DBMS > AgensGraph vs. Databricks vs. Google BigQuery vs. LeanXcale vs. RavenDB

System Properties Comparison AgensGraph vs. Databricks vs. Google BigQuery vs. LeanXcale vs. RavenDB

Editorial information provided by DB-Engines

Name

Description

Multi-model database supporting relational and graph data models and built upon PostgreSQL

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

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

Open Source Operational and Transactional Enterprise NoSQL Document Database

Primary database model

Graph DBMS
Relational DBMS

Document store
Relational DBMS

Relational DBMS

Key-value store
Relational DBMS

Document store

Secondary database models

Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	0.20
Rank	#321	Overall
	#27	Graph DBMS
	#143	Relational DBMS

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.29
Rank	#291	Overall
	#41	Key-value stores
	#132	Relational DBMS

Score	2.92
Rank	#101	Overall
	#18	Document stores

Website

bitnine.net/agensgraph

www.databricks.com

cloud.google.com/bigquery

www.leanxcale.com

ravendb.net

Technical documentation

bitnine.net/documentation

docs.databricks.com

cloud.google.com/bigquery/docs

ravendb.net/docs

Developer

Bitnine Global Inc.

Databricks

Google

LeanXcale

Hibernating Rhinos

Initial release

2016

2013

2010

2015

2010

Current release

2.1, December 2018

5.4, July 2022

License

Commercial or Open Source

Open Source

Apache License 2.0

commercial

Open Source

AGPL version 3, 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

Server operating systems

Linux
OS X
Windows

hosted

Linux
macOS
Raspberry Pi
Windows

Data scheme

depending on used data model

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

yes

schema-free

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

yes

with Databricks SQL

yes

through Apache Derby

SQL-like query language (RQL)

APIs and other access methods

Cypher Query Language
JDBC

JDBC
ODBC
RESTful HTTP API

RESTful HTTP/JSON API

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

Supported programming languages

C
Java
JavaScript
Python

Python
R
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
Java
Scala

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

Server-side scripts

Stored procedures

yes

user defined functions and aggregates

user defined functions

in JavaScript

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

no, but can be realized using table inheritance

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

ACID

ACID, Cluster-wide transaction available

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

fine grained access rights according to SQL-standard

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

Google Cloud Identity & Access Management (IAM)

Authorization levels configured per client per database

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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