DBMS > CrateDB vs. Databricks vs. MySQL vs. SQream DB

System Properties Comparison CrateDB vs. Databricks vs. MySQL vs. SQream DB

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Editorial information provided by DB-Engines

Name

Description

Distributed Database based on Lucene

The Databricks Lakehouse Platform combines elements of data lakes and data warehouses to provide a unified view onto structured and unstructured data. It is based on Apache Spark.

Widely used open source RDBMS

a GPU-based, columnar RDBMS for big data analytics workloads

Primary database model

Document store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS

Document store
Relational DBMS

Relational DBMS

Key/Value like access via memcached API

Relational DBMS

Secondary database models

Relational DBMS

Document store
Spatial DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	0.71
Rank	#227	Overall
	#37	Document stores
	#5	Spatial DBMS
	#16	Search engines
	#19	Time Series DBMS
	#8	Vector DBMS

Score	81.08
Rank	#15	Overall
	#2	Document stores
	#10	Relational DBMS

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Score	0.74
Rank	#224	Overall
	#103	Relational DBMS

Website

Technical documentation

Developer

Crate

Databricks

Oracle

since 2010, originally MySQL AB, then Sun

SQream Technologies

Initial release

2013

1995

2017

Current release

8.4.0, April 2024

2022.1.6, December 2022

License

Commercial or Open Source

Open Source

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

CrateDB Cloud: a distributed SQL database that spreads data and processing across an elastic cluster of shared nothing nodes. CrateDB Cloud enables data insights at scale on Microsoft Azure, AWS and Google Cloud Platform.

Aiven for MySQL: Fully managed MySQL, deployable in the cloud of your choice, with seamless integrations and lightning-fast setup.

Implementation language

Java

C and C++

C++, CUDA, Haskell, Java, Scala

Server operating systems

All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator support

hosted

FreeBSD
Linux
OS X
Solaris
Windows

Linux

Data scheme

Flexible Schema (defined schema, partial schema, schema free)

yes

Typing

predefined data types such as float or date

yes

yes, ANSI Standard SQL Types

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

yes

Secondary indexes

yes

SQL

Support of SQL

yes, but no triggers and constraints, and PostgreSQL compatibility

with Databricks SQL

yes

with proprietary extensions

yes

APIs and other access methods

ADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API

JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

.Net
JDBC
ODBC

Supported programming languages

.NET
Erlang
Go

community maintained client
Java
JavaScript (Node.js)

community maintained client
Perl

community maintained client
PHP
Python
R
Ruby

community maintained client
Scala

community maintained client

Python
R
Scala

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions (Javascript)

user defined functions and aggregates

yes

proprietary syntax

user defined functions in Python

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

horizontal and vertical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Configurable replication on table/partition-level

yes

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Read-after-write consistency on record level

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

unique row identifiers can be used for implementing an optimistic concurrency control strategy

ACID

not for MyISAM storage engine

ACID

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

yes

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

rights management via user accounts

Users with fine-grained authorization concept

no user groups or roles

More information provided by the system vendor

Specific characteristics

The enterprise database for time series, documents, and vectors. Distributed - Native...
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Supported database models : In addition to the Document store and Relational DBMS...
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Competitive advantages

Response time in milliseconds: e ven for complex ad-hoc queries. Massive scaling...
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Typical application scenarios

IoT: accelerate your IIoT projects with CrateDB, delivering real-time analytics...
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Key customers

Across all continents, CrateDB is used by companies of all sizes to meet the most...
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Market metrics

The CrateDB open source project was started in 2013 Honorable Mention in 2021 Gartner®...
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Licensing and pricing models

See CrateDB pricing >
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