DBMS > Badger vs. Databend vs. Derby vs. Google Cloud Bigtable

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

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.

An open-source, elastic, and workload-aware cloud data warehouse designed to meet businesses' massive-scale analytics needs at low cost and with low complexity

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.

Primary database model

Key-value store

Relational DBMS

Key-value 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	0.34
Rank	#283	Overall
	#130	Relational DBMS

Score	4.60
Rank	#70	Overall
	#38	Relational DBMS

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

Website

github.com/dgraph-io/badger

github.com/datafuselabs/databend
www.databend.com

db.apache.org/derby

cloud.google.com/bigtable

Technical documentation

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

docs.databend.com

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

cloud.google.com/bigtable/docs

Developer

DGraph Labs

Databend Labs

Apache Software Foundation

Google

Initial release

2017

2021

1997

2015

Current release

1.0.59, April 2023

10.17.1.0, November 2023

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache Version 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

Rust

Java

Server operating systems

BSD
Linux
OS X
Solaris
Windows

hosted
Linux
macOS

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

CLI Client
JDBC
RESTful HTTP API

JDBC

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

Supported programming languages

Go
Java
JavaScript (Node.js)
Python
Rust

Java

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

Server-side scripts

Stored procedures

Java Stored Procedures

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

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

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)

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

ACID

Atomic single-row operations

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

Users with fine-grained authorization concept, user roles

fine grained access rights according to SQL-standard

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

More information provided by the system vendor

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

Recent citations in the news

Data Bending: Creating Unique Digital Visual Effects
23 April 2020, RedShark News

Rust and the OS, the Web, Database and Other Languages
21 November 2022, The New Stack

Â£1.1 Million in AddisonMckee Tube Bending Technologies Provides Dinex with Outstanding OEM Credentials
24 May 2007, news.thomasnet.com

provided by Google News

JDBC tutorial: Easy installation and setup with Apache Derby
20 December 2019, TheServerSide.com

Installing Apache Hive 3.1.2 on Windows 10 | by Hadi Fadlallah
3 May 2020, Towards Data Science

The Arrival of Java 20
21 March 2023, blogs.oracle.com

The Ultimate Open Source Database List Profiling 16 Software Tools
30 May 2019, Solutions Review

The Apache® Software Foundation Announces 18 Years of Open Source Leadership
28 March 2017, GlobeNewswire

provided by Google News

Google Introduces Autoscaling for Cloud Bigtable for Optimizing Costs
31 January 2022, InfoQ.com

Google scales up Cloud Bigtable NoSQL database
27 January 2022, TechTarget

Review: Google Bigtable scales with ease
7 September 2016, InfoWorld

Google Cloud makes it cheaper to run smaller workloads on Bigtable
7 April 2020, TechCrunch

Google introduces Cloud Bigtable managed NoSQL database to process data at scale
6 May 2015, VentureBeat

provided by Google News

Share this page