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DBMS > Apache Impala vs. Brytlyt vs. Google Cloud Bigtable vs. ObjectBox

System Properties Comparison Apache Impala vs. Brytlyt vs. Google Cloud Bigtable vs. ObjectBox

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Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonBrytlyt  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonObjectBox  Xexclude from comparison
DescriptionAnalytic DBMS for HadoopScalable GPU-accelerated RDBMS for very fast analytic and streaming workloads, leveraging PostgreSQLGoogle's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.Lightweight, fast on-device database for IoT, Mobile and Embedded devices, persisting and synchronising objects and vectors
Primary database modelRelational DBMSRelational DBMSKey-value store
Wide column store
Object oriented DBMS
Vector DBMS
Secondary database modelsDocument storeTime Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score10.63
Rank#40  Overall
#24  Relational DBMS
Score0.27
Rank#292  Overall
#132  Relational DBMS
Score2.97
Rank#92  Overall
#15  Key-value stores
#8  Wide column stores
Score1.08
Rank#179  Overall
#6  Object oriented DBMS
#9  Vector DBMS
Websiteimpala.apache.orgbrytlyt.iocloud.google.com/­bigtablegithub.com/­objectbox
objectbox.io
Technical documentationimpala.apache.org/­impala-docs.htmldocs.brytlyt.iocloud.google.com/­bigtable/­docsdocs.objectbox.io
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaBrytlytGoogleObjectBox Limited
Initial release2013201620152017
Current release4.1.0, June 20225.0, August 20234.0 (May 2024)
License infoCommercial or Open SourceOpen Source infoApache Version 2commercialcommercialBindings are released under Apache 2.0 infoApache License 2.0
Cloud-based only infoOnly available as a cloud servicenonoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++C, C++ and CUDAC and C++
Server operating systemsLinuxLinux
OS X
Windows
hostedAndroid
Any POSIX system
Docker
iOS
Linux
macOS
QNX
Windows
Data schemeyesyesschema-freeyes
Typing infopredefined data types such as float or dateyesyesnoyes, plus "flex" map-like types
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.noyes infospecific XML-type available, but no XML query functionality.nono
Secondary indexesyesyesnoyes
SQL infoSupport of SQLSQL-like DML and DDL statementsyesnono
APIs and other access methodsJDBC
ODBC
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
Proprietary native API
Supported programming languagesAll languages supporting JDBC/ODBC.Net
C
C++
Delphi
Java
Perl
Python
Tcl
C#
C++
Go
Java
JavaScript (Node.js)
Python
C
C++
Dart (Flutter)
Go
Java
Kotlin
Python
Swift
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduceuser defined functions infoin PL/pgSQLnono
Triggersnoyesnono
Partitioning methods infoMethods for storing different data on different nodesShardingShardingnone
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorSource-replica replicationInternal replication in Colossus, and regional replication between two clusters in different zonesData sync between devices allowing occasional connected databases to work completely offline
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReducenoyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate ConsistencyImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)Immediate Consistency
Foreign keys infoReferential integritynoyesnoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDAtomic single-row operationsACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonono
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and Kerberosfine grained access rights according to SQL-standardAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)yes
More information provided by the system vendor
Apache ImpalaBrytlytGoogle Cloud BigtableObjectBox
News

MongoDB Realm & Device Sync alternatives – ObjectBox
17 September 2024

Local AI – what it is and why we need it
11 September 2024

First on-device Vector Database (aka Semantic Index) for iOS
24 July 2024

Retrieval Augmented Generation (RAG) with vector databases: Expanding AI Capabilities
18 June 2024

The on-device Vector Database for Android and Java
29 May 2024

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More resources
Apache ImpalaBrytlytGoogle Cloud BigtableObjectBox
Recent citations in the news

Apache Impala 4 Supports Operator Multi-Threading
29 July 2021, iProgrammer

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

Cloudera Bringing Impala to AWS Cloud
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Google's AI-First Strategy Brings Vector Support To Cloud Databases
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