DB-EnginesExtremeDB for everyone with an RTOSEnglish
Deutsch
Knowledge Base of Relational and NoSQL Database Management Systemsprovided by solid IT

DBMS > Amazon Redshift vs. Apache Impala vs. Vitess

System Properties Comparison Amazon Redshift vs. Apache Impala vs. Vitess

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines
NameAmazon Redshift  Xexclude from comparisonApache Impala  Xexclude from comparisonVitess  Xexclude from comparison
DescriptionLarge scale data warehouse service for use with business intelligence toolsAnalytic DBMS for HadoopScalable, distributed, cloud-native DBMS, extending MySQL
Primary database modelRelational DBMSRelational DBMSRelational DBMS
Secondary database modelsDocument storeDocument store
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score19.03
Rank#34  Overall
#21  Relational DBMS
Score14.03
Rank#40  Overall
#24  Relational DBMS
Score1.04
Rank#191  Overall
#89  Relational DBMS
Websiteaws.amazon.com/­redshiftimpala.apache.orgvitess.io
Technical documentationdocs.aws.amazon.com/­redshiftimpala.apache.org/­impala-docs.htmlvitess.io/­docs
DeveloperAmazon (based on PostgreSQL)Apache Software Foundation infoApache top-level project, originally developed by ClouderaThe Linux Foundation, PlanetScale
Initial release201220132013
Current release4.1.0, June 202215.0.2, December 2022
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2Open Source infoApache Version 2.0, commercial licenses available
Cloud-based only infoOnly available as a cloud serviceyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageCC++Go
Server operating systemshostedLinuxDocker
Linux
macOS
Data schemeyesyesyes
Typing infopredefined data types such as float or dateyesyesyes
XML support infoSome form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.nono
Secondary indexesrestrictedyesyes
SQL infoSupport of SQLyes infodoes not fully support an SQL-standardSQL-like DML and DDL statementsyes infowith proprietary extensions
APIs and other access methodsJDBC
ODBC
JDBC
ODBC
ADO.NET
JDBC
MySQL protocol
ODBC
Supported programming languagesAll languages supporting JDBC/ODBCAll languages supporting JDBC/ODBCAda
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl
Server-side scripts infoStored proceduresuser defined functions infoin Pythonyes infouser defined functions and integration of map-reduceyes infoproprietary syntax
Triggersnonoyes
Partitioning methods infoMethods for storing different data on different nodesShardingShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyesselectable replication factorMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infoquery execution via MapReduceno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual ConsistencyEventual Consistency across shards
Immediate Consistency within a shard
Foreign keys infoReferential integrityyes infoinformational only, not enforced by the systemnoyes infonot for MyISAM storage engine
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnoACID at shard level
Concurrency infoSupport for concurrent manipulation of datayesyesyes infotable locks or row locks depending on storage engine
Durability infoSupport for making data persistentyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnoyes
User concepts infoAccess controlfine grained access rights according to SQL-standardAccess rights for users, groups and roles infobased on Apache Sentry and KerberosUsers with fine-grained authorization concept infono user groups or roles

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.

Related products and services
3rd partiesCData: Connect to Big Data & NoSQL through standard Drivers.
» more

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources
Amazon RedshiftApache ImpalaVitess
DB-Engines blog posts

Cloud-based DBMS's popularity grows at high rates
12 December 2019, Paul Andlinger

The popularity of cloud-based DBMSs has increased tenfold in four years
7 February 2017, Matthias Gelbmann

Increased popularity for consuming DBMS services out of the cloud
2 October 2015, Paul Andlinger

show all

Recent citations in the news

Power analytics as a service capabilities using Amazon Redshift | Amazon Web Services
17 April 2024, AWS Blog

Handle tables without primary keys while creating Amazon Aurora PostgreSQL zero-ETL integrations with Amazon ...
18 April 2024, AWS Blog

Explore data with ease: Use SQL and Text-to-SQL in Amazon SageMaker Studio JupyterLab notebooks | Amazon Web ...
16 April 2024, AWS Blog

How Aura from Unity revolutionized their big data pipeline with Amazon Redshift Serverless | Amazon Web Services
4 April 2024, AWS Blog

Amazon Redshift adds new AI capabilities, including Amazon Q, to boost efficiency and productivity | Amazon Web ...
29 November 2023, AWS Blog

provided by Google News

Apache Impala becomes Top-Level Project
28 November 2017, SDTimes.com

Cloudera Bringing Impala to AWS Cloud
28 November 2017, Datanami

Apache Doris just 'graduated': Why care about this SQL data warehouse
24 June 2022, InfoWorld

Hudi: Uber Engineering’s Incremental Processing Framework on Apache Hadoop
12 March 2017, Uber

Updates & Upserts in Hadoop Ecosystem with Apache Kudu
27 October 2017, KDnuggets

provided by Google News

Massively Scaling MySQL Using Vitess
19 February 2019, InfoQ.com

PlanetScale grabs YouTube-developed open-source tech, promises Vitess DBaaS with on-the-fly schema changes
18 May 2021, The Register

With Vitess 4.0, database vendor matures cloud-native platform
13 November 2019, TechTarget

They scaled YouTube — now they’ll shard everyone with PlanetScale
13 December 2018, TechCrunch

PlanetScale Unveils Distributed MySQL Database Service Based on Vitess
18 May 2021, Datanami

provided by Google News



Share this page

Featured Products

Datastax Astra logo

Bring all your data to Generative AI applications with vector search enabled by the most scalable
vector database available.
Try for Free

SingleStore logo

Database for your real-time AI and Analytics Apps.
Try it today.

Neo4j logo

See for yourself how a graph database can make your life easier.
Use Neo4j online for free.

Milvus logo

Vector database designed for GenAI, fully equipped for enterprise implementation.
Try Managed Milvus for Free

Ontotext logo

GraphDB allows you to link diverse data, index it for semantic search and enrich it via text analysis to build big knowledge graphs. Get it free.

Present your product here