DBMS > Amazon Redshift vs. BigchainDB vs. EXASOL vs. Hawkular Metrics vs. Microsoft Azure Data Explorer

System Properties Comparison Amazon Redshift vs. BigchainDB vs. EXASOL vs. Hawkular Metrics vs. Microsoft Azure Data Explorer

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

BigchainDB is scalable blockchain database offering decentralization, immutability and native assets

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.

Fully managed big data interactive analytics platform

Primary database model

column oriented

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

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	0.85
Rank	#208	Overall
	#35	Document stores

Score	1.76
Rank	#139	Overall
	#62	Relational DBMS

Score	0.08
Rank	#366	Overall
	#39	Time Series DBMS

Score	3.80
Rank	#81	Overall
	#43	Relational DBMS

Website

aws.amazon.com/redshift

www.bigchaindb.com

www.exasol.com

www.hawkular.org

azure.microsoft.com/services/data-explorer

Technical documentation

docs.aws.amazon.com/redshift

bigchaindb.readthedocs.io/en/latest

www.exasol.com/resources

www.hawkular.org/hawkular-metrics/docs/user-guide

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

Developer

Amazon (based on PostgreSQL)

Exasol

Community supported by Red Hat

Microsoft

Initial release

2012

2016

2000

2014

2019

Current release

cloud service with continuous releases

License

Commercial or Open Source

commercial

Open Source

AGPL v3

commercial

Open Source

Apache 2.0

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

Python

Java

Server operating systems

hosted

Linux

Linux
OS X
Windows

hosted

Data scheme

yes

schema-free

yes

schema-free

Fixed schema with schema-less datatypes (dynamic)

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

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

restricted

yes

all fields are automatically indexed

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

Kusto Query Language (KQL), SQL subset

APIs and other access methods

JDBC
ODBC

CLI Client
RESTful HTTP API

.Net
JDBC
ODBC
WebSocket

HTTP REST

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

Supported programming languages

All languages supporting JDBC/ODBC

Go
Haskell
Java
JavaScript
Python
Ruby

Java
Lua
Python
R

Go
Java
Python
Ruby

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

Server-side scripts

Stored procedures

user defined functions

in Python

user defined functions

Yes, possible languages: KQL, Python, R

Triggers

yes

via Hawkular Alerting

yes

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

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

based on Cassandra

yes

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

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

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

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes,with MongoDB ord RethinkDB

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

fine grained access rights according to SQL-standard

yes

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

Azure Active Directory Authentication

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