DBMS > Axibase vs. Badger vs. Databricks vs. GridGain

System Properties Comparison Axibase vs. Badger vs. Databricks vs. GridGain

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

Scalable TimeSeries DBMS based on HBase with integrated rule engine and visualization

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

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.

GridGain is an in-memory computing platform, built on Apache Ignite

Primary database model

Time Series DBMS

Key-value store

Document store
Relational DBMS

Key-value store
Relational DBMS

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

Score	0.29
Rank	#292	Overall
	#25	Time Series DBMS

Score	0.14
Rank	#331	Overall
	#49	Key-value stores

Score	78.61
Rank	#15	Overall
	#2	Document stores
	#10	Relational DBMS

Score	1.47
Rank	#154	Overall
	#26	Key-value stores
	#72	Relational DBMS

Website

axibase.com/docs/atsd/finance

github.com/dgraph-io/badger

www.databricks.com

www.gridgain.com

Technical documentation

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

docs.databricks.com

www.gridgain.com/docs/index.html

Developer

Axibase Corporation

DGraph Labs

Databricks

GridGain Systems, Inc.

Initial release

2013

2017

2013

2007

Current release

15585

GridGain 8.5.1

License

Commercial or Open Source

commercial

Community Edition (single node) is free, Enterprise Edition (distributed) is paid

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.

Implementation language

Java

Java, C++, .Net

Server operating systems

Linux

BSD
Linux
OS X
Solaris
Windows

hosted

Linux
OS X
Solaris
Windows

Data scheme

yes

schema-free

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

yes

Typing

predefined data types such as float or date

yes

short, integer, long, float, double, decimal, string

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

SQL

Support of SQL

SQL-like query language

with Databricks SQL

ANSI-99 for query and DML statements, subset of DDL

APIs and other access methods

JDBC
Proprietary protocol (Network API)
RESTful HTTP API

JDBC
ODBC
RESTful HTTP API

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

Supported programming languages

Go
Java
PHP
Python
R
Ruby

Python
R
Scala

C#
C++
Java
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

yes

user defined functions and aggregates

yes (compute grid and cache interceptors can be used instead)

Triggers

yes

yes (cache interceptors and events)

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

yes

yes (replicated cache)

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

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

Security Hooks for custom implementations

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

The Ultimate ATV Test: Suzuki's King Quad 750 AXI Rugged Package vs. Alaska's Hunting Season
14 October 2020, Outdoor Life

provided by Google News

This Is the Platform Nancy Pelosi Used to Make Her Private Investment in Databricks
9 May 2024, Yahoo Finance

Databricks Announces Major Updates to Its AI Suite to Boost AI Model Accuracy
10 May 2024, EnterpriseAI

Databricks Enhances Enterprise AI with RAG Applications and Improved Model Serving
9 May 2024, Datanami

Nvidia, Databricks Sued in Latest AI Copyright Class Actions
3 May 2024, Bloomberg Law

Databricks adds vector search, new LLM support to AI suite
8 May 2024, TechTarget

provided by Google News

GridGain in-memory data and generative AI – Blocks and Files
10 May 2024, Blocks & Files

GridGain to Sponsor and Speak at Three Key Industry Events in May 2024
6 May 2024, Martechcube

GridGain's 2023 Growth Positions Company for Strong 2024
25 January 2024, Datanami

GridGain Announces Call for Speakers for Virtual Apache Ignite Summit 2024
8 February 2024, PR Newswire

GridGain Named in the 2023 Gartner® Market Guide for Event Stream Processing
22 August 2023, GlobeNewswire

provided by Google News

Share this page