DBMS > Google BigQuery vs. IBM Db2 warehouse vs. NSDb vs. Sadas Engine

System Properties Comparison Google BigQuery vs. IBM Db2 warehouse vs. NSDb vs. Sadas Engine

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Cloud-based data warehousing service

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

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

Primary database model

Relational DBMS

Time Series DBMS

Relational DBMS

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	1.30
Rank	#164	Overall
	#75	Relational DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	0.00
Rank	#383	Overall
	#158	Relational DBMS

Website

cloud.google.com/bigquery

www.ibm.com/products/db2/warehouse

nsdb.io

www.sadasengine.com

Technical documentation

cloud.google.com/bigquery/docs

nsdb.io/Architecture

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

Developer

Google

IBM

SADAS s.r.l.

Initial release

2010

2014

2017

2006

Current release

8.0

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

free trial version available

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

Java, Scala

C++

Server operating systems

hosted

Linux
macOS

AIX
Linux
Windows

Data scheme

yes

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, string

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.

Import/export of XML data possible

Secondary indexes

yes

all fields are automatically indexed

yes

SQL

Support of SQL

yes

SQL-like query language

yes

APIs and other access methods

RESTful HTTP/JSON API

.NET Client API
JDBC
ODBC
OLE DB

gRPC
HTTP REST
WebSocket

JDBC
ODBC
Proprietary protocol

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

Java
Scala

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

Server-side scripts

Stored procedures

user defined functions

in JavaScript

PL/SQL, SQL PL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

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

Using Apache Lucene

yes

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)

fine grained access rights according to SQL-standard

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

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