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DBMS > Apache Drill vs. Apache Impala vs. Netezza vs. Vertica

System Properties Comparison Apache Drill vs. Apache Impala vs. Netezza vs. Vertica

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Editorial information provided by DB-Engines
NameApache Drill  Xexclude from comparisonApache Impala  Xexclude from comparisonNetezza infoAlso called PureData System for Analytics by IBM  Xexclude from comparisonVertica infoOpenText™ Vertica™  Xexclude from comparison
DescriptionSchema-free SQL Query Engine for Hadoop, NoSQL and Cloud StorageAnalytic DBMS for HadoopData warehouse and analytics appliance part of IBM PureSystemsCloud 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 modelDocument store
Relational DBMS
Relational DBMSRelational DBMSRelational DBMS infoColumn oriented
Secondary database modelsDocument storeSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.95
Rank#127  Overall
#23  Document stores
#60  Relational DBMS
Score13.77
Rank#40  Overall
#24  Relational DBMS
Score9.06
Rank#46  Overall
#29  Relational DBMS
Score10.68
Rank#43  Overall
#27  Relational DBMS
Websitedrill.apache.orgimpala.apache.orgwww.ibm.com/­products/­netezzawww.vertica.com
Technical documentationdrill.apache.org/­docsimpala.apache.org/­impala-docs.htmlvertica.com/­documentation
DeveloperApache Software FoundationApache Software Foundation infoApache top-level project, originally developed by ClouderaIBMOpenText infopreviously Micro Focus and Hewlett Packard
Initial release2012201320002005
Current release1.20.3, January 20234.1.0, June 202212.0.3, January 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2Open Source infoApache Version 2commercialcommercial infoLimited community edition free
Cloud-based only infoOnly available as a cloud servicenononono 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++
Server operating systemsLinux
OS X
Windows
LinuxLinux infoincluded in applianceLinux
Data schemeschema-freeyesyesYes, 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 dateyesyesyesyes
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.nonono
Secondary indexesnoyesyesNo Indexes Required. Different internal optimization strategy, but same functionality included.
SQL infoSupport of SQLSQL SELECT statement is SQL:2003 compliantSQL-like DML and DDL statementsyesFull 1999 standard plus machine learning, time series and geospatial. Over 650 functions.
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
JDBC
ODBC
JDBC
ODBC
OLE DB
ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL infocharacter-based, interactive, front-end utility
Supported programming languagesC++All languages supporting JDBC/ODBCC
C++
Fortran
Java
Lua
Perl
Python
R
C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Server-side scripts infoStored proceduresuser defined functionsyes infouser defined functions and integration of map-reduceyesyes, PostgreSQL PL/pgSQL, with minor differences
Triggersnononoyes, called Custom Alerts
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardinghorizontal partitioning, hierarchical partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorSource-replica replicationMulti-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 methodsyesyes infoquery execution via MapReduceyesno infoBi-directional Spark integration
Consistency concepts infoMethods to ensure consistency in a distributed systemnoneEventual ConsistencyImmediate Consistency
Foreign keys infoReferential integritynononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentDepending on the underlying data sourceyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.Depending on the underlying data sourcenono
User concepts infoAccess controlDepending on the underlying data sourceAccess rights for users, groups and roles infobased on Apache Sentry and KerberosUsers with fine-grained authorization conceptfine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash
More information provided by the system vendor
Apache DrillApache ImpalaNetezza infoAlso called PureData System for Analytics by IBMVertica 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 DrillApache ImpalaNetezza infoAlso called PureData System for Analytics by IBMVertica infoOpenText™ Vertica™
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