DBMS > Google BigQuery vs. Netezza vs. Sadas Engine vs. XTDB

System Properties Comparison Google BigQuery vs. Netezza vs. Sadas Engine vs. XTDB

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Data warehouse and analytics appliance part of IBM PureSystems

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Relational DBMS

Document store

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

Score	61.90
Rank	#19	Overall
	#13	Relational DBMS

Score	10.18
Rank	#46	Overall
	#29	Relational DBMS

Score	0.03
Rank	#379	Overall
	#156	Relational DBMS

Score	0.09
Rank	#351	Overall
	#47	Document stores

Website

cloud.google.com/bigquery

www.ibm.com/products/netezza

www.sadasengine.com

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

cloud.google.com/bigquery/docs

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

www.xtdb.com/docs

Developer

Google

IBM

SADAS s.r.l.

Juxt Ltd.

Initial release

2010

2000

2006

2019

Current release

8.0

1.19, September 2021

License

Commercial or Open Source

commercial

free trial version available

Open Source

MIT License

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++

Clojure

Server operating systems

hosted

Linux

included in appliance

AIX
Linux
Windows

All OS with a Java 8 (and higher) VM
Linux

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, extensible-data-notation format

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

yes

SQL

Support of SQL

yes

limited SQL, making use of Apache Calcite

APIs and other access methods

RESTful HTTP/JSON API

JDBC
ODBC
OLE DB

JDBC
ODBC
Proprietary protocol

HTTP REST
JDBC

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C++
Fortran
Java
Lua
Perl
Python
R

.Net
C
C#
C++
Groovy
Java
PHP
Python

Clojure
Java

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

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

yes

managed by 'Learn by Usage'

User concepts

Access control

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Users with fine-grained authorization concept

Access rights for users, groups and roles according to SQL-standard

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