DBMS > Apache Impala vs. Drizzle vs. Microsoft SQL Server vs. ScyllaDB vs. Teradata

System Properties Comparison Apache Impala vs. Drizzle vs. Microsoft SQL Server vs. ScyllaDB vs. Teradata

Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

Description

Analytic DBMS for Hadoop

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

Microsofts flagship relational DBMS

Cassandra and DynamoDB compatible wide column store

A hybrid cloud data analytics software platform (Teradata Vantage)

Primary database model

Relational DBMS

Wide column store

Relational DBMS

Secondary database models

Document store

Document store
Graph DBMS
Spatial DBMS

Key-value store

Document store
Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	4.08
Rank	#76	Overall
	#5	Wide column stores

Score	44.87
Rank	#22	Overall
	#15	Relational DBMS

Website

impala.apache.org

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

www.scylladb.com

www.teradata.com

Technical documentation

impala.apache.org/impala-docs.html

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

docs.scylladb.com

docs.teradata.com

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Drizzle project, originally started by Brian Aker

Microsoft

ScyllaDB

Teradata

Initial release

2013

2008

1989

2015

1984

Current release

4.1.0, June 2022

7.2.4, September 2012

SQL Server 2022, November 2022

ScyllaDB Open Source 5.4.1, January 2024

Teradata Vantage 1.0 MU2, January 2019

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

GNU GPL

commercial

restricted free version is available

Open Source

Open Source (AGPL), commercial license available

commercial

Cloud-based only

Only available as a cloud service

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Implementation language

C++

Server operating systems

Linux

FreeBSD
Linux
OS X

Linux
Windows

Linux

hosted
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.

yes

Secondary indexes

yes

cluster global secondary indices

yes

Join-index to prejoin tables, aggregate index, sparse index, hash index

SQL

Support of SQL

SQL-like DML and DDL statements

yes

with proprietary extensions

yes

SQL-like DML and DDL statements (CQL)

yes

SQL 2016 + extensions

APIs and other access methods

JDBC
ODBC

JDBC

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

Proprietary protocol (CQL)

compatible with CQL (Cassandra Query Language, an SQL-like language)
RESTful HTTP API (DynamoDB compatible)
Thrift

.NET Client API
HTTP REST
JDBC
JMS Adapter
ODBC
OLE DB

Supported programming languages

All languages supporting JDBC/ODBC

C
C++
Java
PHP

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

For CQL interface: C#, C++, Clojure, Erlang, Go, Haskell, Java, JavaScript, Node.js, Perl, PHP, Python, Ruby, Rust, Scala
For DynamoDB interface: .Net, ColdFusion, Erlang, Groovy, Java, JavaScript, Perl, PHP, Python, Ruby

C
C++
Cobol
Java (JDBC-ODBC)
Perl
PL/1
Python
R
Ruby

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

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

yes, Lua

yes

UDFs, stored procedures, table functions in parallel

Triggers

hooks for callbacks inside the server can be used.

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

Sharding

Hashing

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Multi-source replication
Source-replica replication

yes, but depending on the SQL-Server Edition

selectable replication factor

Representation of geographical distribution of servers is possible

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

Immediate Consistency

Eventual Consistency
Tunable Consistency

can be individually decided for each write operation

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Atomicity and isolation are supported for single operations

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

in-memory tables

yes

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Pluggable authentication mechanisms

e.g. LDAP, HTTP

fine grained access rights according to SQL-standard

Access rights for users can be defined per object

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

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Competitive advantages

Highly-performant (efficiently utilizes full resources of a node and network; millions...
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Typical application scenarios

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Key customers

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Market metrics

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Licensing and pricing models

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