DBMS > Amazon Redshift vs. Databricks vs. Drizzle vs. Google Cloud Spanner vs. Percona Server for MySQL

System Properties Comparison Amazon Redshift vs. Databricks vs. Drizzle vs. Google Cloud Spanner vs. Percona Server for MySQL

Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

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.

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

A horizontally scalable, globally consistent, relational database service. It is the externalization of the core Google database that runs the biggest aspects of Google, like Ads and Google Play.

Enhanced drop-in replacement for MySQL based on XtraDB or TokuDB storage engines with improved performance and additional diagnostic and management features.

Primary database model

Relational DBMS

Document store
Relational DBMS

Relational DBMS

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	81.08
Rank	#15	Overall
	#2	Document stores
	#10	Relational DBMS

Score	2.84
Rank	#100	Overall
	#51	Relational DBMS

Score	2.10
Rank	#119	Overall
	#57	Relational DBMS

Website

aws.amazon.com/redshift

www.databricks.com

cloud.google.com/spanner

www.percona.com/software/mysql-database/percona-server

Technical documentation

docs.aws.amazon.com/redshift

docs.databricks.com

cloud.google.com/spanner/docs

www.percona.com/downloads/Percona-Server-LATEST

Developer

Amazon (based on PostgreSQL)

Databricks

Drizzle project, originally started by Brian Aker

Google

Percona

Initial release

2012

2013

2008

2017

2008

Current release

7.2.4, September 2012

8.0.36-28, 2024

License

Commercial or Open Source

commercial

Open Source

GNU GPL

commercial

Open Source

GPL version 2

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++

C and C++

Server operating systems

hosted

FreeBSD
Linux
OS X

hosted

Linux

Data scheme

yes

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

yes

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

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

with Databricks SQL

yes

with proprietary extensions

yes

Query statements complying to ANSI 2011

yes

APIs and other access methods

JDBC
ODBC

JDBC
ODBC
RESTful HTTP API

JDBC

gRPC (using protocol buffers) API
JDBC

At present, JDBC supports read-only queries. No support for DDL or DML statements.
RESTful HTTP API

ADO.NET
JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

Python
R
Scala

C
C++
Java
PHP

Go
Java
JavaScript (Node.js)
Python

Ada
C
C#
C++
D
Eiffel
Erlang
Haskell
Java
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

user defined functions

in Python

user defined functions and aggregates

yes

Triggers

hooks for callbacks inside the server can be used.

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

Multi-source replication with 3 replicas for regional instances.

Multi-source replication
Source-replica replication
XtraDB Cluster

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

by using interleaved tables, this features focuses more on performance improvements than on referential integrity

yes

Transaction concepts

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

ACID

Strict serializable isolation

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

fine grained access rights according to SQL-standard

Pluggable authentication mechanisms

e.g. LDAP, HTTP

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

Users with fine-grained authorization concept

no user groups or roles

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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