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DBMS > Databricks vs. etcd vs. Microsoft Azure Data Explorer

System Properties Comparison Databricks vs. etcd vs. Microsoft Azure Data Explorer

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Editorial information provided by DB-Engines
NameDatabricks  Xexclude from comparisonetcd  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparison
DescriptionThe Databricks Lakehouse Platform combines elements of data lakes and data warehouses to provide a unified view onto structured and unstructured data. It is based on Apache Spark.A distributed reliable key-value storeFully managed big data interactive analytics platform
Primary database modelDocument store
Relational DBMS
Key-value storeRelational 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
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score74.34
Rank#17  Overall
#3  Document stores
#11  Relational DBMS
Score7.66
Rank#55  Overall
#6  Key-value stores
Score5.82
Rank#65  Overall
#37  Relational DBMS
Websitewww.databricks.cometcd.io
github.com/­etcd-io/­etcd
azure.microsoft.com/­services/­data-explorer
Technical documentationdocs.databricks.cometcd.io/­docs
github.com/­etcd-io/­etcd/­tree/­master/­Documentation
docs.microsoft.com/­en-us/­azure/­data-explorer
DeveloperDatabricksMicrosoft
Initial release20132019
Current release3.4, August 2019cloud service with continuous releases
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2.0commercial
Cloud-based only infoOnly available as a cloud serviceyesnoyes
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageGo
Server operating systemshostedFreeBSD
Linux
Windows infoexperimental
hosted
Data schemeFlexible Schema (defined schema, partial schema, schema free)schema-freeFixed schema with schema-less datatypes (dynamic)
Typing infopredefined data types such as float or datenoyes 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.yesnoyes
Secondary indexesyesnoall fields are automatically indexed
SQL infoSupport of SQLwith Databricks SQLnoKusto Query Language (KQL), SQL subset
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
gRPC
JSON over HTTP
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Supported programming languagesPython
R
Scala
.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Rust
Scala
Tcl
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
Server-side scripts infoStored proceduresuser defined functions and aggregatesnoYes, possible languages: KQL, Python, R
Triggersyes, watching key changesyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicy
Partitioning methods infoMethods for storing different data on different nodesSharding infoImplicit feature of the cloud service
Replication methods infoMethods for redundantly storing data on multiple nodesyesUsing Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.yes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoSpark connector (open source): github.com/­Azure/­azure-kusto-spark
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyEventual Consistency
Immediate Consistency
Foreign keys infoReferential integritynono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnono
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonono
User concepts infoAccess controlnoAzure Active Directory Authentication
More information provided by the system vendor
DatabricksetcdMicrosoft Azure Data Explorer
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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More resources
DatabricksetcdMicrosoft Azure Data Explorer
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