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DBMS > Apache Impala vs. Databricks vs. Google Cloud Bigtable vs. LokiJS vs. Microsoft Access

System Properties Comparison Apache Impala vs. Databricks vs. Google Cloud Bigtable vs. LokiJS vs. Microsoft Access

Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonDatabricks  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonLokiJS  Xexclude from comparisonMicrosoft Access  Xexclude from comparison
DescriptionAnalytic DBMS for HadoopThe 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.Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.In-memory JavaScript DBMSMicrosoft Access combines a backend RDBMS (JET / ACE Engine) with a GUI frontend for data manipulation and queries. infoThe Access frontend is often used for accessing other datasources (DBMS, Excel, etc.)
Primary database modelRelational DBMSDocument store
Relational DBMS
Key-value store
Wide column store
Document storeRelational DBMS
Secondary database modelsDocument store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score12.45
Rank#40  Overall
#24  Relational DBMS
Score81.08
Rank#15  Overall
#2  Document stores
#10  Relational DBMS
Score3.15
Rank#95  Overall
#14  Key-value stores
#8  Wide column stores
Score0.49
Rank#260  Overall
#42  Document stores
Score101.16
Rank#11  Overall
#8  Relational DBMS
Websiteimpala.apache.orgwww.databricks.comcloud.google.com/­bigtablegithub.com/­techfort/­LokiJSwww.microsoft.com/­en-us/­microsoft-365/­access
Technical documentationimpala.apache.org/­impala-docs.htmldocs.databricks.comcloud.google.com/­bigtable/­docstechfort.github.io/­LokiJSdeveloper.microsoft.com/­en-us/­access
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaDatabricksGoogleMicrosoft
Initial release20132013201520141992
Current release4.1.0, June 20221902 (16.0.11328.20222), March 2019
License infoCommercial or Open SourceOpen Source infoApache Version 2commercialcommercialOpen Sourcecommercial infoBundled with Microsoft Office
Cloud-based only infoOnly available as a cloud servicenoyesyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++JavaScriptC++
Server operating systemsLinuxhostedhostedserver-less, requires a JavaScript environment (browser, Node.js)Windows infoNot a real database server, but making use of DLLs
Data schemeyesFlexible Schema (defined schema, partial schema, schema free)schema-freeschema-freeyes
Typing infopredefined data types such as float or dateyesnonoyes
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.noyesnono
Secondary indexesyesyesnoyes infovia viewsyes
SQL infoSupport of SQLSQL-like DML and DDL statementswith Databricks SQLnonoyes infobut not compliant to any SQL standard
APIs and other access methodsJDBC
ODBC
JDBC
ODBC
RESTful HTTP API
gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
JavaScript APIADO.NET
DAO
ODBC
OLE DB
Supported programming languagesAll languages supporting JDBC/ODBCPython
R
Scala
C#
C++
Go
Java
JavaScript (Node.js)
Python
JavaScriptC
C#
C++
Delphi
Java (JDBC-ODBC)
VBA
Visual Basic.NET
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduceuser defined functions and aggregatesnoView functions in JavaScriptyes infosince Access 2010 using the ACE-engine
Triggersnonoyesyes infosince Access 2010 using the ACE-engine
Partitioning methods infoMethods for storing different data on different nodesShardingShardingnonenone
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factoryesInternal replication in Colossus, and regional replication between two clusters in different zonesnonenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReduceyesyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate ConsistencyImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)none
Foreign keys infoReferential integritynononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDAtomic single-row operationsno infoatomic operations within a single collection possibleACID infobut no files for transaction logging
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes infoSerialization of a DB in a Node.JS/Cordova/PhoneGap environment. Usage of the IndexedDB-API in a browser.yes infobut no files for transaction logging
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nononoyes
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and KerberosAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)nono infoa simple user-level security was built in till version Access 2003
More information provided by the system vendor
Apache ImpalaDatabricksGoogle Cloud BigtableLokiJSMicrosoft Access
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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More resources
Apache ImpalaDatabricksGoogle Cloud BigtableLokiJSMicrosoft Access
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