DBMS > Databricks vs. Ehcache vs. GridGain vs. Microsoft Azure Cosmos DB vs. Warp 10

System Properties Comparison Databricks vs. Ehcache vs. GridGain vs. Microsoft Azure Cosmos DB vs. Warp 10

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.

A widely adopted Java cache with tiered storage options

GridGain is an in-memory computing platform, built on Apache Ignite

Globally distributed, horizontally scalable, multi-model database service

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

Primary database model

Document store
Relational DBMS

Key-value store

Key-value store
Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Time Series DBMS

Secondary database models

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	4.64
Rank	#68	Overall
	#8	Key-value stores

Score	1.55
Rank	#150	Overall
	#26	Key-value stores
	#70	Relational DBMS

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	0.14
Rank	#344	Overall
	#32	Time Series DBMS

Website

www.databricks.com

www.ehcache.org

www.gridgain.com

azure.microsoft.com/services/cosmos-db

www.warp10.io

Technical documentation

docs.databricks.com

www.ehcache.org/documentation

www.gridgain.com/docs/index.html

learn.microsoft.com/azure/cosmos-db

www.warp10.io/content/02_Getting_started

Developer

Databricks

Terracotta Inc, owned by Software AG

GridGain Systems, Inc.

Microsoft

SenX

Initial release

2013

2009

2007

2014

2015

Current release

3.10.0, March 2022

GridGain 8.5.1

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; commercial licenses available

commercial

Open Source

Apache License 2.0

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

Java

Java, C++, .Net

Java

Server operating systems

hosted

All OS with a Java VM

Linux
OS X
Solaris
Windows

hosted

Linux
OS X
Windows

Data scheme

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

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

JSON types

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

All properties auto-indexed by default

SQL

Support of SQL

with Databricks SQL

ANSI-99 for query and DML statements, subset of DDL

SQL-like query language

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

JCache

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

HTTP API
Jupyter
WebSocket

Supported programming languages

Python
R
Scala

Java

C#
C++
Java
PHP
Python
Ruby
Scala

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

Server-side scripts

Stored procedures

user defined functions and aggregates

yes (compute grid and cache interceptors can be used instead)

JavaScript

yes

WarpScript

Triggers

yes

Cache Event Listeners

yes (cache interceptors and events)

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

by using Terracotta Server

Sharding

Implicit feature of the cloud service

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

yes

by using Terracotta Server

yes (replicated cache)

yes

Implicit feature of the cloud service

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Tunable Consistency (Strong, Eventual, Weak)

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

based on HBase

Foreign keys

Referential integrity

Transaction concepts

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

ACID

yes

supports JTA and can work as an XA resource

ACID

Multi-item ACID transactions with snapshot isolation within a partition

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using a tiered cache-storage approach

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

Security Hooks for custom implementations

Access rights can be defined down to the item level

Mandatory use of cryptographic tokens, containing fine-grained authorizations

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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