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How ClickHouse Uses Google Cloud

9 engineering articles about Google Cloud from ClickHouse's engineering team

Articles

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ClickHouse
Advanced
This article details how ClickPy, a free Python download statistics platform powered by ClickHouse, scaled to over 2 trillion rows by replacing its legacy cron-based ingestion pipeline with ClickPi...
8 min read
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ClickHouse
Beginner
The article discusses the potential of lakehouses using open table formats like Apache Iceberg and Delta Lake for observability, highlighting their advantages in scalability, cost-effectiveness, an...
Melvyn Peignon & Dale McDiarmid
24 min read
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ClickHouse
Intermediate
The article announces the launch of Ruby gem analytics powered by ClickHouse, enabling Ruby developers to analyze gem download data since 2017.
The ClickHouse & Ruby Central teams
21 min read
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ClickHouse
Intermediate
The article details a complex debugging journey faced by ClickHouse engineers as they investigated a mysterious CPU spike in their cloud infrastructure on GCP.
Sergei Trifonov
37 min read
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ClickHouse
Intermediate
This article discusses the implementation of a Medallion architecture for Bluesky data using ClickHouse, focusing on the challenges of handling high-volume JSON event streams.
PME Team
30 min read
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ClickHouse
Beginner
The article discusses how to enhance Google Analytics data using ClickHouse, focusing on the limitations of GA4 and the advantages of ClickHouse for flexible, fast analytics with infinite data rete...
Dale McDiarmid
21 min read
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ClickHouse
Beginner
This article compares ClickHouse and Snowflake for real-time analytics, highlighting their architectural similarities and differences, performance benchmarks, and migration strategies.
The ClickHouse Team
25 min read
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Beginner
This article discusses the integration of ClickHouse with Kafka Connect and Confluent Cloud to facilitate real-time event streaming, particularly for Ethereum Cryptocurrency events.
Dale McDiarmid
20 min read
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ClickHouse
Intermediate
This article explores the integration of ClickHouse and BigQuery for real-time analytics, highlighting their complementary strengths.
Dale McDiarmid
28 min read
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