DBMS > Google Cloud Datastore vs. Sadas Engine vs. SQLite vs. Teradata Aster

System Properties Comparison Google Cloud Datastore vs. Sadas Engine vs. SQLite vs. Teradata Aster

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Editorial information provided by DB-Engines

Name

Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.

Description

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

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

Widely used embeddable, in-process RDBMS

Platform for big data analytics on multistructured data sources and types

Primary database model

Document store

Relational DBMS

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

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	0.00
Rank	#383	Overall
	#158	Relational DBMS

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Website

cloud.google.com/datastore

www.sadasengine.com

www.sqlite.org

Technical documentation

cloud.google.com/datastore/docs

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

www.sqlite.org/docs.html

Developer

Google

SADAS s.r.l.

Dwayne Richard Hipp

Teradata

Initial release

2008

2006

2000

2005

Current release

8.0

3.45.3 (15 April 2024), April 2024

License

Commercial or Open Source

commercial

free trial version available

Open Source

Public Domain

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

C++

Server operating systems

hosted

AIX
Linux
Windows

server-less

Linux

Data scheme

schema-free

yes

dynamic column types

Flexible Schema (defined schema, partial schema, schema free)

defined schema within the relational store; partial schema or schema free in the Aster File Store

Typing

predefined data types such as float or date

yes, details here

yes

not rigid because of 'dynamic typing' concept.

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

in Aster File Store

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language (GQL)

yes

SQL-92 is not fully supported

yes

APIs and other access methods

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

JDBC
ODBC
Proprietary protocol

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

ADO.NET
JDBC
ODBC
OLE DB

Supported programming languages

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

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

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

C
C#
C++
Java
Python
R

Server-side scripts

Stored procedures

using Google App Engine

R packages

Triggers

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

none

yes

Dimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

yes

SQL Map-Reduce Framework

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

Immediate Consistency or Eventual Consistency depending on configuration

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

via file-system locks

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

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 according to SQL-standard

fine grained access rights according to SQL-standard

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