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DBMS > Databricks vs. Netezza vs. Postgres-XL vs. Sadas Engine

System Properties Comparison Databricks vs. Netezza vs. Postgres-XL vs. Sadas Engine

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Editorial information provided by DB-Engines
NameDatabricks  Xexclude from comparisonNetezza infoAlso called PureData System for Analytics by IBM  Xexclude from comparisonPostgres-XL  Xexclude from comparisonSadas Engine  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.Data warehouse and analytics appliance part of IBM PureSystemsBased on PostgreSQL enhanced with MPP and write-scale-out cluster featuresSADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments
Primary database modelDocument store
Relational DBMS
Relational DBMSRelational DBMSRelational DBMS
Secondary database modelsDocument store
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score81.08
Rank#15  Overall
#2  Document stores
#10  Relational DBMS
Score8.59
Rank#45  Overall
#29  Relational DBMS
Score0.53
Rank#254  Overall
#117  Relational DBMS
Score0.07
Rank#373  Overall
#157  Relational DBMS
Websitewww.databricks.comwww.ibm.com/­products/­netezzawww.postgres-xl.orgwww.sadasengine.com
Technical documentationdocs.databricks.comwww.postgres-xl.org/­documentationwww.sadasengine.com/­en/­sadas-engine-download-free-trial-and-documentation/­#documentation
DeveloperDatabricksIBMSADAS s.r.l.
Initial release201320002014 infosince 2012, originally named StormDB2006
Current release10 R1, October 20188.0
License infoCommercial or Open SourcecommercialcommercialOpen Source infoMozilla public licensecommercial infofree trial version available
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageCC++
Server operating systemshostedLinux infoincluded in applianceLinux
macOS
AIX
Linux
Windows
Data schemeFlexible Schema (defined schema, partial schema, schema free)yesyesyes
Typing infopredefined data types such as float or dateyesyesyes
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.yesyes infoXML type, but no XML query functionalityno
Secondary indexesyesyesyesyes
SQL infoSupport of SQLwith Databricks SQLyesyes infodistributed, parallel query executionyes
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
JDBC
ODBC
OLE DB
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
JDBC
ODBC
Proprietary protocol
Supported programming languagesPython
R
Scala
C
C++
Fortran
Java
Lua
Perl
Python
R
.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl
.Net
C
C#
C++
Groovy
Java
PHP
Python
Server-side scripts infoStored proceduresuser defined functions and aggregatesyesuser defined functionsno
Triggersnoyesno
Partitioning methods infoMethods for storing different data on different nodesShardinghorizontal partitioninghorizontal partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesyesSource-replica replicationnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynoyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDACID infoMVCC
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 infomanaged by 'Learn by Usage'
User concepts infoAccess controlUsers with fine-grained authorization conceptfine grained access rights according to SQL-standardAccess rights for users, groups and roles according to SQL-standard
More information provided by the system vendor
DatabricksNetezza infoAlso called PureData System for Analytics by IBMPostgres-XLSadas Engine
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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More resources
DatabricksNetezza infoAlso called PureData System for Analytics by IBMPostgres-XLSadas Engine
DB-Engines blog posts

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Recent citations in the news

Databricks acquires data optimization startup Tabular in fresh challenge to Snowflake
4 June 2024, CNBC

Databricks buys Tabular to win the Iceberg war – Blocks and Files
5 June 2024, Blocks & Files

Databricks CEO Ali Ghodsi on Snowflake rivalry and the 'why' behind Databricks' latest billion-dollar deal
5 June 2024, Yahoo Finance

Exclusive | Databricks to Buy Data-Management Startup Tabular in Bid for AI Clients
4 June 2024, The Wall Street Journal

Databricks Agrees to Acquire Tabular, the Company Founded by the Original Creators of Apache Iceberg USA - English
4 June 2024, PR Newswire

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IBM announces availability of the high-performance, cloud-native Netezza Performance Server as a Service on AWS
11 July 2023, IBM

AWS and IBM Netezza come out in support of Iceberg in table format face-off
1 August 2023, The Register

How to migrate a large data warehouse from IBM Netezza to Amazon Redshift with no downtime | Amazon Web Services
21 August 2019, AWS Blog

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