DBMS > Amazon Redshift vs. Hypertable vs. Sqrrl vs. TigerGraph

System Properties Comparison Amazon Redshift vs. Hypertable vs. Sqrrl vs. TigerGraph

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Editorial information provided by DB-Engines

Name

Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.

Sqrrl has been acquired by Amazon and became a part of Amazon Web Services. It has been removed from the DB-Engines ranking.

Description

Large scale data warehouse service for use with business intelligence tools

An open source BigTable implementation based on distributed file systems such as Hadoop

Adaptable, secure NoSQL built on Apache Accumulo

A complete, distributed, parallel graph computing platform supporting web-scale data analytics in real-time

Primary database model

Relational DBMS

Wide column store

Document store
Graph DBMS
Key-value store
Wide column store

Graph DBMS

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

Score	15.25
Rank	#38	Overall
	#23	Relational DBMS

Score	1.44
Rank	#152	Overall
	#14	Graph DBMS

Website

aws.amazon.com/redshift

sqrrl.com

www.tigergraph.com

Technical documentation

docs.aws.amazon.com/redshift

docs.tigergraph.com

Developer

Amazon (based on PostgreSQL)

Hypertable Inc.

Amazon

originally Sqrrl Data, Inc.

Initial release

2012

2009

2012

2017

Current release

0.9.8.11, March 2016

License

Commercial or Open Source

commercial

Open Source

GNU version 3. Commercial license available

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Java

C++

Server operating systems

hosted

Linux
OS X
Windows

an inofficial Windows port is available

Linux

Data scheme

yes

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.

Secondary indexes

restricted

only exact value or prefix value scans

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like query language (GSQL)

APIs and other access methods

JDBC
ODBC

C++ API
Thrift

Accumulo Shell
Java API
JDBC
ODBC
RESTful HTTP API
Thrift

GSQL (TigerGraph Query Language)
Kafka
RESTful HTTP/JSON API

Supported programming languages

All languages supporting JDBC/ODBC

C++
Java
Perl
PHP
Python
Ruby

Actionscript
C

using GLib
C#
C++
Cocoa
Delphi
Erlang
Go
Haskell
Java
JavaScript
OCaml
Perl
PHP
Python
Ruby
Smalltalk

C++
Java

Server-side scripts

Stored procedures

user defined functions

in Python

yes

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

making use of Hadoop

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor on file system level

selectable replication factor

making use of Hadoop

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Document store kept consistent with combination of global timestamping, row-level transactions, and server-side consistency resolution.

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

Relationships in graphs

Transaction concepts

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

ACID

Atomic updates per row, document, or graph entity

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

fine grained access rights according to SQL-standard

Cell-level Security, Data-Centric Security, Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC)

Role-based access control

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