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Instaclustr Blog
Category: ClickHouse

How to use ClickHouse system.query_log for query observability and performance monitoring

ClickHouse is renowned for its high-performance analytical capabilities, not only excelling at speed but also providing remarkable...

How to query Apache Iceberg tables from ClickHouse

ClickHouse can query an Apache Iceberg table sitting in S3 in a fraction of a second - no Spark job, with no&hellip...

Query Amazon FSx for NetApp ONTAP Data from Managed ClickHouse

NetApp Instaclustr for ClickHouse can query data stored in Amazon FSx for NetApp ONTAP through ONTAP's S3-compatible interface. ...

Instaclustr product update: June 2026

Here’s a roundup of the latest features and updates that we’ve recently released. If you have any particular feature requests or ...

What are Asynchronous Data Inserts in ClickHouse®

Asynchronous data inserts in ClickHouse are a server-side batching mechanism that buffers incoming data in memory before writing ...

Kafka® to Iceberg: Build a queryable data lake with ClickHouse®

That's ClickHouse below querying an Iceberg table on S3 within 0.31 seconds to read metadata and return the first rows. No Spark&...

Apache Iceberg explained: A better table format for modern data lakes

Data lakes had a reputation problem. The promise was compelling: dump all your data into cheap object storage—S3, GCS, Azure Blob—...

Kafka Sink Connectors explained: Bridging your data pipeline

A "sink connector" sounds simple—until you realize different systems use the term from different perspectives. In Apache Kafka ...

Powering low-latency analytics with ClickHouse® and Amazon FSx

Analytics platforms are commonly constrained by a familiar trade‑off: file‑based storage delivers the performance and semantics ...