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DBMS > Amazon Redshift vs. Databricks vs. Datomic vs. DuckDB vs. RocksDB

System Properties Comparison Amazon Redshift vs. Databricks vs. Datomic vs. DuckDB vs. RocksDB

Editorial information provided by DB-Engines
NameAmazon Redshift  Xexclude from comparisonDatabricks  Xexclude from comparisonDatomic  Xexclude from comparisonDuckDB  Xexclude from comparisonRocksDB  Xexclude from comparison
DescriptionLarge scale data warehouse service for use with business intelligence toolsThe 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.Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durabilityAn embeddable, in-process, column-oriented SQL OLAP RDBMSEmbeddable persistent key-value store optimized for fast storage (flash and RAM)
Primary database modelRelational DBMSDocument store
Relational DBMS
Relational DBMSRelational DBMSKey-value store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score17.94
Rank#34  Overall
#21  Relational DBMS
Score78.61
Rank#15  Overall
#2  Document stores
#10  Relational DBMS
Score1.59
Rank#150  Overall
#69  Relational DBMS
Score4.57
Rank#74  Overall
#40  Relational DBMS
Score3.65
Rank#85  Overall
#11  Key-value stores
Websiteaws.amazon.com/­redshiftwww.databricks.comwww.datomic.comduckdb.orgrocksdb.org
Technical documentationdocs.aws.amazon.com/­redshiftdocs.databricks.comdocs.datomic.comduckdb.org/­docsgithub.com/­facebook/­rocksdb/­wiki
DeveloperAmazon (based on PostgreSQL)DatabricksCognitectFacebook, Inc.
Initial release20122013201220182013
Current release1.0.6735, June 20230.10, February 20248.11.4, April 2024
License infoCommercial or Open Sourcecommercialcommercialcommercial infolimited edition freeOpen Source infoMIT LicenseOpen Source infoBSD
Cloud-based only infoOnly available as a cloud serviceyesyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageCJava, ClojureC++C++
Server operating systemshostedhostedAll OS with a Java VMserver-lessLinux
Data schemeyesFlexible Schema (defined schema, partial schema, schema free)yesyesschema-free
Typing infopredefined data types such as float or dateyesyesyesno
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 indexesrestrictedyesyesyesno
SQL infoSupport of SQLyes infodoes not fully support an SQL-standardwith Databricks SQLnoyesno
APIs and other access methodsJDBC
ODBC
JDBC
ODBC
RESTful HTTP API
RESTful HTTP APIArrow Database Connectivity (ADBC)
CLI Client
JDBC
ODBC
C++ API
Java API
Supported programming languagesAll languages supporting JDBC/ODBCPython
R
Scala
Clojure
Java
C
C# info3rd party driver
C++
Crystal info3rd party driver
Go info3rd party driver
Java
Lisp info3rd party driver
Python
R
Ruby info3rd party driver
Rust
Swift
Zig info3rd party driver
C
C++
Go
Java
Perl
Python
Ruby
Server-side scripts infoStored proceduresuser defined functions infoin Pythonuser defined functions and aggregatesyes infoTransaction Functionsnono
TriggersnoBy using transaction functionsno
Partitioning methods infoMethods for storing different data on different nodesShardingnone infoBut extensive use of caching in the application peersnonehorizontal partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesyesyesnone infoBut extensive use of caching in the application peersnoneyes
MapReduce infoOffers an API for user-defined Map/Reduce methodsnononono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integrityyes infoinformational only, not enforced by the systemnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDACIDACIDyes
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes, multi-version concurrency control (MVCC)yes
Durability infoSupport for making data persistentyesyesyes infousing external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)yesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnoyes inforecommended only for testing and developmentyesyes
User concepts infoAccess controlfine grained access rights according to SQL-standardnonono
More information provided by the system vendor
Amazon RedshiftDatabricksDatomicDuckDBRocksDB
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services
3rd partiesCData: Connect to Big Data & NoSQL through standard Drivers.
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Speedb: A high performance RocksDB-compliant key-value store optimized for write-intensive workloads.
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More resources
Amazon RedshiftDatabricksDatomicDuckDBRocksDB
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