DBMS > Elasticsearch vs. Google BigQuery vs. Microsoft SQL Server vs. SAP HANA vs. SiteWhere

System Properties Comparison Elasticsearch vs. Google BigQuery vs. Microsoft SQL Server vs. SAP HANA vs. SiteWhere

Editorial information provided by DB-Engines

Name

Description

A distributed, RESTful modern search and analytics engine based on Apache Lucene

Elasticsearch lets you perform and combine many types of searches such as structured, unstructured, geo, and metric

Large scale data warehouse service with append-only tables

Microsofts flagship relational DBMS

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

M2M integration platform for persisting/querying time series data

Primary database model

Search engine

Relational DBMS

Time Series DBMS

Secondary database models

Document store
Spatial DBMS
Vector DBMS

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	128.79
Rank	#8	Overall
	#1	Search engines

Score	52.67
Rank	#19	Overall
	#13	Relational DBMS

Score	807.76
Rank	#3	Overall
	#3	Relational DBMS

Score	41.32
Rank	#23	Overall
	#16	Relational DBMS

Score	0.07
Rank	#347	Overall
	#33	Time Series DBMS

Website

www.elastic.co/elasticsearch

cloud.google.com/bigquery

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

www.sap.com/products/hana.html

github.com/sitewhere/sitewhere

Technical documentation

www.elastic.co/guide/en/elasticsearch/reference/current/index.html

cloud.google.com/bigquery/docs

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

help.sap.com/hana

sitewhere1.sitewhere.io/index.html

Developer

Elastic

Google

Microsoft

SAP

SiteWhere

Initial release

2010

1989

2010

Current release

8.6, January 2023

SQL Server 2022, November 2022

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

Open Source

Elastic License

commercial

restricted free version is available

commercial

Open Source

Common Public Attribution License Version 1.0

Cloud-based only

Only available as a cloud service

yes

also available as a cloud based service

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Implementation language

Java

C++

Java

Server operating systems

All OS with a Java VM

hosted

Linux
Windows

Appliance or cloud-service

Linux
OS X
Windows

Data scheme

schema-free

Flexible type definitions. Once a type is defined, it is persistent

yes

predefined scheme

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

All search fields are automatically indexed

yes

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

Java API
RESTful HTTP/JSON API

RESTful HTTP/JSON API

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

JDBC
ODBC

HTTP REST

Supported programming languages

.Net
Groovy
Community Contributed Clients
Java
JavaScript
Perl
PHP
Python
Ruby

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

Server-side scripts

Stored procedures

yes

user defined functions

in JavaScript

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

SQLScript, R

Triggers

yes

by using the 'percolation' feature

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

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

yes

yes, but depending on the SQL-Server Edition

yes

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

ES-Hadoop Connector

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Synchronous doc based replication. Get by ID may show delays up to 1 sec. Configurable write consistency: one, quorum, all

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

In-memory capabilities

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

Memcached and Redis integration

yes

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

yes

Users with fine-grained authorization concept

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