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DBMS > Databricks vs. Drizzle vs. Google Cloud Bigtable vs. Sqrrl

System Properties Comparison Databricks vs. Drizzle vs. Google Cloud Bigtable vs. Sqrrl

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Editorial information provided by DB-Engines
NameDatabricks  Xexclude from comparisonDrizzle  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonSqrrl  Xexclude from comparison
Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.Sqrrl has been acquired by Amazon and became a part of Amazon Web Services. It has been removed from the DB-Engines ranking.
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.MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.Adaptable, secure NoSQL built on Apache Accumulo
Primary database modelDocument store
Relational DBMS
Relational DBMSKey-value store
Wide column store
Document store
Graph DBMS
Key-value store
Wide column store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score78.61
Rank#15  Overall
#2  Document stores
#10  Relational DBMS
Score3.26
Rank#92  Overall
#13  Key-value stores
#8  Wide column stores
Websitewww.databricks.comcloud.google.com/­bigtablesqrrl.com
Technical documentationdocs.databricks.comcloud.google.com/­bigtable/­docs
DeveloperDatabricksDrizzle project, originally started by Brian AkerGoogleAmazon infooriginally Sqrrl Data, Inc.
Initial release2013200820152012
Current release7.2.4, September 2012
License infoCommercial or Open SourcecommercialOpen Source infoGNU GPLcommercialcommercial
Cloud-based only infoOnly available as a cloud serviceyesnoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++Java
Server operating systemshostedFreeBSD
Linux
OS X
hostedLinux
Data schemeFlexible Schema (defined schema, partial schema, schema free)yesschema-freeschema-free
Typing infopredefined data types such as float or dateyesnoyes
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.yesno
Secondary indexesyesyesnoyes
SQL infoSupport of SQLwith Databricks SQLyes infowith proprietary extensionsnono
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
JDBCgRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
Accumulo Shell
Java API
JDBC
ODBC
RESTful HTTP API
Thrift
Supported programming languagesPython
R
Scala
C
C++
Java
PHP
C#
C++
Go
Java
JavaScript (Node.js)
Python
Actionscript
C infousing GLib
C#
C++
Cocoa
Delphi
Erlang
Go
Haskell
Java
JavaScript
OCaml
Perl
PHP
Python
Ruby
Smalltalk
Server-side scripts infoStored proceduresuser defined functions and aggregatesnonono
Triggersno infohooks for callbacks inside the server can be used.nono
Partitioning methods infoMethods for storing different data on different nodesShardingShardingSharding infomaking use of Hadoop
Replication methods infoMethods for redundantly storing data on multiple nodesyesMulti-source replication
Source-replica replication
Internal replication in Colossus, and regional replication between two clusters in different zonesselectable replication factor infomaking use of Hadoop
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesyes
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)Immediate Consistency infoDocument store kept consistent with combination of global timestamping, row-level transactions, and server-side consistency resolution.
Foreign keys infoReferential integrityyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDAtomic single-row operationsAtomic updates per row, document, or graph entity
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.nono
User concepts infoAccess controlPluggable authentication mechanisms infoe.g. LDAP, HTTPAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Cell-level Security, Data-Centric Security, Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC)
More information provided by the system vendor
DatabricksDrizzleGoogle Cloud BigtableSqrrl
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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DatabricksDrizzleGoogle Cloud BigtableSqrrl
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