CTOs and Data Architects

ChistaDATA Inc.

A three-day ClickHouse workshop for CTOs and data architects from ChistaDATA University: architecture fit, measured cost, rollup design and migration cutover, with real lab output.

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ChistaDATA Performance

ClickHouse Performance Audit: 8 Best Tips for 26.8 LTS

ChistaDATA Inc.

ClickHouse performance audit tips for 26.8 LTS with real lab output: rank query shapes in system.query_log, measure cold past the query condition cache, read EXPLAIN ANALYZE and EXPLAIN WHATIF, and audit codecs per block.

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Real-Time Payments Analytics on ClickHouse: 6 Proven Layers for Southeast Asia

ChistaDATA Inc.

A reference model for real-time payments analytics on ClickHouse with the Apache stack (Kafka, Flink, Iceberg, Spark, Airflow, Superset) for a high-volume Southeast Asian mobile payment platform: schema, ingestion, serving, residency and operations.

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ClickHouse Performance Settings in 26.8 LTS: 14 Proven Changes

ChistaDATA Inc.

The ClickHouse performance settings that changed between 26.3 LTS and 26.8 LTS: what each default does to ingest, aggregation, joins and distributed execution, how to measure it, and the rollout method we use.

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ClickHouse Troubleshooting Techniques: 8 Proven Drills We Teach

ChistaDATA Inc.

The ClickHouse troubleshooting techniques taught in CHT-400 at ChistaDATA University: a scope, prove, fix method and the diagnostic queries for eight production incidents, from too many parts to distributed timeouts.

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ClickHouse certification and competency ladder at ChistaDATA University: CHT-100 to CHT-300 core levels, CHT-400 and CHT-500 specialist programmes, CHT-900 executive track, and the three certification tiers

ClickHouse Certification at ChistaDATA University: 3 Proven Tiers

ChistaDATA Inc.

How ChistaDATA University builds ClickHouse certification on a competency ladder: CHT-100 to CHT-900 programmes, real lab code, failure drills, a live-cluster capstone defence, corporate and academic delivery, and how to start.

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Real-Time Analytics on ClickHouse 26.8: 6 Proven Levers

ChistaDATA Inc.

Real-time analytics on ClickHouse 26.8: the ingest-to-dashboard latency budget, async-insert freshness, top-K and cache serving, lightweight UPDATE, and ingest/serving isolation on self-managed clusters versus ClickHouse Cloud.

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* Everything changes over time – Our blogs/posts and comments changes over time, That’s how it should be! Whatever we comment from ChistaDATA Inc. Teams (including Shiv Iyer) and other stakeholders or guest bloggers posted here are never permanent, These things worked for us. But, there is no guarantee they will work for you too, When using the recommendations from ChistaDATA or MinervaDB or MinervaSQL or any other online resources / Google, You must test the advice before applying them to your production systems, and always invest for a robust Database DR solution, Thank you for understanding.

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