DBMS > Badger vs. Derby vs. Google Cloud Bigtable vs. Microsoft Azure Table Storage

System Properties Comparison Badger vs. Derby vs. Google Cloud Bigtable 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.

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

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

Primary database model

Key-value store

Relational DBMS

Key-value store
Wide column store

Wide column store

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

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

Score	4.60
Rank	#70	Overall
	#38	Relational DBMS

Score	3.15
Rank	#95	Overall
	#14	Key-value stores
	#8	Wide column stores

Score	4.04
Rank	#77	Overall
	#6	Wide column stores

Website

github.com/dgraph-io/badger

db.apache.org/derby

cloud.google.com/bigtable

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

Technical documentation

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

db.apache.org/derby/manuals/index.html

cloud.google.com/bigtable/docs

Developer

DGraph Labs

Apache Software Foundation

Google

Microsoft

Initial release

2017

1997

2015

2012

Current release

10.17.1.0, November 2023

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache version 2

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

Server operating systems

BSD
Linux
OS X
Solaris
Windows

All OS with a Java VM

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

APIs and other access methods

JDBC

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

RESTful HTTP API

Supported programming languages

Java

C#
C++
Go
Java
JavaScript (Node.js)
Python

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

Server-side scripts

Stored procedures

Java Stored Procedures

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

Internal replication in Colossus, and regional replication between two clusters in different zones

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

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Atomic single-row operations

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

fine grained access rights according to SQL-standard

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

Access rights based on private key authentication or shared access signatures

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