DBMS > Databricks vs. Google BigQuery vs. ObjectBox vs. OrientDB vs. Splice Machine

System Properties Comparison Databricks vs. Google BigQuery vs. ObjectBox vs. OrientDB vs. Splice Machine

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.

Large scale data warehouse service with append-only tables

Extremely fast embedded database for small devices, IoT and Mobile

Multi-model DBMS (Document, Graph, Key/Value)

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Primary database model

Document store
Relational DBMS

Relational DBMS

Object oriented DBMS

Document store
Graph DBMS
Key-value store

Relational DBMS

Secondary database models

Time Series 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	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	1.29
Rank	#166	Overall
	#5	Object oriented DBMS

Score	3.25
Rank	#89	Overall
	#16	Document stores
	#6	Graph DBMS
	#13	Key-value stores

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Website

www.databricks.com

cloud.google.com/bigquery

objectbox.io

orientdb.org

splicemachine.com

Technical documentation

docs.databricks.com

cloud.google.com/bigquery/docs

docs.objectbox.io

www.orientdb.com/docs/last/index.html

splicemachine.com/how-it-works

Developer

Databricks

Google

ObjectBox Limited

OrientDB LTD; CallidusCloud; SAP

Splice Machine

Initial release

2013

2010

2017

2010

2014

Current release

3.2.29, March 2024

3.1, March 2021

License

Commercial or Open Source

commercial

Open Source

Apache License 2.0

Open Source

Apache version 2

Open Source

AGPL 3.0, 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 and C++

Java

Server operating systems

hosted

Android
iOS
Linux
macOS
Windows

All OS with a Java JDK (>= JDK 6)

Linux
OS X
Solaris
Windows

Data scheme

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

yes

schema-free

Schema can be enforced for whole record ("schema-full") or for some fields only ("schema-hybrid")

yes

Typing

predefined data types such as float or date

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

yes

SQL-like query language, no joins

yes

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

RESTful HTTP/JSON API

Proprietary native API

Tinkerpop technology stack with Blueprints, Gremlin, Pipes
Java API
RESTful HTTP/JSON API

JDBC
Native Spark Datasource
ODBC

Supported programming languages

Python
R
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C++
Dart
Go
Java
JavaScript

planned (as of Jan 2019)
Kotlin
Python

planned (as of Jan 2019)
Swift

.Net
C
C#
C++
Clojure
Java
JavaScript
JavaScript (Node.js)
PHP
Python
Ruby
Scala

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions and aggregates

user defined functions

in JavaScript

Java, Javascript

yes

Java

Triggers

Hooks

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

online/offline synchronization between client and server

Multi-source replication

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

could be achieved with distributed queries

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

relationship in graphs

yes

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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 privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

yes

Access rights for users and roles; record level security configurable

Access rights for users, groups and roles according to SQL-standard

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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