DBMS > Badger vs. EXASOL vs. Google Cloud Datastore vs. ITTIA

System Properties Comparison Badger vs. EXASOL vs. Google Cloud Datastore vs. ITTIA

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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.

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

Edge Time Series DBMS with Real-Time Stream Processing

Primary database model

Secondary database models

Relational DBMS

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

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

Score	1.76
Rank	#139	Overall
	#62	Relational DBMS

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	0.44
Rank	#267	Overall
	#23	Time Series DBMS

Website

github.com/dgraph-io/badger

www.exasol.com

cloud.google.com/datastore

www.ittia.com

Technical documentation

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

www.exasol.com/resources

cloud.google.com/datastore/docs

Developer

DGraph Labs

Exasol

Google

ITTIA L.L.C.

Initial release

2017

2000

2008

2007

Current release

8.7

License

Commercial or Open Source

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

C and C++

Server operating systems

BSD
Linux
OS X
Solaris
Windows

hosted

Linux
Windows

Data scheme

schema-free

yes

schema-free

Fixed schema

Typing

predefined data types such as float or date

yes

yes, details here

Float/Blob/Integer/String/Unicode/Date/Time/Timestamp/Interval

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

yes

SQL-like query language (GQL)

yes

APIs and other access methods

.Net
JDBC
ODBC
WebSocket

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

JDBC
ODBC
RESTful HTTP API
WebSocket

Supported programming languages

Java
Lua
Python
R

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

C
C++
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

using Google App Engine

yes

Triggers

yes

Callbacks using the Google Apps Engine

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication using Paxos

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

yes

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

Database file passwords

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