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

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

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Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonBrytlyt  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonTimescaleDB  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.A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL
Primary database modelRelational DBMSRelational DBMSKey-value store
Wide column store
Time Series DBMS
Secondary database modelsDocument storeRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score14.03
Rank#40  Overall
#24  Relational DBMS
Score0.38
Rank#279  Overall
#126  Relational DBMS
Score3.58
Rank#92  Overall
#14  Key-value stores
#8  Wide column stores
Score4.87
Rank#74  Overall
#4  Time Series DBMS
Websiteimpala.apache.orgbrytlyt.iocloud.google.com/­bigtablewww.timescale.com
Technical documentationimpala.apache.org/­impala-docs.htmldocs.brytlyt.iocloud.google.com/­bigtable/­docsdocs.timescale.com
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaBrytlytGoogleTimescale
Initial release2013201620152017
Current release4.1.0, June 20225.0, August 20232.13.0, November 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2commercialcommercialOpen Source infoApache 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
Server operating systemsLinuxLinux
OS X
Windows
hostedLinux
OS X
Windows
Data schemeyesyesschema-freeyes
Typing infopredefined data types such as float or dateyesyesnonumerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data 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.noyes
Secondary indexesyesyesnoyes
SQL infoSupport of SQLSQL-like DML and DDL statementsyesnoyes infofull PostgreSQL SQL syntax
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)
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
Supported programming languagesAll languages supporting JDBC/ODBC.Net
C
C++
Delphi
Java
Perl
Python
Tcl
C#
C++
Go
Java
JavaScript (Node.js)
Python
.Net
C
C++
Delphi
Java infoJDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduceuser defined functions infoin PL/pgSQLnouser defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell
Triggersnoyesnoyes
Partitioning methods infoMethods for storing different data on different nodesShardingShardingyes, across time and space (hash partitioning) attributes
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 zonesSource-replica replication with hot standby and reads on replicas info
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)fine grained access rights according to SQL-standard

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More resources
Apache ImpalaBrytlytGoogle Cloud BigtableTimescaleDB
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