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Data

Big Data Database

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

CrateDB is a high performance big data database that delivers real time analytics across large volumes of structured and semi structured data. It combines distributed SQL, automatic indexing, and columnar storage to ingest data at scale and query it instantly, even as workloads grow into billions of records.

With CrateDB, teams can unify ingestion, storage, and analytics in one engine and move from raw data to live insights without batch delays or complex pipelines.

Built for high volume big data workloads

Big data workloads require high throughput ingestion, fast analytics, and scalable storage. CrateDB provides:

CrateDB handles the classic big data challenges of volume, velocity, and variety in a single database.

Distributed SQL for big data analytics

CrateDB uses a shared nothing, distributed SQL architecture that automatically spreads data across nodes for parallel processing. This allows complex queries to run quickly, even across massive datasets.

  • Parallel execution: Queries run across the cluster and aggregate results automatically.
  • Real time ingestion with instant availability: New data becomes query ready within milliseconds.
  • Designed for high concurrency: Run dashboards, analytical queries, and AI feature pipelines at the same time without degrading performance.
  • Scales horizontally: Add nodes as data grows. CrateDB manages sharding, rebalancing, and failover behind the scenes.

Columnar storage for fast analytics

Large analytical queries perform best on columnar formats. CrateDB uses columnar storage to deliver:

  • Fast scans across large tables
  • Efficient aggregations and filtering
  • Better compression for reduced storage cost
  • Faster analytical workloads on historical data
Users can analyze billions of records with low latency.
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Handles all data types in one engine

Modern big data is multi-model. CrateDB supports:

  • Time series metrics
  • JSON payloads
  • Geospatial shapes
  • Text data
  • Vector embedding
  • Relational attributes
  • Binary objects
All can be queried together using SQL. This removes the need for multiple specialized databases and reduces operational complexity.
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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 big data database is designed to handle large volumes of fast moving data from many sources while delivering high performance analytics. It must support high throughput ingestion, distributed storage, parallel query execution, and real time insights across structured and semi structured data. CrateDB delivers all of these capabilities in one engine with SQL simplicity.

Traditional big data stacks rely on batch processing and heavy ETL pipelines. CrateDB provides real time ingestion, automatic indexing, and distributed SQL, allowing teams to run analytics immediately without MapReduce or Spark jobs. This reduces complexity while improving performance and time to insight.

Yes. CrateDB ingests data at high speed and makes it query ready within milliseconds. It also stores large volumes of historical data efficiently using columnar storage and distributed execution, making it ideal for both live dashboards and long term analytical workloads.

CrateDB supports time series metrics, JSON documents, logs, events, geospatial shapes, vector embeddings, text attributes, and relational data. This multi-model approach allows teams to combine different data types in a single query and eliminate multiple specialized systems.

CrateDB scales horizontally by adding nodes to the cluster. Sharding, replication, failover, and rebalancing are handled automatically. This keeps performance predictable as the dataset grows from millions to billions of records and beyond.

No. CrateDB automatically indexes incoming data and distributes storage and processing across the cluster. You do not need to manage index strategies, partitioning schemes, or query tuning for standard workloads.

Yes. CrateDB consolidates search, analytics, time series storage, and vector operations into one engine. This removes the need to combine separate systems for ingestion, warehousing, search, and machine learning features.

CrateDB executes distributed queries across all nodes in parallel. Aggregations, filters, and search operations run quickly even on terabytes of data, and performance remains consistent as data volume grows.

Yes. CrateDB supports vector data types and vector search in the same engine that handles relational, JSON, and time series data. You can store large embeddings, run similarity search, and feed models with real time features.

Yes. CrateDB runs as a fully managed cloud service, as a self managed installation on your own infrastructure, or at the edge in industrial or remote environments. All deployment models support big data ingest and analytics.