DBMS > BigchainDB vs. Google Cloud Datastore vs. SAP HANA vs. STSdb vs. Teradata

System Properties Comparison BigchainDB vs. Google Cloud Datastore vs. SAP HANA vs. STSdb vs. Teradata

Editorial information provided by DB-Engines

Name

Description

BigchainDB is scalable blockchain database offering decentralization, immutability and native assets

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

In-memory, column based data store. Available as appliance or cloud service

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

A hybrid cloud data analytics software platform (Teradata Vantage)

Primary database model

Secondary database models

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

Document store
Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	0.79
Rank	#212	Overall
	#36	Document stores

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	44.69
Rank	#22	Overall
	#16	Relational DBMS

Score	0.04
Rank	#360	Overall
	#52	Key-value stores

Score	45.33
Rank	#21	Overall
	#15	Relational DBMS

Website

www.bigchaindb.com

cloud.google.com/datastore

www.sap.com/products/hana.html

github.com/STSSoft/STSdb4

www.teradata.com

Technical documentation

bigchaindb.readthedocs.io/en/latest

cloud.google.com/datastore/docs

help.sap.com/hana

docs.teradata.com

Developer

Google

SAP

STS Soft SC

Teradata

Initial release

2016

2008

2010

2011

1984

Current release

2.0 SPS07 (April 4, 2023), April 2023

4.0.8, September 2015

Teradata Vantage 1.0 MU2, January 2019

License

Commercial or Open Source

Open Source

AGPL v3

commercial

Open Source

GPLv2, commercial license available

commercial

Cloud-based only

Only available as a cloud service

yes

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Python

Server operating systems

Linux

hosted

Appliance or cloud-service

Windows

hosted
Linux

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes, details here

yes

primitive types and user defined types (classes)

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

yes

Join-index to prejoin tables, aggregate index, sparse index, hash index

SQL

Support of SQL

SQL-like query language (GQL)

yes

SQL 2016 + extensions

APIs and other access methods

CLI Client
RESTful HTTP API

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

JDBC
ODBC

.NET Client API

.NET Client API
HTTP REST
JDBC
JMS Adapter
ODBC
OLE DB

Supported programming languages

Go
Haskell
Java
JavaScript
Python
Ruby

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

C#
Java

C
C++
Cobol
Java (JDBC-ODBC)
Perl
PL/1
Python
R
Ruby

Server-side scripts

Stored procedures

using Google App Engine

SQLScript, R

yes

UDFs, stored procedures, table functions in parallel

Triggers

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

none

Sharding

Hashing

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Multi-source replication using Paxos

yes

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes,with MongoDB ord RethinkDB

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

yes

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

yes

fine grained access rights according to SQL-standard

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