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Compare CrateDB

How is CrateDB different from other databases in the market?


There is no one-size-fits-all when it comes to databases: the ideal database will be determined by your use case and dataset. The characteristics of CrateDB make it optimal for real-time use-cases with high data volumes, data variety, and heavy load.

Analytics/AI/ML use-case
Comparisons

CrateDB
icon-cratedb-comparison-1-high-data-volume High data volume Comparisons icon-cratedb-comparison-1-unlimited-scalability Unlimited Scalability
icon-cratedb-comparison-2-different-data-types
Different types of data
Comparisons icon-cratedb-comparison-2-versatile-data-model Versatile data model
icon-cratedb-comparison-concurrent-users Concurrent users Comparisons icon-cratedb-comparison-3-high-concurrency High concurrency performance
icon-cratedb-comparison-4-realtime-response
Real-time response
Comparisons icon-cratedb-comparison-4-dynamic-architecture
Dynamic architecture
icon-cratedb-comparison-5-frequent-changes
Frequent changes
Comparisons icon-cratedb-comparison-5-easy-integration Easy integration
icon-cratedb-comparison-6-efficiency-priority
Efficiency as priority
Comparisons icon-cratedb-comparison-6-cost-efficiency
Cost efficiency
icon-cratedb-comparison-7-need-versatility
Need for versatility
Comparisons icon-cratedb-comparison-7-cloud
Run in any cloud, EDGE and on premises
icon-cratedb-comparison-1-high-data-volumeHigh data volume Comparisons icon-cratedb-comparison-1-unlimited-scalability-1Unlimited Scalability
icon-cratedb-comparison-2-different-data-typesDifferent types of data
Comparisons icon-cratedb-comparison-2-versatile-data-modelVersatile data model
icon-cratedb-comparison-concurrent-usersConcurrent users Comparisons icon-cratedb-comparison-3-high-concurrencyHigh concurrency performance
icon-cratedb-comparison-4-realtime-response-1Real-time response
Comparisons
icon-cratedb-comparison-4-dynamic-architectureDynamic architecture
icon-cratedb-comparison-5-frequent-changes-2Frequent changes
Comparisons icon-cratedb-comparison-5-easy-integrationEasy integration
icon-cratedb-comparison-6-efficiency-priorityEfficiency as priority
Comparisons
icon-cratedb-comparison-6-cost-efficiency-2Cost efficiency
icon-cratedb-comparison-7-need-versatility-2Need for versatility
Comparisons
icon-cratedb-comparison-7-cloudRun in any cloud, EDGE and on premises

CrateDB is neither a relational database, nor a NoSQL database, nor a time-series database; instead, it offers the best of these worlds. CrateDB is a distributed database built on a NoSQL foundation, but fully accessible through SQL.

CrateDB is able to work with large datasets without losing accuracy, keeping things simple and efficient. And due to its real-time features, indexing, and schemas, CrateDB is ideal for handling huge amounts of time-series data.

  Traditional RDBMS NoSQL Time-series databases CrateDB
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Simple
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CrateDB VS
Traditional RDBMS

Traditional relational databases can be an excellent choice for a use case without huge data volumes or high scalability needs. But they are insufficient for large data volume applications demanding horizontal scalability and the real-time processing of massive datasets.

  Traditional RDBMS CrateDB
Accessibility ANSI SQL ANSI SQL
Core architecture Monolithic or master/slave Shared-nothing nodes
Data model Rigid schemas of tabular data Dynamic schema, structured & unstructured data
Consistency model ACID Eventually consistent
Scalability model Vertical scaling Horizontal scalability
Partitioning Manual Built-in
Full-text search Not built-in Built-in (Lucene indexing)
Microservices architecture Compatible Ideal

WHITE PAPER


CrateDB vs PostgreSQL

In a benchmark querying 312 million rows of sensor readings, CrateDB provided 33x better price-performance than PostgreSQL.

CrateDB vs NoSQL

NoSQL databases like MongoDB are not the best choice if you are dealing with huge volumes of time-series data that need to be queried in real-time. NoSQL databases can get very expensive for large data volume use cases at scale, consuming unnecessarily large memory and storage capacity. At the same time, you must abandon the power and convenience of SQL.

WHITE PAPER


Comparing databases for an Industrial IoT use-case

In this blogpost we talk about our experience as developers working with MongoDB, TimescaleDB and InfluxDB, discussing the pros and cons of every database for large-scale IoT.

CrateDB vs time-series databases

Time series databases like InfluxDB or TimescaleDB are unable to handle many real-time applications by themselves—if they require metadata operations, for example. They often need to work in combination with an RDBMS, duplicating maintenance efforts and costs. Besides, their performance is usually optimized for single-node processes.

CONTINUE READING


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BLOG

Digital transformation in the factory floor

How are manufacturers addressing innovation in their factories?

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WHITE PAPER

Time-series data in manufacturing

Discover the particularities of industrial time series

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BLOG

Amazon Timestream is finally released: read our first impressions

We tested its performance against an industrial-grade IoT workload

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WHITE PAPER

The cost of running MongoDB, TimescaleDB and CrateDB for industrial IoT

Find out how much the monthly price can vary if different databases are used

Try CrateDB Cloud

The easier way to get a taste of CrateDB! Launch a cluster in just a few steps. Start now with a 30-day free trial

Check our GitHub

See what our engineers are up to. We welcome contributions, feedback and discussions.

Download CrateDB

CrateDB is the leading open-source, distributed SQL database for relational and time-series data. Download it for free.

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