DBMS > Badger vs. ClickHouse vs. Microsoft Azure Synapse Analytics vs. Microsoft Azure Table Storage

System Properties Comparison Badger vs. ClickHouse vs. Microsoft Azure Synapse Analytics vs. Microsoft Azure Table Storage

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Editorial information provided by DB-Engines

Name

Description

An embeddable, persistent, simple and fast Key-Value Store, written purely in Go.

A high-performance, column-oriented SQL DBMS for online analytical processing (OLAP) that uses all available system resources to their full potential to process each analytical query as fast as possible. It is available as both an open-source software and a cloud offering.

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

A Wide Column Store for rapid development using massive semi-structured datasets

Primary database model

Key-value store

Relational DBMS

Wide column store

Secondary database models

Time Series DBMS

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

Score	0.22
Rank	#320	Overall
	#47	Key-value stores

Score	15.55
Rank	#38	Overall
	#23	Relational DBMS

Score	19.93
Rank	#31	Overall
	#19	Relational DBMS

Score	4.04
Rank	#77	Overall
	#6	Wide column stores

Website

github.com/dgraph-io/badger

clickhouse.com

azure.microsoft.com/services/synapse-analytics

azure.microsoft.com/en-us/services/storage/tables

Technical documentation

godoc.org/github.com/dgraph-io/badger

clickhouse.com/docs

docs.microsoft.com/azure/synapse-analytics

Developer

DGraph Labs

Clickhouse Inc.

Microsoft

Initial release

2017

2016

2012

Current release

v24.4.1.2088-stable, May 2024

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache 2.0

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.

Aiven for Clickhouse: Managed cloud data warehousing with high-speed analytics.
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DoubleCloud: Fully managed ClickHouse alongside best-in-class managed open-source services to build analytics at scale.

Implementation language

C++

Server operating systems

BSD
Linux
OS X
Solaris
Windows

FreeBSD
Linux
macOS

hosted

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

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.

Secondary indexes

yes

SQL

Support of SQL

Close to ANSI SQL (SQL/JSON + extensions)

yes

APIs and other access methods

gRPC
HTTP REST
JDBC
MySQL wire protocol
ODBC
PostgreSQL wire protocol
Proprietary protocol

ADO.NET
JDBC
ODBC

RESTful HTTP API

Supported programming languages

3rd party library
C++
Elixir

3rd party library
Go

3rd party library
Java

3rd party library
JavaScript (Node.js)

3rd party library
Kotlin

3rd party library
Nim

3rd party library
Perl

3rd party library
PHP

3rd party library
Python

3rd party library
R

3rd party library
Ruby

3rd party library
Rust
Scala

3rd party library

C#
Java
PHP

.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

Server-side scripts

Stored procedures

yes

Transact SQL

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

key based and custom

Sharding, horizontal partitioning

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

none

Asynchronous and synchronous physical replication; geographically distributed replicas; support for object storages.

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Foreign keys

Referential integrity

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

Transaction concepts

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

ACID

optimistic locking

Concurrency

Support for concurrent manipulation of data

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

Access rights for users and roles. Column and row based policies. Quotas and resource limits. Pluggable authentication with LDAP and Kerberos. Password based, X.509 certificate, and SSH key authentication.

yes

Access rights based on private key authentication or shared access signatures

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