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DBMS > Amazon DocumentDB vs. Heroic vs. LevelDB vs. Microsoft Azure Data Explorer vs. Vertica

System Properties Comparison Amazon DocumentDB vs. Heroic vs. LevelDB vs. Microsoft Azure Data Explorer vs. Vertica

Editorial information provided by DB-Engines
NameAmazon DocumentDB  Xexclude from comparisonHeroic  Xexclude from comparisonLevelDB  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonVertica infoOpenText™ Vertica™  Xexclude from comparison
DescriptionFast, scalable, highly available, and fully managed MongoDB-compatible database serviceTime Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearchEmbeddable fast key-value storage library that provides an ordered mapping from string keys to string valuesFully managed big data interactive analytics platformCloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.
Primary database modelDocument storeTime Series DBMSKey-value storeRelational DBMS infocolumn orientedRelational DBMS infoColumn oriented
Secondary database modelsDocument 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
Spatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.91
Rank#132  Overall
#24  Document stores
Score0.51
Rank#255  Overall
#21  Time Series DBMS
Score2.35
Rank#111  Overall
#19  Key-value stores
Score4.38
Rank#77  Overall
#41  Relational DBMS
Score10.68
Rank#43  Overall
#27  Relational DBMS
Websiteaws.amazon.com/­documentdbgithub.com/­spotify/­heroicgithub.com/­google/­leveldbazure.microsoft.com/­services/­data-explorerwww.vertica.com
Technical documentationaws.amazon.com/­documentdb/­resourcesspotify.github.io/­heroicgithub.com/­google/­leveldb/­blob/­main/­doc/­index.mddocs.microsoft.com/­en-us/­azure/­data-explorervertica.com/­documentation
DeveloperSpotifyGoogleMicrosoftOpenText infopreviously Micro Focus and Hewlett Packard
Initial release20192014201120192005
Current release1.23, February 2021cloud service with continuous releases12.0.3, January 2023
License infoCommercial or Open SourcecommercialOpen Source infoApache 2.0Open Source infoBSDcommercialcommercial infoLimited community edition free
Cloud-based only infoOnly available as a cloud serviceyesnonoyesno infoon-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC++C++
Server operating systemshostedIllumos
Linux
OS X
Windows
hostedLinux
Data schemeschema-freeschema-freeschema-freeFixed schema with schema-less datatypes (dynamic)Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.
Typing infopredefined data types such as float or dateyesyesnoyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesyes
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.nononoyesno
Secondary indexesyesyes infovia Elasticsearchnoall fields are automatically indexedNo Indexes Required. Different internal optimization strategy, but same functionality included.
SQL infoSupport of SQLnononoKusto Query Language (KQL), SQL subsetFull 1999 standard plus machine learning, time series and geospatial. Over 650 functions.
APIs and other access methodsproprietary protocol using JSON (MongoDB compatible)HQL (Heroic Query Language, a JSON-based language)
HTTP API
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL infocharacter-based, interactive, front-end utility
Supported programming languagesGo
Java
JavaScript (Node.js)
PHP
Python
C++
Go
Java info3rd party binding
JavaScript (Node.js) info3rd party binding
Python info3rd party binding
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Server-side scripts infoStored proceduresnononoYes, possible languages: KQL, Python, Ryes, PostgreSQL PL/pgSQL, with minor differences
Triggersnononoyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyyes, called Custom Alerts
Partitioning methods infoMethods for storing different data on different nodesnoneShardingnoneSharding infoImplicit feature of the cloud servicehorizontal partitioning, hierarchical partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-availability zones for high availability, asynchronous replication for up to 15 read replicasyesnoneyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.Multi-source replication infoOne, or more copies of data replicated across nodes, or object-store used for repository.
MapReduce infoOffers an API for user-defined Map/Reduce methodsno infomay be implemented via Amazon Elastic MapReduce (Amazon EMR)nonoSpark connector (open source): github.com/­Azure/­azure-kusto-sparkno infoBi-directional Spark integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency
Immediate Consistency
Immediate ConsistencyEventual Consistency
Immediate Consistency
Immediate Consistency
Foreign keys infoReferential integrityno infotypically not used, however similar functionality with DBRef possiblenononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic single-document operationsnononoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyes infowith automatic compression on writesyesyes
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 and rolesnoAzure Active Directory Authenticationfine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash
More information provided by the system vendor
Amazon DocumentDBHeroicLevelDBMicrosoft Azure Data ExplorerVertica infoOpenText™ Vertica™
Specific characteristicsDeploy-anywhere database for large-scale analytical deployments. Deploy off-cloud,...
» more
Competitive advantagesFast, scalable, and capable of high concurrency. Separation of compute/storage leverages...
» more
Typical application scenariosCommunication and network analytics, Embedded analytics, Fraud monitoring and Risk...
» more
Key customersAbiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
» more
Licensing and pricing modelsCost-based models and subscription-based models are both available. One license is...
» more

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More resources
Amazon DocumentDBHeroicLevelDBMicrosoft Azure Data ExplorerVertica infoOpenText™ Vertica™
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