DBMS > Databricks vs. IBM Db2 Event Store vs. RocksDB vs. TypeDB vs. XTDB

System Properties Comparison Databricks vs. IBM Db2 Event Store vs. RocksDB vs. TypeDB vs. XTDB

Editorial information provided by DB-Engines

Name

Description

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.

Distributed Event Store optimized for Internet of Things use cases

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

TypeDB is a strongly-typed database with a rich and logical type system and TypeQL as its query language

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Document store
Relational DBMS

Event Store
Time Series DBMS

Key-value store

Graph DBMS
Relational DBMS

Often described as a 'hyper-relational' database, since it implements the 'Entity-Relationship Paradigm' to manage complex data structures and ontologies.

Document store

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

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

Score	0.27
Rank	#309	Overall
	#2	Event Stores
	#28	Time Series DBMS

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

Score	0.70
Rank	#230	Overall
	#20	Graph DBMS
	#106	Relational DBMS

Score	0.18
Rank	#332	Overall
	#46	Document stores

Website

www.databricks.com

www.ibm.com/products/db2-event-store

rocksdb.org

typedb.com

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

docs.databricks.com

www.ibm.com/docs/en/db2-event-store

github.com/facebook/rocksdb/wiki

typedb.com/docs

www.xtdb.com/docs

Developer

Databricks

IBM

Facebook, Inc.

Vaticle

Juxt Ltd.

Initial release

2013

2017

2013

2016

2019

Current release

2.0

9.2.1, May 2024

2.26.3, January 2024

1.19, September 2021

License

Commercial or Open Source

commercial

free developer edition available

Open Source

BSD

Open Source

GPL Version 3, commercial licenses available

Open Source

MIT License

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 and C++

C++

Java

Clojure

Server operating systems

hosted

Linux

Linux, macOS, Windows for the developer addition

Linux

Linux
OS X
Windows

All OS with a Java 8 (and higher) VM
Linux

Data scheme

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

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, extensible-data-notation format

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

with Databricks SQL

yes

through the embedded Spark runtime

limited SQL, making use of Apache Calcite

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

C++ API
Java API

gRPC protocol
TypeDB Console (shell)
TypeDB Studio (Visualisation software- previously TypeDB Workbase)

HTTP REST
JDBC

Supported programming languages

Python
R
Scala

C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic

C
C++
Go
Java
Perl
Python
Ruby

All JVM based languages
Groovy
Java
JavaScript (Node.js)
Python
Scala

Clojure
Java

Server-side scripts

Stored procedures

user defined functions and aggregates

yes

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Sharding

by using Cassandra

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Active-active shard replication

yes

Multi-source replication

by using Cassandra

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

by using Apache Kafka and Apache Zookeeper

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

substituted by the relationship feature

Transaction concepts

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

ACID

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

No - written data is immutable

yes

Durability

Support for making data persistent

yes

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

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

yes

User concepts

Access control

fine grained access rights according to SQL-standard

yes

at REST API level; other APIs in progress

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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TypeDB is a polymorphic database with a conceptual data model, a strong subtyping...
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Competitive advantages

TypeDB provides a new level of expressivity, extensibility, interoperability, and...
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Typical application scenarios

Life sciences : TypeDB makes working with biological data much easier and accelerates...
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Licensing and pricing models

Apache f or language drivers, and AGPL and Commercial for the database server. The...
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