DBMS > Amazon Redshift vs. Databricks vs. HarperDB vs. Microsoft Azure Data Explorer

System Properties Comparison Amazon Redshift vs. Databricks vs. HarperDB vs. Microsoft Azure Data Explorer

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

The Databricks Lakehouse Platform combines elements of data lakes and data warehouses to provide a unified view onto structured and unstructured data. It is based on Apache Spark.

Ultra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.

Fully managed big data interactive analytics platform

Primary database model

Relational DBMS

Document store
Relational DBMS

Document store

Relational DBMS

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	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	78.61
Rank	#15	Overall
	#2	Document stores
	#10	Relational DBMS

Score	0.55
Rank	#248	Overall
	#38	Document stores

Score	4.38
Rank	#77	Overall
	#41	Relational DBMS

Website

aws.amazon.com/redshift

www.databricks.com

www.harperdb.io

azure.microsoft.com/services/data-explorer

Technical documentation

docs.aws.amazon.com/redshift

docs.databricks.com

docs.harperdb.io/docs

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

Developer

Amazon (based on PostgreSQL)

Databricks

HarperDB

Microsoft

Initial release

2012

2013

2017

2019

Current release

3.1, August 2021

cloud service with continuous releases

License

Commercial or Open Source

commercial

free community edition available

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

Node.js

Server operating systems

hosted

Linux
OS X

hosted

Data scheme

yes

Flexible Schema (defined schema, partial schema, schema free)

dynamic schema

Fixed schema with schema-less datatypes (dynamic)

Typing

predefined data types such as float or date

yes

JSON data types

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

with Databricks SQL

SQL-like data manipulation statements

Kusto Query Language (KQL), SQL subset

APIs and other access methods

JDBC
ODBC

JDBC
ODBC
RESTful HTTP API

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

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

Supported programming languages

All languages supporting JDBC/ODBC

Python
R
Scala

.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift

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

Server-side scripts

Stored procedures

user defined functions

in Python

user defined functions and aggregates

Custom Functions

since release 3.1

Yes, possible languages: KQL, Python, R

Triggers

yes

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

Partitioning methods

Methods for storing different data on different nodes

Sharding

A table resides as a whole on one (or more) nodes in a cluster

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

yes

the nodes on which a table resides can be defined

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

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

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

Transaction concepts

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

ACID

Atomic execution of specific operations

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, using LMDB

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

Access rights for users and roles

Azure Active Directory Authentication

More information provided by the system vendor

Specific characteristics

Supported database models : In addition to the Document store and Relational DBMS...
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