DBMS > Apache Impala vs. Databricks vs. Oracle Rdb vs. STSdb

System Properties Comparison Apache Impala vs. Databricks vs. Oracle Rdb vs. STSdb

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

The 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.

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Relational DBMS

Document store
Relational DBMS

Relational DBMS

Key-value store

Secondary database models

Document store

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	78.61
Rank	#15	Overall
	#2	Document stores
	#10	Relational DBMS

Score	1.08
Rank	#184	Overall
	#85	Relational DBMS

Score	0.04
Rank	#360	Overall
	#52	Key-value stores

Website

impala.apache.org

www.databricks.com

www.oracle.com/database/technologies/related/rdb.html

github.com/STSSoft/STSdb4

Technical documentation

impala.apache.org/impala-docs.html

docs.databricks.com

www.oracle.com/database/technologies/related/rdb-doc.html

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Databricks

Oracle, originally developed by Digital Equipment Corporation (DEC)

STS Soft SC

Initial release

2013

1984

2011

Current release

4.1.0, June 2022

7.4.1.1, 2021

4.0.8, September 2015

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

GPLv2, commercial license available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

Server operating systems

Linux

hosted

HP Open VMS

Windows

Data scheme

yes

Flexible Schema (defined schema, partial schema, schema free)

yes

Typing

predefined data types such as float or date

yes

primitive types and user defined types (classes)

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

with Databricks SQL

yes

APIs and other access methods

JDBC
ODBC

JDBC
ODBC
RESTful HTTP API

.NET Client API

Supported programming languages

All languages supporting JDBC/ODBC

Python
R
Scala

C#
Java

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

user defined functions and aggregates

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

yes, on a single node

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

More information provided by the system vendor

Specific characteristics

Supported 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

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

Cloudera creates observability tool to help enterprises manage cloud costs
6 June 2023, SiliconANGLE News

Apache Impala 4 Supports Operator Multi-Threading
29 July 2021, iProgrammer

Apache Impala becomes Top-Level Project
28 November 2017, SDTimes.com

Cloudera Bringing Impala to AWS Cloud
28 November 2017, Datanami

Apache Doris just 'graduated': Why care about this SQL data warehouse
24 June 2022, InfoWorld

provided by Google News

New AI Copyright Class Actions Target Nvidia, Databricks
4 May 2024, Law360

Nvidia, Databricks Sued in Latest AI Copyright Class Actions
3 May 2024, Bloomberg Law

Exclusive | Pete Sonsini, Early Investor in Databricks, Gets Closer to Launching New VC Firm
3 May 2024, The Wall Street Journal

Protecting Your AI Investments: Databricks' Breakthrough Security Framework
2 May 2024, Acceleration Economy

Tableau adds generative AI tools, tightens Databricks bond
30 April 2024, TechTarget

provided by Google News

Oracle Adds New AI-Enabling Features To MySQL HeatWave
23 March 2023, Forbes

Oracle Business Model - How Oracle Makes Money?
12 June 2023, Business Model Analyst

Should we all consolidate databases for the storage benefits? Reg vultures deploy DevOps, zoos, haircuts
18 September 2020, The Register

2013 Data Science Salary Survey – O'Reilly
4 May 2013, O'Reilly Media

provided by Google News

Share this page