DBMS > Apache Impala vs. Blueflood vs. Datomic vs. IBM Db2 vs. Microsoft SQL Server

System Properties Comparison Apache Impala vs. Blueflood vs. Datomic vs. IBM Db2 vs. Microsoft SQL Server

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

Scalable TimeSeries DBMS based on Cassandra

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

Common in IBM host environments, 2 different versions for host and Windows/Linux

Microsofts flagship relational DBMS

Primary database model

Relational DBMS

Time Series DBMS

Relational DBMS

Since Version 10.5 support for JSON/BSON documents compatible with MongoDB

Relational DBMS

Secondary database models

Document store

Document store
RDF store

in Db2 LUW (Linux, Unix, Windows)
Spatial DBMS

with Db2 Spatial Extender

Document store
Graph DBMS
Spatial DBMS

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	0.06
Rank	#353	Overall
	#34	Time Series DBMS

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	128.46
Rank	#8	Overall
	#5	Relational DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Website

impala.apache.org

blueflood.io

www.datomic.com

www.ibm.com/products/db2

www.microsoft.com/en-us/sql-server

Technical documentation

impala.apache.org/impala-docs.html

github.com/rax-maas/blueflood/wiki

docs.datomic.com

www.ibm.com/docs/en/db2

learn.microsoft.com/en-US/sql/sql-server

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Rackspace

Cognitect

IBM

Microsoft

Initial release

2013

2012

1983

host version

1989

Current release

4.1.0, June 2022

1.0.6735, June 2023

12.1, October 2016

SQL Server 2022, November 2022

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache 2.0

commercial

limited edition free

commercial

free version is available

commercial

restricted free version is available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C++

Java

Java, Clojure

C and C++

C++

Server operating systems

Linux

Linux
OS X

All OS with a Java VM

AIX
HP-UX
Linux
Solaris
Windows
z/OS

Linux
Windows

Data scheme

yes

predefined 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

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC
ODBC

HTTP REST

RESTful HTTP API

ADO.NET
JDBC
JSON style queries

MongoDB compatible
ODBC
XQuery

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

Supported programming languages

All languages supporting JDBC/ODBC

Clojure
Java

C
C#
C++
Cobol
Delphi
Fortran
Java
Perl
PHP
Python
Ruby
Visual Basic

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

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

Transaction Functions

yes

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

Triggers

By using transaction functions

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

none

But extensive use of caching in the application peers

Sharding

only with Windows/Unix/Linux Version

tables can be distributed across several files (horizontal partitioning); sharding through federation

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

none

But extensive use of caching in the application peers

yes

with separate tools (MQ, InfoSphere)

yes, but depending on the SQL-Server Edition

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

In-memory capabilities

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

yes

recommended only for testing and development

yes

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

fine grained access rights according to SQL-standard

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