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DBMS > Apache Impala vs. Google Cloud Bigtable vs. HBase vs. OrigoDB

System Properties Comparison Apache Impala vs. Google Cloud Bigtable vs. HBase vs. OrigoDB

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Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonHBase  Xexclude from comparisonOrigoDB  Xexclude from comparison
DescriptionAnalytic DBMS for HadoopGoogle's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.Wide-column store based on Apache Hadoop and on concepts of BigTableA fully ACID in-memory object graph database
Primary database modelRelational DBMSKey-value store
Wide column store
Wide column storeDocument store
Object oriented DBMS
Secondary database modelsDocument store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score14.03
Rank#40  Overall
#24  Relational DBMS
Score3.58
Rank#92  Overall
#14  Key-value stores
#8  Wide column stores
Score31.25
Rank#26  Overall
#2  Wide column stores
Score0.03
Rank#378  Overall
#51  Document stores
#18  Object oriented DBMS
Websiteimpala.apache.orgcloud.google.com/­bigtablehbase.apache.orgorigodb.com
Technical documentationimpala.apache.org/­impala-docs.htmlcloud.google.com/­bigtable/­docshbase.apache.org/­book.htmlorigodb.com/­docs
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaGoogleApache Software Foundation infoApache top-level project, originally developed by PowersetRobert Friberg et al
Initial release2013201520082009 infounder the name LiveDB
Current release4.1.0, June 20222.3.4, January 2021
License infoCommercial or Open SourceOpen Source infoApache Version 2commercialOpen Source infoApache version 2Open Source
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++JavaC#
Server operating systemsLinuxhostedLinux
Unix
Windows infousing Cygwin
Linux
Windows
Data schemeyesschema-freeschema-free, schema definition possibleyes
Typing infopredefined data types such as float or dateyesnooptions to bring your own types, AVROUser defined using .NET types and collections
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.nononono infocan be achieved using .NET
Secondary indexesyesnonoyes
SQL infoSupport of SQLSQL-like DML and DDL statementsnonono
APIs and other access methodsJDBC
ODBC
gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
Java API
RESTful HTTP API
Thrift
.NET Client API
HTTP API
LINQ
Supported programming languagesAll languages supporting JDBC/ODBCC#
C++
Go
Java
JavaScript (Node.js)
Python
C
C#
C++
Groovy
Java
PHP
Python
Scala
.Net
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reducenoyes infoCoprocessors in Javayes
Triggersnonoyesyes infoDomain Events
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardinghorizontal partitioning infoclient side managed; servers are not synchronized
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorInternal replication in Colossus, and regional replication between two clusters in different zonesMulti-source replication
Source-replica replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReduceyesyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)Immediate Consistency or Eventual Consistency
Foreign keys infoReferential integritynononodepending on model
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoAtomic single-row operationsSingle row ACID (across millions of columns)ACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes infoWrite ahead log
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyesyes
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and KerberosAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABACRole based authorization

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More resources
Apache ImpalaGoogle Cloud BigtableHBaseOrigoDB
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