DBMS > Google Cloud Bigtable vs. HBase vs. Memcached vs. SQLite vs. WakandaDB

System Properties Comparison Google Cloud Bigtable vs. HBase vs. Memcached vs. SQLite vs. WakandaDB

Editorial information provided by DB-Engines

Name

Description

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

Wide-column store based on Apache Hadoop and on concepts of BigTable

In-memory key-value store, originally intended for caching

Widely used embeddable, in-process RDBMS

WakandaDB is embedded in a server that provides a REST API and a server-side javascript engine to access data

Primary database model

Key-value store
Wide column store

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

Score	3.26
Rank	#92	Overall
	#13	Key-value stores
	#8	Wide column stores

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

Score	19.42
Rank	#32	Overall
	#4	Key-value stores

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Score	0.03
Rank	#364	Overall
	#17	Object oriented DBMS

Website

cloud.google.com/bigtable

Technical documentation

cloud.google.com/bigtable/docs

hbase.apache.org/book.html

github.com/memcached/memcached/wiki

www.sqlite.org/docs.html

wakanda.github.io/doc

Developer

Google

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Danga Interactive

originally developed by Brad Fitzpatrick for LiveJournal

Dwayne Richard Hipp

Wakanda SAS

Initial release

2015

2008

2003

2000

2012

Current release

2.3.4, January 2021

1.6.25, March 2024

3.45.3 (15 April 2024), April 2024

2.7.0 (April 29, 2019), April 2019

License

Commercial or Open Source

commercial

Open Source

Apache version 2

Open Source

BSD license

Open Source

Public Domain

Open Source

AGPLv3, extended commercial license available

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++, JavaScript

Server operating systems

hosted

Linux
Unix
Windows

using Cygwin

FreeBSD
Linux
OS X
Unix
Windows

server-less

Linux
OS X
Windows

Data scheme

schema-free

schema-free, schema definition possible

schema-free

yes

dynamic column types

yes

Typing

predefined data types such as float or date

options to bring your own types, AVRO

yes

not rigid because of 'dynamic typing' concept.

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.

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-92 is not fully supported

APIs and other access methods

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

Java API
RESTful HTTP API
Thrift

Proprietary protocol

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

RESTful HTTP API

Supported programming languages

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

C
C#
C++
Groovy
Java
PHP
Python
Scala

.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
Perl
PHP
Python
Ruby

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

JavaScript

Server-side scripts

Stored procedures

yes

Coprocessors in Java

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

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

Multi-source replication
Source-replica replication

none

Repcached, a Memcached patch, provides this functionallity

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

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

Immediate Consistency or Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic single-row operations

Single row ACID (across millions of columns)

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

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 based on Google Cloud Identity and Access Management (IAM)

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

yes

using SASL (Simple Authentication and Security Layer) protocol

yes

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.