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DBMS > Apache Impala vs. Ignite vs. Vertica

System Properties Comparison Apache Impala vs. Ignite vs. Vertica

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Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonIgnite  Xexclude from comparisonVertica infoOpenText™ Vertica™  Xexclude from comparison
DescriptionAnalytic DBMS for HadoopApache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale.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 modelRelational DBMSKey-value store
Relational DBMS
Relational DBMS infoColumn oriented
Secondary database modelsDocument storeSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score13.77
Rank#40  Overall
#24  Relational DBMS
Score3.16
Rank#96  Overall
#15  Key-value stores
#49  Relational DBMS
Score10.68
Rank#43  Overall
#27  Relational DBMS
Websiteimpala.apache.orgignite.apache.orgwww.vertica.com
Technical documentationimpala.apache.org/­impala-docs.htmlapacheignite.readme.io/­docsvertica.com/­documentation
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaApache Software FoundationOpenText infopreviously Micro Focus and Hewlett Packard
Initial release201320152005
Current release4.1.0, June 2022Apache Ignite 2.612.0.3, January 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2Open Source infoApache 2.0commercial infoLimited community edition free
Cloud-based only infoOnly available as a cloud servicenonono infoon-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++C++, Java, .NetC++
Server operating systemsLinuxLinux
OS X
Solaris
Windows
Linux
Data schemeyesyesYes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.
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.noyesno
Secondary indexesyesyesNo Indexes Required. Different internal optimization strategy, but same functionality included.
SQL infoSupport of SQLSQL-like DML and DDL statementsANSI-99 for query and DML statements, subset of DDLFull 1999 standard plus machine learning, time series and geospatial. Over 650 functions.
APIs and other access methodsJDBC
ODBC
HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data
ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL infocharacter-based, interactive, front-end utility
Supported programming languagesAll languages supporting JDBC/ODBCC#
C++
Java
PHP
Python
Ruby
Scala
C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduceyes (compute grid and cache interceptors can be used instead)yes, PostgreSQL PL/pgSQL, with minor differences
Triggersnoyes (cache interceptors and events)yes, called Custom Alerts
Partitioning methods infoMethods for storing different data on different nodesShardingShardinghorizontal partitioning, hierarchical partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factoryes (replicated cache)Multi-source replication infoOne, or more copies of data replicated across nodes, or object-store used for repository.
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReduceyes (compute grid and hadoop accelerator)no infoBi-directional Spark integration
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes
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.noyesno
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and KerberosSecurity Hooks for custom implementationsfine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash
More information provided by the system vendor
Apache ImpalaIgniteVertica infoOpenText™ Vertica™
Specific characteristicsDeploy-anywhere database for large-scale analytical deployments. Deploy off-cloud,...
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Competitive advantagesFast, scalable, and capable of high concurrency. Separation of compute/storage leverages...
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Typical application scenariosCommunication and network analytics, Embedded analytics, Fraud monitoring and Risk...
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Key customersAbiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
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Licensing and pricing modelsCost-based models and subscription-based models are both available. One license is...
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More resources
Apache ImpalaIgniteVertica infoOpenText™ Vertica™
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