DBMS > Amazon Redshift vs. etcd vs. Google Cloud Bigtable vs. Hawkular Metrics vs. SAP SQL Anywhere

System Properties Comparison Amazon Redshift vs. etcd vs. Google Cloud Bigtable vs. Hawkular Metrics vs. SAP SQL Anywhere

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

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

Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.

RDBMS database and synchronization technologies for server, desktop, remote office, and mobile environments

Primary database model

Relational DBMS

Key-value store

Key-value store
Wide column store

Time Series DBMS

Relational DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	7.25
Rank	#54	Overall
	#5	Key-value stores

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

Score	0.00
Rank	#379	Overall
	#40	Time Series DBMS

Score	4.25
Rank	#79	Overall
	#43	Relational DBMS

Website

aws.amazon.com/redshift

etcd.io
github.com/etcd-io/etcd

cloud.google.com/bigtable

www.hawkular.org

www.sap.com/products/technology-platform/sql-anywhere.html

Technical documentation

docs.aws.amazon.com/redshift

etcd.io/docs
github.com/etcd-io/etcd/tree/master/Documentation

cloud.google.com/bigtable/docs

www.hawkular.org/hawkular-metrics/docs/user-guide

help.sap.com/docs/SAP_SQL_Anywhere

Developer

Amazon (based on PostgreSQL)

Google

Community supported by Red Hat

SAP

formerly Sybase

Initial release

2012

2015

2014

1992

Current release

3.4, August 2019

17, July 2015

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

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

Java

Server operating systems

hosted

FreeBSD
Linux
Windows

experimental

hosted

Linux
OS X
Windows

AIX
HP-UX
Linux
OS X
Solaris
Windows

Data scheme

yes

schema-free

yes

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

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

APIs and other access methods

JDBC
ODBC

gRPC
JSON over HTTP

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

HTTP REST

ADO.NET
HTTP API
JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Rust
Scala
Tcl

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

Go
Java
Python
Ruby

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

Server-side scripts

Stored procedures

user defined functions

in Python

yes, in C/C++, Java, .Net or Perl

Triggers

yes, watching key changes

yes

via Hawkular Alerting

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Using Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.

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

selectable replication factor

based on Cassandra

Source-replica replication

Database mirroring

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

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

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

Transaction concepts

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

ACID

Atomic single-row operations

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

fine grained access rights according to SQL-standard

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

fine grained access rights according to SQL-standard

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