DBMS > Cassandra vs. Google Cloud Bigtable vs. GridGain vs. SAP Adaptive Server

System Properties Comparison Cassandra vs. Google Cloud Bigtable vs. GridGain vs. SAP Adaptive Server

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Editorial information provided by DB-Engines

Name

Description

Wide-column store based on ideas of BigTable and DynamoDB

Optimized for write access

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

GridGain is an in-memory computing platform, built on Apache Ignite

The SAP (Sybase) Adaptive Server Enterprise (ASE) is an enterprise-class RDBMS

Primary database model

Wide column store

Key-value store
Wide column store

Key-value store
Relational DBMS

Relational DBMS

Secondary database models

Vector DBMS

starting with release V5

Spatial DBMS

with Boeing's Spatial Query Server

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

Score	98.83
Rank	#12	Overall
	#1	Wide column stores

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

Score	1.55
Rank	#150	Overall
	#26	Key-value stores
	#70	Relational DBMS

Score	35.09
Rank	#25	Overall
	#17	Relational DBMS

Website

cassandra.apache.org

cloud.google.com/bigtable

www.gridgain.com

www.sap.com/products/technology-platform/sybase-ase.html

Technical documentation

cassandra.apache.org/doc/latest

cloud.google.com/bigtable/docs

www.gridgain.com/docs/index.html

help.sap.com/docs/SAP_ASE

Developer

Apache Software Foundation

Apache top level project, originally developped by Facebook

Google

GridGain Systems, Inc.

SAP, Sybase

Initial release

2008

2015

2007

1987

Current release

4.1.3, July 2023

GridGain 8.5.1

16.0

License

Commercial or Open Source

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.

Astra DB: Multi-cloud DBaaS built on Apache Cassandra.

Implementation language

Java

Java, C++, .Net

C and C++

Server operating systems

BSD
Linux
OS X
Windows

hosted

Linux
OS X
Solaris
Windows

AIX
HP-UX
Linux
Solaris
Unix
Windows

Data scheme

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

only equality queries, not always the best performing solution

yes

SQL

Support of SQL

SQL-like SELECT, DML and DDL statements (CQL)

ANSI-99 for query and DML statements, subset of DDL

yes

APIs and other access methods

Proprietary protocol

CQL (Cassandra Query Language, an SQL-like language)
Thrift

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

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

Supported programming languages

C#
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript

Node.js
Perl
PHP
Python
Ruby
Scala

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

C#
C++
Java
PHP
Python
Ruby
Scala

C
C++
Cobol
Java
Java
Perl
PHP
Python

Server-side scripts

Stored procedures

yes (compute grid and cache interceptors can be used instead)

Java and Transact-SQL

Triggers

yes

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

no "single point of failure"

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Representation of geographical distribution of servers is possible

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

yes (replicated cache)

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be individually decided for each write operation

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

Atomicity and isolation are supported for single operations

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

Access rights for users can be defined per object

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

Security Hooks for custom implementations

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

Apache Cassandra is the leading NoSQL, distributed database management system, well...
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Competitive advantages

No single point of failure ensures 100% availability . Operational simplicity for...
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Typical application scenarios

Internet of Things (IOT), fraud detection applications, recommendation engines, product...
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Key customers

Apple, Netflix, Uber, ING,, Intuit,Fidelity, NY Times, Outbrain, BazaarVoice, Best...
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Market metrics

Cassandra is used by 40% of the Fortune 100.
» more

Licensing and pricing models

Apache licenseÂ Pricing for commercial distributions provided by DataStax and available...
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