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DBMS > Apache Impala vs. Databricks vs. Google Cloud Datastore vs. TDSQL for MySQL vs. Yaacomo

System Properties Comparison Apache Impala vs. Databricks vs. Google Cloud Datastore vs. TDSQL for MySQL vs. Yaacomo

Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonDatabricks  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonTDSQL for MySQL  Xexclude from comparisonYaacomo  Xexclude from comparison
Yaacomo seems to be discontinued and is removed from the DB-Engines ranking
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.Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformA high-performance distributed database management system with features such as automatic sharding, intelligent operation and maintenance, elastic scalability without downtime, and enterprise-grade security. It is highly compatible with MySQL.OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing
Primary database modelRelational DBMSDocument store
Relational DBMS
Document storeRelational DBMSRelational DBMS
Secondary database modelsDocument storeDocument store
Spatial DBMS
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
Score4.36
Rank#72  Overall
#12  Document stores
Score0.85
Rank#207  Overall
#97  Relational DBMS
Websiteimpala.apache.orgwww.databricks.comcloud.google.com/­datastorewww.tencentcloud.com/­products/­dcdbyaacomo.com
Technical documentationimpala.apache.org/­impala-docs.htmldocs.databricks.comcloud.google.com/­datastore/­docswww.tencentcloud.com/­document/­product/­1042
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaDatabricksGoogleTencentQ2WEB GmbH
Initial release20132013200820132009
Current release4.1.0, June 2022
License infoCommercial or Open SourceOpen Source infoApache Version 2commercialcommercialcommercialcommercial
Cloud-based only infoOnly available as a cloud servicenoyesyesyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++
Server operating systemsLinuxhostedhostedhostedAndroid
Linux
Windows
Data schemeyesFlexible Schema (defined schema, partial schema, schema free)schema-freeyesyes
Typing infopredefined data types such as float or dateyesyes, details hereyesyes
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.noyesnonono
Secondary indexesyesyesyesyesyes
SQL infoSupport of SQLSQL-like DML and DDL statementswith Databricks SQLSQL-like query language (GQL)yesyes
APIs and other access methodsJDBC
ODBC
JDBC
ODBC
RESTful HTTP API
gRPC (using protocol buffers) API
RESTful HTTP/JSON API
JDBC
ODBC
JDBC
ODBC
Supported programming languagesAll languages supporting JDBC/ODBCPython
R
Scala
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C
C#
C++
Java
PHP
Python
Ruby
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduceuser defined functions and aggregatesusing Google App Engineyes
TriggersnoCallbacks using the Google Apps Engineyesyes
Partitioning methods infoMethods for storing different data on different nodesShardingShardingAutomatic shardinghorizontal partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factoryesMulti-source replication using PaxosMulti-source replication
Source-replica replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReduceyes infousing Google Cloud Dataflownono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on type of query and configuration infoStrong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.Immediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynoyes infovia ReferenceProperties or Ancestor pathsnoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsACIDACID
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.nonononoyes
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)Users with fine-grained authorization conceptfine grained access rights according to SQL-standard
More information provided by the system vendor
Apache ImpalaDatabricksGoogle Cloud DatastoreTDSQL for MySQLYaacomo
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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More resources
Apache ImpalaDatabricksGoogle Cloud DatastoreTDSQL for MySQLYaacomo
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