DBMS > Apache Druid vs. Microsoft Azure Synapse Analytics vs. MySQL vs. Vertica

System Properties Comparison Apache Druid vs. Microsoft Azure Synapse Analytics vs. MySQL vs. Vertica

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Editorial information provided by DB-Engines

Name

Description

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

Widely used open source RDBMS

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

Primary database model

Relational DBMS
Time Series DBMS

Relational DBMS

Key/Value like access via memcached API

Relational DBMS

Column oriented

Secondary database models

Document store
Spatial DBMS

Spatial DBMS
Time Series DBMS

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

Score	3.34
Rank	#88	Overall
	#48	Relational DBMS
	#7	Time Series DBMS

Score	20.56
Rank	#31	Overall
	#19	Relational DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	10.68
Rank	#43	Overall
	#27	Relational DBMS

Website

druid.apache.org

azure.microsoft.com/services/synapse-analytics

www.mysql.com

www.vertica.com

Technical documentation

druid.apache.org/docs/latest/design

docs.microsoft.com/azure/synapse-analytics

dev.mysql.com/doc

vertica.com/documentation

Developer

Apache Software Foundation and contributors

Microsoft

Oracle

since 2010, originally MySQL AB, then Sun

OpenText

previously Micro Focus and Hewlett Packard

Initial release

2012

2016

1995

2005

Current release

29.0.1, April 2024

8.3.0, January 2024

12.0.3, January 2023

License

Commercial or Open Source

Open Source

Apache license v2

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

commercial

Limited community edition free

Cloud-based only

Only available as a cloud service

yes

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++

C and C++

C++

Server operating systems

Linux
OS X
Unix

hosted

FreeBSD
Linux
OS X
Solaris
Windows

Linux

Data scheme

yes

schema-less columns are supported

yes

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

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

No Indexes Required. Different internal optimization strategy, but same functionality included.

SQL

Support of SQL

SQL for querying

yes

with proprietary extensions

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

APIs and other access methods

JDBC
RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC

ADO.NET
JDBC
ODBC
Proprietary native API

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

C#
Java
PHP

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R

Server-side scripts

Stored procedures

Transact SQL

yes

proprietary syntax

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Sharding, horizontal partitioning

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

yes

Multi-source replication
Source-replica replication

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

MapReduce

Offers an API for user-defined Map/Reduce methods

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

yes

not for MyISAM storage engine

yes

Transaction concepts

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

ACID

not for MyISAM storage engine

ACID

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

yes

Users with fine-grained authorization concept

no user groups or roles

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

More information provided by the system vendor

Specific characteristics

Deploy-anywhere database for large-scale analytical deployments. Deploy off-cloud,...
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Competitive advantages

Fast, scalable, and capable of high concurrency. Separation of compute/storage leverages...
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Typical application scenarios

Communication and network analytics, Embedded analytics, Fraud monitoring and Risk...
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Key customers

Abiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
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Licensing and pricing models

Cost-based models and subscription-based models are both available. One license is...
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