DBMS > Google Cloud Bigtable vs. Heroic vs. SAP Advantage Database Server vs. Trafodion vs. Yanza

System Properties Comparison Google Cloud Bigtable vs. Heroic vs. SAP Advantage Database Server vs. Trafodion vs. Yanza

Editorial information provided by DB-Engines

Name

SAP Advantage Database Server seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Apache Trafodion has been retired in 2021. Therefore it is excluded from the DB-Engines Ranking.

Yanza seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

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.

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

Low-cost RDBMS with access to ISAM and FoxPro data structures

Transactional SQL-on-Hadoop DBMS

Time Series DBMS for IoT Applications

Primary database model

Key-value store
Wide column store

Time Series DBMS

Relational DBMS

Time Series DBMS

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

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

Score	0.46
Rank	#265	Overall
	#22	Time Series DBMS

Website

cloud.google.com/bigtable

github.com/spotify/heroic

www.sap.com/products/advantage-database-server.html

trafodion.apache.org

yanza.com

Technical documentation

cloud.google.com/bigtable/docs

spotify.github.io/heroic

trafodion.apache.org/documentation.html

Developer

Google

Spotify

Sybase, SAP

Apache Software Foundation, originally developed by HP

Yanza

Initial release

2015

2014

1993

2014

2015

Current release

2.3.0, February 2019

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

Open Source

Apache 2.0

commercial

free version available

Cloud-based only

Only available as a cloud service

yes

but mainly used as a service provided by Yanza

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C++, Java

Server operating systems

hosted

Linux
Windows

Linux

Windows

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.

Secondary indexes

yes

via Elasticsearch

yes

SQL

Support of SQL

yes

APIs and other access methods

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

HQL (Heroic Query Language, a JSON-based language)
HTTP API

.NET Client API
ADO.NET
JDBC
ODBC
OLE DB

ADO.NET
JDBC
ODBC

HTTP API

Supported programming languages

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

Delphi
Perl
PHP

All languages supporting JDBC/ODBC/ADO.Net

any language that supports HTTP calls

Server-side scripts

Stored procedures

yes

Java Stored Procedures

Triggers

yes

Timer and event based

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

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

yes

Multi-source replication

CONFLICT Trigger to resolve replication conflicts
Source-replica replication

yes, via HBase

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via user defined functions and HBase

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
Immediate 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

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.

caching of (temporary) tables can be configured

User concepts

Access control

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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More resources

Recent citations in the news

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Google Introduces Autoscaling for Cloud Bigtable for Optimizing Costs
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