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DBMS > GeoMesa vs. Google Cloud Datastore vs. HarperDB vs. Microsoft Azure Synapse Analytics vs. WakandaDB

System Properties Comparison GeoMesa vs. Google Cloud Datastore vs. HarperDB vs. Microsoft Azure Synapse Analytics vs. WakandaDB

Editorial information provided by DB-Engines
NameGeoMesa  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonHarperDB  Xexclude from comparisonMicrosoft Azure Synapse Analytics infopreviously named Azure SQL Data Warehouse  Xexclude from comparisonWakandaDB  Xexclude from comparison
DescriptionGeoMesa is a distributed spatio-temporal DBMS based on various systems as storage layer.Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformUltra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.Elastic, large scale data warehouse service leveraging the broad eco-system of SQL ServerWakandaDB is embedded in a server that provides a REST API and a server-side javascript engine to access data
Primary database modelSpatial DBMSDocument storeDocument storeRelational DBMSObject oriented DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.78
Rank#213  Overall
#4  Spatial DBMS
Score4.47
Rank#76  Overall
#12  Document stores
Score0.55
Rank#248  Overall
#38  Document stores
Score20.56
Rank#31  Overall
#19  Relational DBMS
Score0.03
Rank#364  Overall
#17  Object oriented DBMS
Websitewww.geomesa.orgcloud.google.com/­datastorewww.harperdb.ioazure.microsoft.com/­services/­synapse-analyticswakanda.github.io
Technical documentationwww.geomesa.org/­documentation/­stable/­user/­index.htmlcloud.google.com/­datastore/­docsdocs.harperdb.io/­docsdocs.microsoft.com/­azure/­synapse-analyticswakanda.github.io/­doc
DeveloperCCRi and othersGoogleHarperDBMicrosoftWakanda SAS
Initial release20142008201720162012
Current release5.0.0, May 20243.1, August 20212.7.0 (AprilĀ 29, 2019), April 2019
License infoCommercial or Open SourceOpen Source infoApache License 2.0commercialcommercial infofree community edition availablecommercialOpen Source infoAGPLv3, extended commercial license available
Cloud-based only infoOnly available as a cloud servicenoyesnoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageScalaNode.jsC++C++, JavaScript
Server operating systemshostedLinux
OS X
hostedLinux
OS X
Windows
Data schemeyesschema-freedynamic schemayesyes
Typing infopredefined data types such as float or dateyesyes, details hereyes infoJSON data typesyesyes
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.nonononono
Secondary indexesyesyesyesyes
SQL infoSupport of SQLnoSQL-like query language (GQL)SQL-like data manipulation statementsyesno
APIs and other access methodsgRPC (using protocol buffers) API
RESTful HTTP/JSON API
JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket
ADO.NET
JDBC
ODBC
RESTful HTTP API
Supported programming languages.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift
C#
Java
PHP
JavaScript
Server-side scripts infoStored proceduresnousing Google App EngineCustom Functions infosince release 3.1Transact SQLyes
TriggersnoCallbacks using the Google Apps Enginenonoyes
Partitioning methods infoMethods for storing different data on different nodesdepending on storage layerShardingA table resides as a whole on one (or more) nodes in a clusterSharding, horizontal partitioningnone
Replication methods infoMethods for redundantly storing data on multiple nodesdepending on storage layerMulti-source replication using Paxosyes infothe nodes on which a table resides can be definedyesnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesyes infousing Google Cloud Dataflownonono
Consistency concepts infoMethods to ensure consistency in a distributed systemdepending on storage layerImmediate Consistency or Eventual Consistency depending on type of query and configuration infoStrong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.Immediate ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynoyes infovia ReferenceProperties or Ancestor pathsnono infodocs.microsoft.com/­en-us/­azure/­synapse-analytics/­sql-data-warehouse/­sql-data-warehouse-table-constraints
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsAtomic execution of specific operationsACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyes, using LMDByesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.depending on storage layernoyesno
User concepts infoAccess controlyes infodepending on the DBMS used for storageAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Access rights for users and rolesyesyes

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More resources
GeoMesaGoogle Cloud DatastoreHarperDBMicrosoft Azure Synapse Analytics infopreviously named Azure SQL Data WarehouseWakandaDB
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