DBMS > Amazon Redshift vs. Apache Impala vs. IBM Db2 Event Store vs. Tibero vs. TigerGraph

System Properties Comparison Amazon Redshift vs. Apache Impala vs. IBM Db2 Event Store vs. Tibero vs. TigerGraph

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Analytic DBMS for Hadoop

Distributed Event Store optimized for Internet of Things use cases

A secure RDBMS, designed for easy portability from Oracle

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

Primary database model

Relational DBMS

Event Store
Time Series DBMS

Relational DBMS

Graph DBMS

Secondary database models

Document store

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	0.19
Rank	#323	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	1.78
Rank	#140	Overall
	#64	Relational DBMS

Score	1.83
Rank	#139	Overall
	#13	Graph DBMS

Website

aws.amazon.com/redshift

impala.apache.org

www.ibm.com/products/db2-event-store

us.tmaxsoft.com/products/tibero

www.tigergraph.com

Technical documentation

docs.aws.amazon.com/redshift

impala.apache.org/impala-docs.html

www.ibm.com/docs/en/db2-event-store

technet.tmaxsoft.com/upload/download/online/tibero/pver-20150504-000002/index.html

docs.tigergraph.com

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

IBM

TmaxSoft

Initial release

2012

2013

2017

2003

2017

Current release

4.1.0, June 2022

2.0

6, April 2015

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

commercial

free developer edition available

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++

C and C++

C and Assembler

C++

Server operating systems

hosted

Linux

Linux, macOS, Windows for the developer addition

AIX
HP-UX
Linux
Solaris
Windows

Linux

Data scheme

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.

yes

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like DML and DDL statements

yes

through the embedded Spark runtime

yes

SQL-like query language (GSQL)

APIs and other access methods

JDBC
ODBC

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
Tibero CLI

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

Supported programming languages

All languages supporting JDBC/ODBC

C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic

C
C#
C++
Cobol
Java
Objective-C
Perl
PHP
Python
Ruby
Visual Basic

C++
Java

Server-side scripts

Stored procedures

user defined functions

in Python

yes

user defined functions and integration of map-reduce

yes

Persistent Stored Procedure (PSM)

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

by range, hash, list or composite

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

Active-active shard replication

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

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

Concurrency

Support for concurrent manipulation of data

yes

No - written data is immutable

yes

Durability

Support for making data persistent

yes

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

planned for next version

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

fine grained access rights according to SQL-standard

fine grained access rights according to SQL-standard (SQL 92, SQL 99)

Role-based access control

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.