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DBMS > Amazon CloudSearch vs. Apache Impala vs. CrateDB vs. EsgynDB vs. Microsoft Azure Data Explorer

System Properties Comparison Amazon CloudSearch vs. Apache Impala vs. CrateDB vs. EsgynDB vs. Microsoft Azure Data Explorer

Editorial information provided by DB-Engines
NameAmazon CloudSearch  Xexclude from comparisonApache Impala  Xexclude from comparisonCrateDB  Xexclude from comparisonEsgynDB  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparison
DescriptionA hosted search engine service by Amazon with the data stored in Amazons cloudAnalytic DBMS for HadoopDistributed Database based on LuceneEnterprise-class SQL-on-Hadoop solution, powered by Apache TrafodionFully managed big data interactive analytics platform
Primary database modelSearch engineRelational DBMSDocument store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS
Relational DBMSRelational DBMS infocolumn oriented
Secondary database modelsDocument storeRelational DBMSDocument store infoIf a column is of type dynamic docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-types/­dynamic then it's possible to add arbitrary JSON documents in this cell
Event Store infothis is the general usage pattern at Microsoft. Billing, Logs, Telemetry events are stored in ADX and the state of an individual entity is defined by the arg_max(timestamps)
Spatial DBMS
Search engine infosupport for complex search expressions docs.microsoft.com/­en-us/­azure/­kusto/­query/­parseoperator FTS, Geospatial docs.microsoft.com/­en-us/­azure/­kusto/­query/­geo-point-to-geohash-function distributed search -> ADX acts as a distributed search engine
Time Series DBMS infosee docs.microsoft.com/­en-us/­azure/­data-explorer/­time-series-analysis
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.81
Rank#137  Overall
#12  Search engines
Score12.45
Rank#40  Overall
#24  Relational DBMS
Score0.71
Rank#227  Overall
#37  Document stores
#5  Spatial DBMS
#16  Search engines
#19  Time Series DBMS
#8  Vector DBMS
Score0.25
Rank#312  Overall
#138  Relational DBMS
Score3.80
Rank#81  Overall
#43  Relational DBMS
Websiteaws.amazon.com/­cloudsearchimpala.apache.orgcratedb.comwww.esgyn.cnazure.microsoft.com/­services/­data-explorer
Technical documentationdocs.aws.amazon.com/­cloudsearchimpala.apache.org/­impala-docs.htmlcratedb.com/­docsdocs.microsoft.com/­en-us/­azure/­data-explorer
DeveloperAmazonApache Software Foundation infoApache top-level project, originally developed by ClouderaCrateEsgynMicrosoft
Initial release20122013201320152019
Current release4.1.0, June 2022cloud service with continuous releases
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2Open Sourcecommercialcommercial
Cloud-based only infoOnly available as a cloud serviceyesnononoyes
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
CrateDB Cloud: a distributed SQL database that spreads data and processing across an elastic cluster of shared nothing nodes. CrateDB Cloud enables data insights at scale on Microsoft Azure, AWS and Google Cloud Platform.
Implementation languageC++JavaC++, Java
Server operating systemshostedLinuxAll Operating Systems, including Kubernetes with CrateDB Kubernetes Operator supportLinuxhosted
Data schemeyesyesFlexible Schema (defined schema, partial schema, schema free)yesFixed schema with schema-less datatypes (dynamic)
Typing infopredefined data types such as float or dateyesyesyesyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-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 indexesyes infoall search fields are automatically indexedyesyesyesall fields are automatically indexed
SQL infoSupport of SQLnoSQL-like DML and DDL statementsyes, but no triggers and constraints, and PostgreSQL compatibilityyesKusto Query Language (KQL), SQL subset
APIs and other access methodsHTTP APIJDBC
ODBC
ADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API
ADO.NET
JDBC
ODBC
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Supported programming languagesAll languages supporting JDBC/ODBC.NET
Erlang
Go infocommunity maintained client
Java
JavaScript (Node.js) infocommunity maintained client
Perl infocommunity maintained client
PHP
Python
R
Ruby infocommunity maintained client
Scala infocommunity maintained client
All languages supporting JDBC/ODBC/ADO.Net.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
Server-side scripts infoStored proceduresnoyes infouser defined functions and integration of map-reduceuser defined functions (Javascript)Java Stored ProceduresYes, possible languages: KQL, Python, R
Triggersnonononoyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicy
Partitioning methods infoMethods for storing different data on different nodesyes infoautomatic partitioning across Amazon Search Instance as requiredShardingShardingShardingSharding infoImplicit feature of the cloud service
Replication methods infoMethods for redundantly storing data on multiple nodesyes infomanaged transparently by AWSselectable replication factorConfigurable replication on table/partition-levelMulti-source replication between multi datacentersyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infoquery execution via MapReducenoyesSpark connector (open source): github.com/­Azure/­azure-kusto-spark
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyEventual Consistency
Read-after-write consistency on record level
Immediate ConsistencyEventual Consistency
Immediate Consistency
Foreign keys infoReferential integritynononoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonono infounique row identifiers can be used for implementing an optimistic concurrency control strategyACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
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.nononono
User concepts infoAccess controlauthentication via encrypted signaturesAccess rights for users, groups and roles infobased on Apache Sentry and Kerberosrights management via user accountsfine grained access rights according to SQL-standardAzure Active Directory Authentication
More information provided by the system vendor
Amazon CloudSearchApache ImpalaCrateDBEsgynDBMicrosoft Azure Data Explorer
Specific characteristicsThe enterprise database for time series, documents, and vectors. Distributed - Native...
» more
Competitive advantagesResponse time in milliseconds: e ven for complex ad-hoc queries. Massive scaling...
» more
Typical application scenarios​ IoT: accelerate your IIoT projects with CrateDB, delivering real-time analytics...
» more
Key customersAcross all continents, CrateDB is used by companies of all sizes to meet the most...
» more
Market metricsThe CrateDB open source project was started in 2013 Honorable Mention in 2021 Gartner®...
» more
Licensing and pricing modelsSee CrateDB pricing >
» more

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More resources
Amazon CloudSearchApache ImpalaCrateDBEsgynDBMicrosoft Azure Data Explorer
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