DBMS > Databricks vs. Dgraph vs. Google Cloud Bigtable vs. jBASE vs. MySQL

System Properties Comparison Databricks vs. Dgraph vs. Google Cloud Bigtable vs. jBASE vs. MySQL

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 and scalable native Graph DBMS

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

A robust multi-value DBMS comprising development tools and middleware

Widely used open source RDBMS

Primary database model

Document store
Relational DBMS

Graph DBMS

Key-value store
Wide column store

Multivalue DBMS

Relational DBMS

Key/Value like access via memcached API

Secondary database models

Document store
Spatial DBMS

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	1.53
Rank	#152	Overall
	#15	Graph DBMS

Score	3.15
Rank	#95	Overall
	#14	Key-value stores
	#8	Wide column stores

Score	1.49
Rank	#156	Overall
	#3	Multivalue DBMS

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Website

www.databricks.com

dgraph.io

cloud.google.com/bigtable

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-jbase

www.mysql.com

Technical documentation

docs.databricks.com

dgraph.io/docs

cloud.google.com/bigtable/docs

docs.rocketsoftware.com/bundle?labelkey=jbase_5.9

dev.mysql.com/doc

Developer

Databricks

Dgraph Labs, Inc.

Google

Rocket Software (formerly Zumasys)

Oracle

since 2010, originally MySQL AB, then Sun

Initial release

2013

2016

2015

1991

1995

Current release

5.7

8.4.0, April 2024

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Cloud-based only

Only available as a cloud service

yes

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Database as a Service

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Implementation language

C and C++

Server operating systems

hosted

Linux
OS X
Windows

hosted

AIX
Linux
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

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

schema-free

yes

Typing

predefined data types such as float or date

yes

optional

yes

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

Embedded SQL for jBASE in BASIC

yes

with proprietary extensions

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

GraphQL query language
gRPC (using protocol buffers) API
HTTP API

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

JDBC
ODBC
Proprietary protocol
RESTful HTTP API
SOAP-based API

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

Python
R
Scala

C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

C#
C++
Go
Java
JavaScript (Node.js)
Python

.Net
Basic
Jabbascript
Java

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

user defined functions and aggregates

yes

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

yes

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Synchronous replication via Raft

Internal replication in Colossus, and regional replication between two clusters in different zones

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

Atomic single-row operations

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

Planned for future releases

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

Access rights can be defined down to the item level

Users with fine-grained authorization concept

no user groups or roles

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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