DB-EnginesExtremeDB: mitigate connectivity issues in a DBMSEnglish
Deutsch
Knowledge Base of Relational and NoSQL Database Management Systemsprovided by solid IT

DBMS > Databricks vs. Heroic vs. LeanXcale vs. OpenQM

System Properties Comparison Databricks vs. Heroic vs. LeanXcale vs. OpenQM

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines
NameDatabricks  Xexclude from comparisonHeroic  Xexclude from comparisonLeanXcale  Xexclude from comparisonOpenQM infoalso called QM  Xexclude from comparison
DescriptionThe 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.Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearchA highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilitiesQpenQM is a high-performance, self-tuning, multi-value DBMS
Primary database modelDocument store
Relational DBMS
Time Series DBMSKey-value store
Relational DBMS
Multivalue DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score78.61
Rank#15  Overall
#2  Document stores
#10  Relational DBMS
Score0.51
Rank#255  Overall
#21  Time Series DBMS
Score0.29
Rank#291  Overall
#41  Key-value stores
#132  Relational DBMS
Score0.27
Rank#298  Overall
#10  Multivalue DBMS
Websitewww.databricks.comgithub.com/­spotify/­heroicwww.leanxcale.comwww.rocketsoftware.com/­products/­rocket-multivalue-application-development-platform/­rocket-open-qm
Technical documentationdocs.databricks.comspotify.github.io/­heroic
DeveloperDatabricksSpotifyLeanXcaleRocket Software, originally Martin Phillips
Initial release2013201420151993
Current release3.4-12
License infoCommercial or Open SourcecommercialOpen Source infoApache 2.0commercialOpen Source infoGPLv2, extended commercial license available
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJava
Server operating systemshostedAIX
FreeBSD
Linux
macOS
Raspberry Pi
Solaris
Windows
Data schemeFlexible Schema (defined schema, partial schema, schema free)schema-freeyesyes infowith some exceptions
Typing infopredefined data types such as float or dateyes
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.yesnoyes
Secondary indexesyesyes infovia Elasticsearchyes
SQL infoSupport of SQLwith Databricks SQLnoyes infothrough Apache Derbyno
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
HQL (Heroic Query Language, a JSON-based language)
HTTP API
JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector
Supported programming languagesPython
R
Scala
C
Java
Scala
.Net
Basic
C
Java
Objective C
PHP
Python
Server-side scripts infoStored proceduresuser defined functions and aggregatesnoyes
Triggersnoyes
Partitioning methods infoMethods for storing different data on different nodesShardingyes
Replication methods infoMethods for redundantly storing data on multiple nodesyesyesyes
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency
Immediate Consistency
Immediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnoACIDACID
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.nonoyes
User concepts infoAccess controlAccess rights can be defined down to the item level
More information provided by the system vendor
DatabricksHeroicLeanXcaleOpenQM infoalso called QM
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
» more

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources
DatabricksHeroicLeanXcaleOpenQM infoalso called QM
DB-Engines blog posts

PostgreSQL is the DBMS of the Year 2023
2 January 2024, Matthias Gelbmann, Paul Andlinger

show all

Recent citations in the news

5. Databricks
14 May 2024, CNBC

Researchers from Columbia University and Databricks Conducted a Comparative Study of LoRA and Full Finetuning in Large Language Models
19 May 2024, MarkTechPost

This Is the Platform Nancy Pelosi Used to Make Her Private Investment in Databricks
9 May 2024, Yahoo Finance

AI is Driving Record Sales at Multibillion-Dollar Databricks. An IPO Can Wait … - WSJ
6 March 2024, The Wall Street Journal

Top 5 Lessons Learned from Databricks' Journey from $400M to $1.5B+
23 April 2024, saastr.com

provided by Google News



Share this page

Featured Products

Datastax Astra logo

Bring all your data to Generative AI applications with vector search enabled by the most scalable
vector database available.
Try for Free

SingleStore logo

Build AI apps with Vectors on SQL and JSON with milliseconds response times.
Try it today.

RaimaDB logo

RaimaDB, embedded database for mission-critical applications. When performance, footprint and reliability matters.
Try RaimaDB for free.

Milvus logo

Vector database designed for GenAI, fully equipped for enterprise implementation.
Try Managed Milvus for Free

Neo4j logo

See for yourself how a graph database can make your life easier.
Use Neo4j online for free.

Present your product here