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Data

Columnar Database

Real time ingestion, distributed SQL, and fast analytics on massive datasets.

CrateDB is a high performance columnar database built for real time analytics on large and fast moving datasets. Its column oriented storage engine accelerates scans, filtering, and aggregations, enabling instant insights across billions of records. With distributed SQL, automatic indexing, and support for all data types, CrateDB delivers the performance of a modern analytical database with the flexibility of a multi model platform.

Built for analytical speed

Column oriented storage organizes data by column, which significantly reduces the amount of data scanned and allows analytics to run at high speed. CrateDB uses a highly optimized columnar format that provides:

  • Fast aggregations for dashboards and reports
  • Low latency scans across large tables
  • High compression rates to reduce storage costs
  • Efficient filtering on selective columns
  • Consistent performance for high concurrency workloads
This makes CrateDB ideal for real time analytical applications that combine fresh data with large historical datasets.

Real-time ingestion and instant queryability

CrateDB is more than a traditional columnar database. It is designed for real time workloads:

  • High throughput ingestion from sensors, logs, events, and applications
  • Query readiness within milliseconds
  • Distributed execution for parallel query processing
  • Predictable performance as ingestion rates increase
CrateDB handles both streaming and analytical workloads without batch delays or pipeline complexity.

Distributed columnar execution

CrateDB pairs columnar storage with a shared nothing, distributed SQL architecture. Queries execute in parallel across the cluster and merge results automatically.

The benefits include:

  • Linear scale for compute and storage
  • High availability through replication
  • Automatic sharding and rebalancing
  • Consistent performance as datasets grow
  • Support for high concurrency analytical workloads
This combination is ideal for organizations that need to scale horizontally across large data volumes.
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How it fits in your architecture

CrateDB replaces complex analytical pipelines with a single columnar SQL engine.
It supports ingestion, storage, and analytics in one platform, eliminating the need for multiple systems such as:

  • OLAP databases
  • Search engines
  • NoSQL document stores
  • Hadoop or Spark based batch pipelines
Teams gain real time analytical capability with a simplified data architecture.
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How it fits in your architecture

CrateDB replaces heavy batch oriented big data stacks with a real time analytics engine.
It runs ingestion, storage, search, and analytics on one platform, simplifying architectures that previously required multiple systems such as:

  • Hadoop and HDFS storage
  • Spark for processing
  • Elasticsearch for search
  • NoSQL stores for flexible schemas
  • OLAP engines for aggregation
CrateDB brings these capabilities together in one modern SQL based system.
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Learn more about CrateDB

CrateDB Architecture Guide

This comprehensive guide covers all the key concepts you need to know about CrateDB's architecture. It will help you gain a deeper understanding of what makes it performant, scalable, flexible and easy to use. Armed with this knowledge, you will be better equipped to make informed decisions about when to leverage CrateDB for your data projects. 

Download the white paper

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FAQ

A columnar database stores data by column instead of by row. This format improves analytical performance because queries only read the columns needed for filtering and aggregations. CrateDB uses an optimized column oriented storage engine to deliver fast analytics on large and wide datasets.

Traditional columnar databases often focus on batch analytics. CrateDB combines columnar storage with real time ingestion, automatic indexing, and distributed SQL. This allows teams to run fast analytical queries on fresh data without waiting for batch processing or ETL jobs.

Yes. CrateDB supports high throughput ingestion and makes new data query ready immediately. At the same time, its columnar storage format is optimized for scanning and analyzing large historical datasets, allowing you to combine live and long term data in the same query.

CrateDB handles time series data, JSON documents, text attributes, geospatial shapes, vector embeddings, and relational data. All of these data types benefit from the efficiency of CrateDB’s column oriented storage and distributed SQL execution.

CrateDB uses a shared nothing, distributed architecture. Data is sharded automatically across nodes, and queries are executed in parallel. This allows the cluster to scale linearly as data volumes and query workloads grow, maintaining low latency even at large scale.

No. CrateDB indexes data automatically as it is ingested. This removes the need for manual index configuration and ensures that analytical queries perform well without constant optimization.

Yes. CrateDB provides full text search, vector search, geospatial functions, and analytical SQL in one engine. You can combine these capabilities in the same query without moving data to specialized systems.

CrateDB is designed for high throughput ingestion. Data is written quickly to disk and indexed with minimal delay, so new records become queryable almost immediately, even when columnar storage is used.

Yes. CrateDB runs as a fully managed cloud service, as a self-managed installation on your own infrastructure, or at the edge. All deployment models support the same columnar capabilities.

Columnar storage in CrateDB is ideal for time series analytics, IoT platforms, industrial monitoring, operational dashboards, fleet and mobility data, and AI applications that require fast scans and aggregations on large datasets.