DBMS > Apache Pinot vs. Google Cloud Datastore vs. Sadas Engine vs. Stardog

System Properties Comparison Apache Pinot vs. Google Cloud Datastore vs. Sadas Engine vs. Stardog

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Editorial information provided by DB-Engines

Name

Description

Realtime distributed OLAP datastore, designed to answer OLAP queries with low latency

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

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

Primary database model

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

Score	0.38
Rank	#275	Overall
	#126	Relational DBMS

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	0.07
Rank	#373	Overall
	#157	Relational DBMS

Score	2.07
Rank	#122	Overall
	#11	Graph DBMS
	#6	RDF stores

Website

pinot.apache.org

cloud.google.com/datastore

www.sadasengine.com

www.stardog.com

Technical documentation

docs.pinot.apache.org

cloud.google.com/datastore/docs

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

docs.stardog.com

Developer

Apache Software Foundation and contributors

Google

SADAS s.r.l.

Stardog-Union

Initial release

2015

2008

2006

2010

Current release

1.0.0, September 2023

8.0

7.3.0, May 2020

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

free trial version available

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

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++

Java

Server operating systems

All OS with a Java JDK11 or higher

hosted

AIX
Linux
Windows

Linux
macOS
Windows

Data scheme

yes

schema-free

yes

schema-free and OWL/RDFS-schema support

Typing

predefined data types such as float or date

yes

yes, details here

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.

Import/export of XML data possible

Secondary indexes

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

SQL-like query language

SQL-like query language (GQL)

yes

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

APIs and other access methods

JDBC

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

JDBC
ODBC
Proprietary protocol

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

Supported programming languages

Go
Java
Python

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

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

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Server-side scripts

Stored procedures

using Google App Engine

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

Callbacks using the Google Apps Engine

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

none

Multi-source replication in HA-Cluster

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

Immediate Consistency in HA-Cluster

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

relationships in graphs

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

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

Access rights for users and roles

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More resources

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