DBMS > Badger vs. EXASOL vs. GigaSpaces vs. Snowflake

System Properties Comparison Badger vs. EXASOL vs. GigaSpaces vs. Snowflake

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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.

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

High performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented Transactions

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

Key-value store

Relational DBMS

Document store
Object oriented DBMS

Values are user defined objects

Relational DBMS

Secondary database models

Graph DBMS
Search engine

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Trend Chart

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

Score	2.25
Rank	#120	Overall
	#57	Relational DBMS

Score	1.02
Rank	#192	Overall
	#32	Document stores
	#6	Object oriented DBMS

Score	123.20
Rank	#9	Overall
	#6	Relational DBMS

Website

github.com/dgraph-io/badger

www.exasol.com

www.gigaspaces.com

www.snowflake.com

Technical documentation

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

www.exasol.com/resources

docs.gigaspaces.com/latest/landing.html

docs.snowflake.net/manuals/index.html

Developer

DGraph Labs

Exasol

Gigaspaces Technologies

Snowflake Computing Inc.

Initial release

2017

2000

2014

Current release

15.5, September 2020

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

Apache Version 2; Commercial licenses 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.

Implementation language

Java, C++, .Net

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux
macOS
Solaris
Windows

hosted

Data scheme

schema-free

yes

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

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.

XML can be used for describing objects metadata

yes

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-99 for query and DML statements

yes

APIs and other access methods

.Net
JDBC
ODBC
WebSocket

GigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data

CLI Client
JDBC
ODBC

Supported programming languages

Java
Lua
Python
R

.Net
C++
Java
Python
Scala

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

yes

user defined functions

Triggers

yes

yes, event driven architecture

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

yes

Map-Reduce pattern can be built with XAP task executors

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

Immediate Consistency

Foreign keys

Referential integrity

yes

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

Access rights for users, groups and roles according to SQL-standard

Role-based access control

Users with fine-grained authorization concept, user roles and pluggable authentication

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