DBMS > Apache Impala vs. Databricks vs. RocksDB vs. STSdb

System Properties Comparison Apache Impala vs. Databricks vs. RocksDB vs. STSdb

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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.

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

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

Key-value store

Secondary database models

Document store

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	81.08
Rank	#15	Overall
	#2	Document stores
	#10	Relational DBMS

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Score	0.10
Rank	#357	Overall
	#51	Key-value stores

Website

impala.apache.org

www.databricks.com

rocksdb.org

github.com/STSSoft/STSdb4

Technical documentation

impala.apache.org/impala-docs.html

docs.databricks.com

github.com/facebook/rocksdb/wiki

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Databricks

Facebook, Inc.

STS Soft SC

Initial release

2013

2011

Current release

4.1.0, June 2022

9.2.1, May 2024

4.0.8, September 2015

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

BSD

Open Source

GPLv2, commercial license available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Server operating systems

Linux

hosted

Linux

Windows

Data scheme

yes

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

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

APIs and other access methods

JDBC
ODBC

JDBC
ODBC
RESTful HTTP API

C++ API
Java API

.NET Client API

Supported programming languages

All languages supporting JDBC/ODBC

Python
R
Scala

C
C++
Go
Java
Perl
Python
Ruby

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

horizontal partitioning

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

Transaction concepts

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

ACID

yes

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.

yes

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...
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