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DBMS > Apache Drill vs. Google Cloud Bigtable vs. Microsoft Azure Data Explorer vs. Oracle Berkeley DB vs. TerarkDB

System Properties Comparison Apache Drill vs. Google Cloud Bigtable vs. Microsoft Azure Data Explorer vs. Oracle Berkeley DB vs. TerarkDB

Editorial information provided by DB-Engines
NameApache Drill  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonOracle Berkeley DB  Xexclude from comparisonTerarkDB  Xexclude from comparison
DescriptionSchema-free SQL Query Engine for Hadoop, NoSQL and Cloud StorageGoogle's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.Fully managed big data interactive analytics platformWidely used in-process key-value storeA key-value store forked from RocksDB with advanced compression algorithms. It can be used standalone or as a storage engine for MySQL and MongoDB
Primary database modelDocument store
Relational DBMS
Key-value store
Wide column store
Relational DBMS infocolumn orientedKey-value store infosupports sorted and unsorted key sets
Native XML DBMS infoin the Oracle Berkeley DB XML version
Key-value store
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
Score2.02
Rank#124  Overall
#22  Document stores
#59  Relational DBMS
Score3.15
Rank#95  Overall
#14  Key-value stores
#8  Wide column stores
Score3.80
Rank#81  Overall
#43  Relational DBMS
Score2.01
Rank#126  Overall
#21  Key-value stores
#3  Native XML DBMS
Score0.08
Rank#367  Overall
#56  Key-value stores
Websitedrill.apache.orgcloud.google.com/­bigtableazure.microsoft.com/­services/­data-explorerwww.oracle.com/­database/­technologies/­related/­berkeleydb.htmlgithub.com/­bytedance/­terarkdb
Technical documentationdrill.apache.org/­docscloud.google.com/­bigtable/­docsdocs.microsoft.com/­en-us/­azure/­data-explorerdocs.oracle.com/­cd/­E17076_05/­html/­index.htmlbytedance.larkoffice.com/­docs/­doccnZmYFqHBm06BbvYgjsHHcKc
DeveloperApache Software FoundationGoogleMicrosoftOracle infooriginally developed by Sleepycat, which was acquired by OracleByteDance, originally Terark
Initial release20122015201919942016
Current release1.20.3, January 2023cloud service with continuous releases18.1.40, May 2020
License infoCommercial or Open SourceOpen Source infoApache Version 2commercialcommercialOpen Source infocommercial license availablecommercial inforestricted open source version available
Cloud-based only infoOnly available as a cloud servicenoyesyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC, Java, C++ (depending on the Berkeley DB edition)C++
Server operating systemsLinux
OS X
Windows
hostedhostedAIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows
Data schemeschema-freeschema-freeFixed schema with schema-less datatypes (dynamic)schema-freeschema-free
Typing infopredefined data types such as float or dateyesnoyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesnono
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.nonoyesyes infoonly with the Berkeley DB XML editionno
Secondary indexesnonoall fields are automatically indexedyesno
SQL infoSupport of SQLSQL SELECT statement is SQL:2003 compliantnoKusto Query Language (KQL), SQL subsetyes infoSQL interfaced based on SQLite is availableno
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
C++ API
Java API
Supported programming languagesC++C#
C++
Go
Java
JavaScript (Node.js)
Python
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
.Net infoFigaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others infoThird-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js) info3rd party binding
Perl
Python
Tcl
C++
Java
Server-side scripts infoStored proceduresuser defined functionsnoYes, possible languages: KQL, Python, Rnono
Triggersnonoyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyyes infoonly for the SQL APIno
Partitioning methods infoMethods for storing different data on different nodesShardingShardingSharding infoImplicit feature of the cloud servicenonenone
Replication methods infoMethods for redundantly storing data on multiple nodesInternal replication in Colossus, and regional replication between two clusters in different zonesyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.Source-replica replicationnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesyesSpark connector (open source): github.com/­Azure/­azure-kusto-sparknono
Consistency concepts infoMethods to ensure consistency in a distributed systemnoneImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)Eventual Consistency
Immediate Consistency
Foreign keys infoReferential integritynonononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoAtomic single-row operationsnoACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentDepending on the underlying data sourceyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.Depending on the underlying data sourcenonoyesyes
User concepts infoAccess controlDepending on the underlying data sourceAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Azure Active Directory Authenticationnono

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More resources
Apache DrillGoogle Cloud BigtableMicrosoft Azure Data ExplorerOracle Berkeley DBTerarkDB
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