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DBMS > Google Cloud Firestore vs. LMDB vs. Microsoft Azure Synapse Analytics vs. Splice Machine vs. TimescaleDB

System Properties Comparison Google Cloud Firestore vs. LMDB vs. Microsoft Azure Synapse Analytics vs. Splice Machine vs. TimescaleDB

Editorial information provided by DB-Engines
NameGoogle Cloud Firestore  Xexclude from comparisonLMDB  Xexclude from comparisonMicrosoft Azure Synapse Analytics infopreviously named Azure SQL Data Warehouse  Xexclude from comparisonSplice Machine  Xexclude from comparisonTimescaleDB  Xexclude from comparison
DescriptionCloud Firestore is an auto-scaling document database for storing, syncing, and querying data for mobile and web apps. It offers seamless integration with other Firebase and Google Cloud Platform products.A high performant, light-weight, embedded key-value database libraryElastic, large scale data warehouse service leveraging the broad eco-system of SQL ServerOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and SparkA time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL
Primary database modelDocument storeKey-value storeRelational DBMSRelational DBMSTime Series DBMS
Secondary database modelsRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score7.85
Rank#51  Overall
#8  Document stores
Score1.99
Rank#125  Overall
#21  Key-value stores
Score20.56
Rank#31  Overall
#19  Relational DBMS
Score0.54
Rank#250  Overall
#114  Relational DBMS
Score4.64
Rank#71  Overall
#4  Time Series DBMS
Websitefirebase.google.com/­products/­firestorewww.symas.com/­symas-embedded-database-lmdbazure.microsoft.com/­services/­synapse-analyticssplicemachine.comwww.timescale.com
Technical documentationfirebase.google.com/­docs/­firestorewww.lmdb.tech/­docdocs.microsoft.com/­azure/­synapse-analyticssplicemachine.com/­how-it-worksdocs.timescale.com
DeveloperGoogleSymasMicrosoftSplice MachineTimescale
Initial release20172011201620142017
Current release0.9.32, January 20243.1, March 20212.15.0, May 2024
License infoCommercial or Open SourcecommercialOpen SourcecommercialOpen Source infoAGPL 3.0, commercial license availableOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud serviceyesnoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageCC++JavaC
Server operating systemshostedLinux
Unix
Windows
hostedLinux
OS X
Solaris
Windows
Linux
OS X
Windows
Data schemeschema-freeschema-freeyesyesyes
Typing infopredefined data types such as float or dateyesyesyesnumerics, 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.nononoyes
Secondary indexesyesnoyesyesyes
SQL infoSupport of SQLnonoyesyesyes infofull PostgreSQL SQL syntax
APIs and other access methodsAndroid
gRPC (using protocol buffers) API
iOS
JavaScript API
RESTful HTTP API
ADO.NET
JDBC
ODBC
JDBC
Native Spark Datasource
ODBC
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
Supported programming languagesGo
Java
JavaScript
JavaScript (Node.js)
Objective-C
Python
.Net
C
C++
Clojure
Go
Haskell
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Nim
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Rust
Swift
Tcl
C#
Java
PHP
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
.Net
C
C++
Delphi
Java infoJDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl
Server-side scripts infoStored proceduresyes, Firebase Rules & Cloud FunctionsnoTransact SQLyes infoJavauser defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell
Triggersyes, with Cloud Functionsnonoyesyes
Partitioning methods infoMethods for storing different data on different nodesShardingnoneSharding, horizontal partitioningShared Nothhing Auto-Sharding, Columnar Partitioningyes, across time and space (hash partitioning) attributes
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replicationnoneyesMulti-source replication
Source-replica replication
Source-replica replication with hot standby and reads on replicas info
MapReduce infoOffers an API for user-defined Map/Reduce methodsUsing Cloud DataflownonoYes, via Full Spark Integrationno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonono infodocs.microsoft.com/­en-us/­azure/­synapse-analytics/­sql-data-warehouse/­sql-data-warehouse-table-constraintsyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datayesACIDACIDACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes, multi-version concurrency control (MVCC)yes
Durability infoSupport for making data persistentyesyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyesno
User concepts infoAccess controlAccess rights for users, groups and roles based on Google Cloud Identity and Access Management. Security Rules for 3rd party authentication using Firebase Auth.noyesAccess rights for users, groups and roles according to SQL-standardfine grained access rights according to SQL-standard

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More resources
Google Cloud FirestoreLMDBMicrosoft Azure Synapse Analytics infopreviously named Azure SQL Data WarehouseSplice MachineTimescaleDB
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