DBMS > Google Cloud Bigtable vs. Hive vs. Microsoft Azure Cosmos DB vs. Sadas Engine vs. TimesTen

System Properties Comparison Google Cloud Bigtable vs. Hive vs. Microsoft Azure Cosmos DB vs. Sadas Engine vs. TimesTen

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.

data warehouse software for querying and managing large distributed datasets, built on Hadoop

Globally distributed, horizontally scalable, multi-model database service

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

In-Memory RDBMS compatible to Oracle

Primary database model

Key-value store
Wide column store

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Secondary database models

Spatial DBMS

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	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	0.00
Rank	#383	Overall
	#158	Relational DBMS

Score	1.31
Rank	#163	Overall
	#74	Relational DBMS

Website

cloud.google.com/bigtable

hive.apache.org

azure.microsoft.com/services/cosmos-db

www.sadasengine.com

www.oracle.com/database/technologies/related/timesten.html

Technical documentation

cloud.google.com/bigtable/docs

cwiki.apache.org/confluence/display/Hive/Home

learn.microsoft.com/azure/cosmos-db

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

docs.oracle.com/database/timesten-18.1

Developer

Google

Apache Software Foundation

initially developed by Facebook

Microsoft

SADAS s.r.l.

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

2015

2012

2014

2006

1998

Current release

3.1.3, April 2022

8.0

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

commercial

free trial version available

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

C++

Server operating systems

hosted

All OS with a Java VM

hosted

AIX
Linux
Windows

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON types

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

All properties auto-indexed by default

yes

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language

yes

APIs and other access methods

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

JDBC
ODBC
Thrift

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

JDBC
ODBC
Proprietary protocol

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

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

C++
Java
PHP
Python

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

.Net
C
C#
C++
Groovy
Java
PHP
Python

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

JavaScript

PL/SQL

Triggers

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

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

selectable replication factor

yes

Implicit feature of the cloud service

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

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

Eventual Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic single-row operations

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by means of logfiles and checkpoints

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

managed by 'Learn by Usage'

yes

User concepts

Access control

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

Access rights for users, groups and roles

Access rights can be defined down to the item level

Access rights for users, groups and roles according to SQL-standard

fine grained access rights according to SQL-standard

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