DBMS > Google BigQuery vs. Microsoft Azure Data Explorer vs. Rockset vs. SAP SQL Anywhere

System Properties Comparison Google BigQuery vs. Microsoft Azure Data Explorer vs. Rockset vs. SAP SQL Anywhere

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Fully managed big data interactive analytics platform

A scalable, reliable search and analytics service in the cloud, built on RocksDB

RDBMS database and synchronization technologies for server, desktop, remote office, and mobile environments

Primary database model

Relational DBMS

column oriented

Document store

Relational DBMS

Secondary database models

Document store

If a column is of type dynamic docs.microsoft.com/en-us/azure/kusto/query/scalar-data-types/dynamic then it's possible to add arbitrary JSON documents in this cell
Event Store

this is the general usage pattern at Microsoft. Billing, Logs, Telemetry events are stored in ADX and the state of an individual entity is defined by the arg_max(timestamps)
Spatial DBMS
Search engine

support for complex search expressions docs.microsoft.com/en-us/azure/kusto/query/parseoperator FTS, Geospatial docs.microsoft.com/en-us/azure/kusto/query/geo-point-to-geohash-function distributed search -> ADX acts as a distributed search engine
Time Series DBMS

see docs.microsoft.com/en-us/azure/data-explorer/time-series-analysis

Relational DBMS
Search engine

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	3.80
Rank	#81	Overall
	#43	Relational DBMS

Score	0.82
Rank	#212	Overall
	#36	Document stores

Score	3.95
Rank	#80	Overall
	#42	Relational DBMS

Website

cloud.google.com/bigquery

azure.microsoft.com/services/data-explorer

rockset.com

www.sap.com/products/technology-platform/sql-anywhere.html

Technical documentation

cloud.google.com/bigquery/docs

docs.microsoft.com/en-us/azure/data-explorer

docs.rockset.com

help.sap.com/docs/SAP_SQL_Anywhere

Developer

Google

Microsoft

Rockset

SAP

formerly Sybase

Initial release

2010

2019

1992

Current release

cloud service with continuous releases

17, July 2015

License

Commercial or Open Source

commercial

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++

Server operating systems

hosted

AIX
HP-UX
Linux
OS X
Solaris
Windows

Data scheme

yes

Fixed schema with schema-less datatypes (dynamic)

schema-free

yes

Typing

predefined data types such as float or date

yes

bool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/en-us/azure/kusto/query/scalar-data-types

dynamic typing

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

ingestion from XML files supported

yes

Secondary indexes

all fields are automatically indexed

yes

SQL

Support of SQL

yes

Kusto Query Language (KQL), SQL subset

Read-only SQL queries, including JOINs

yes

APIs and other access methods

RESTful HTTP/JSON API

Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API

HTTP REST

ADO.NET
HTTP API
JDBC
ODBC

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R

Go
Java
JavaScript (Node.js)
Python

C
C#
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Yes, possible languages: KQL, Python, R

yes, in C/C++, Java, .Net or Perl

Triggers

yes

see docs.microsoft.com/en-us/azure/kusto/management/updatepolicy

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

Automatic sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

yes

Source-replica replication

Database mirroring

MapReduce

Offers an API for user-defined Map/Reduce methods

Spark connector (open source): github.com/Azure/azure-kusto-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
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

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

Google Cloud Identity & Access Management (IAM)

Azure Active Directory Authentication

Access rights for users and organizations can be defined via Rockset console

fine grained access rights according to SQL-standard

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.