DBMS > Badger vs. Bangdb vs. Databricks vs. Microsoft Azure Synapse Analytics

System Properties Comparison Badger vs. Bangdb vs. Databricks vs. Microsoft Azure Synapse Analytics

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

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

Converged and high performance database for device data, events, time series, document and graph

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.

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

Primary database model

Key-value store

Document store
Graph DBMS
Time Series DBMS

Document store
Relational DBMS

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	0.20
Rank	#325	Overall
	#49	Key-value stores

Score	0.15
Rank	#334	Overall
	#45	Document stores
	#31	Graph DBMS
	#31	Time Series DBMS

Score	76.33
Rank	#17	Overall
	#3	Document stores
	#11	Relational DBMS

Score	22.71
Rank	#30	Overall
	#18	Relational DBMS

Website

github.com/dgraph-io/badger

bangdb.com

www.databricks.com

azure.microsoft.com/services/synapse-analytics

Technical documentation

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

docs.bangdb.com

docs.databricks.com

docs.microsoft.com/azure/synapse-analytics

Developer

DGraph Labs

Sachin Sinha, BangDB

Databricks

Microsoft

Initial release

2017

2012

2013

2016

Current release

BangDB 2.0, October 2021

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

BSD 3

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.

Implementation language

C, C++

C++

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux

hosted

Data scheme

schema-free

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

yes

Typing

predefined data types such as float or date

yes: string, long, double, int, geospatial, stream, events

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.

yes

Secondary indexes

yes

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

SQL like support with command line tool

with Databricks SQL

yes

APIs and other access methods

Proprietary protocol
RESTful HTTP API

JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC

Supported programming languages

C
C#
C++
Java
Python

Python
R
Scala

C#
Java
PHP

Server-side scripts

Stored procedures

user defined functions and aggregates

Transact SQL

Triggers

yes, Notifications (with Streaming only)

Partitioning methods

Methods for storing different data on different nodes

none

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

Sharding, horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

selectable replication factor, Knob for CAP (enterprise version only)

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Tunable consistency, set CAP knob accordingly

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

Concurrency

Support for concurrent manipulation of data

yes

yes, optimistic concurrency control

yes

Durability

Support for making data persistent

yes

yes, implements WAL (Write ahead log) as well

yes

In-memory capabilities

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

yes, run db with in-memory only mode

User concepts

Access control

yes (enterprise version only)

yes

More information provided by the system vendor

Specific characteristics

Supported database models : In addition to the Document store and Relational DBMS...
» more

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

DB-Engines blog posts

PostgreSQL is the DBMS of the Year 2023
2 January 2024, Matthias Gelbmann, Paul Andlinger

show all

Recent citations in the news

Databricks unveils AI platform at first Data Intelligence Day in PH
22 April 2024, Backend News

Data architecture with Databricks Across different cloud | by Sushreeta | Apr, 2024
22 April 2024, Medium

Databricks CEO Says Competition Spurred High-Profile Exit at Snowflake Bloomberg
27 March 2024, Yahoo Finance

Databricks spent $10M on new DBRX generative AI model
27 March 2024, TechCrunch

SambaNova announces new AI Samba-CoE v0.2 that already beats Databricks DBRX
28 March 2024, VentureBeat

provided by Google News

Migrate Microsoft Azure Synapse Analytics to Amazon Redshift using AWS SCT | Amazon Web Services
18 October 2023, AWS Blog

Azure Synapse vs. Databricks: Data Platform Comparison 2024
26 March 2024, eWeek

Azure Synapse Analytics: Everything you need to know about Microsoft's cloud analytics platform
24 September 2023, DataScientest

Azure Synapse Link for Cosmos DB: New Analytics Capabilities
10 November 2023, InfoQ.com

Azure Synapse Analytics vs Snowflake: ETL Tool Comparison
25 July 2023, TechRepublic

provided by Google News

Share this page