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DBMS > Atos Standard Common Repository vs. Databricks vs. QuestDB vs. Riak KV

System Properties Comparison Atos Standard Common Repository vs. Databricks vs. QuestDB vs. Riak KV

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Editorial information provided by DB-Engines
NameAtos Standard Common Repository  Xexclude from comparisonDatabricks  Xexclude from comparisonQuestDB  Xexclude from comparisonRiak KV  Xexclude from comparison
This system has been discontinued and will be removed from the DB-Engines ranking.
DescriptionHighly scalable database system, designed for managing session and subscriber data in modern mobile communication networksThe 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 high performance open source SQL database for time series dataDistributed, fault tolerant key-value store
Primary database modelDocument store
Key-value store
Document store
Relational DBMS
Time Series DBMSKey-value store infowith links between data sets and object tags for the creation of secondary indexes
Secondary database modelsRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score76.33
Rank#17  Overall
#3  Document stores
#11  Relational DBMS
Score2.48
Rank#115  Overall
#9  Time Series DBMS
Score4.44
Rank#80  Overall
#9  Key-value stores
Websiteatos.net/en/convergence-creators/portfolio/standard-common-repositorywww.databricks.comquestdb.io
Technical documentationdocs.databricks.comquestdb.io/­docswww.tiot.jp/­riak-docs/­riak/­kv/­latest
DeveloperAtos Convergence CreatorsDatabricksQuestDB Technology IncOpenSource, formerly Basho Technologies
Initial release2016201320142009
Current release17033.2.0, December 2022
License infoCommercial or Open SourcecommercialcommercialOpen Source infoApache 2.0Open Source infoApache version 2, commercial enterprise edition
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaJava (Zero-GC), C++, RustErlang
Server operating systemsLinuxhostedLinux
macOS
Windows
Linux
OS X
Data schemeSchema and schema-less with LDAP viewsFlexible Schema (defined schema, partial schema, schema free)yes infoschema-free via InfluxDB Line Protocolschema-free
Typing infopredefined data types such as float or dateoptionalyesno
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.yesyesnono
Secondary indexesyesyesnorestricted
SQL infoSupport of SQLnowith Databricks SQLSQL with time-series extensionsno
APIs and other access methodsLDAPJDBC
ODBC
RESTful HTTP API
HTTP REST
InfluxDB Line Protocol (TCP/UDP)
JDBC
PostgreSQL wire protocol
HTTP API
Native Erlang Interface
Supported programming languagesAll languages with LDAP bindingsPython
R
Scala
C infoPostgreSQL driver
C++
Go
Java
JavaScript (Node.js)
Python
Rust infoover HTTP
C infounofficial client library
C#
C++ infounofficial client library
Clojure infounofficial client library
Dart infounofficial client library
Erlang
Go infounofficial client library
Groovy infounofficial client library
Haskell infounofficial client library
Java
JavaScript infounofficial client library
Lisp infounofficial client library
Perl infounofficial client library
PHP
Python
Ruby
Scala infounofficial client library
Smalltalk infounofficial client library
Server-side scripts infoStored proceduresnouser defined functions and aggregatesnoErlang
Triggersyesnoyes infopre-commit hooks and post-commit hooks
Partitioning methods infoMethods for storing different data on different nodesSharding infocell divisionhorizontal partitioning (by timestamps)Sharding infono "single point of failure"
Replication methods infoMethods for redundantly storing data on multiple nodesyesyesSource-replica replication with eventual consistencyselectable replication factor
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on configurationImmediate ConsistencyImmediate ConsistencyEventual Consistency
Foreign keys infoReferential integritynonono infolinks between data sets can be stored
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic execution of specific operationsACIDACID for single-table writesno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnoyes infothrough memory mapped files
User concepts infoAccess controlLDAP bind authenticationyes, using Riak Security
More information provided by the system vendor
Atos Standard Common RepositoryDatabricksQuestDBRiak KV
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
» more
Relational model with native time series support Column-based storage and time partitioned...
» more
Competitive advantagesHigh ingestion throughput: peak of 4M rows/sec (TSBS Benchmark) Code optimizations...
» more
Typical application scenariosFinancial tick data Industrial IoT Application Metrics Monitoring
» more
Key customersBanks & Hedge funds, Yahoo, OKX, Airbus, Aquis Exchange, Net App, Cloudera, Airtel,...
» more
Licensing and pricing modelsOpen source Apache 2.0 QuestDB Enterprise QuestDB Cloud
» more
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