DBMS > Apache Impala vs. BigObject vs. Microsoft SQL Server vs. SAP HANA vs. VoltDB

System Properties Comparison Apache Impala vs. BigObject vs. Microsoft SQL Server vs. SAP HANA vs. VoltDB

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

Analytic DBMS for real-time computations and queries

Microsofts flagship relational DBMS

In-memory, column based data store. Available as appliance or cloud service

Distributed In-Memory NewSQL RDBMS

Used for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Relational DBMS

Secondary database models

Document store

Document store
Graph DBMS
Spatial DBMS

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	0.19
Rank	#329	Overall
	#146	Relational DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Score	1.47
Rank	#157	Overall
	#73	Relational DBMS

Website

impala.apache.org

bigobject.io

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

www.sap.com/products/hana.html

www.voltdb.com

Technical documentation

impala.apache.org/impala-docs.html

docs.bigobject.io

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

help.sap.com/hana

docs.voltdb.com

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

BigObject, Inc.

Microsoft

SAP

VoltDB Inc.

Initial release

2013

2015

1989

2010

Current release

4.1.0, June 2022

SQL Server 2022, November 2022

2.0 SPS07 (April 4, 2023), April 2023

11.3, April 2022

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

free community edition available

commercial

restricted free version is available

commercial

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

Cloud-based only

Only available as a cloud service

also available as a cloud based service

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Implementation language

C++

Java, C++

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

Linux
Windows

Appliance or cloud-service

Linux
OS X

for development

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

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

only a subset of SQL 99

APIs and other access methods

JDBC
ODBC

fluentd
ODBC
RESTful HTTP API

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

JDBC
ODBC

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

All languages supporting JDBC/ODBC

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

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Lua

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

SQLScript, R

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

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

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

yes, but depending on the SQL-Server Edition

yes

Multi-source replication
Source-replica replication

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

none

Immediate Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

yes

FOREIGN KEY constraints are not supported

Transaction concepts

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

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes

Snapshots and command logging

In-memory capabilities

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

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

yes

Users and roles with access to stored procedures

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