DBMS > Datastax Enterprise vs. Hazelcast vs. Microsoft Azure Table Storage vs. Snowflake

System Properties Comparison Datastax Enterprise vs. Hazelcast vs. Microsoft Azure Table Storage vs. Snowflake

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

DataStax Enterprise (DSE) is the always-on, scalable data platform built on Apache Cassandra and designed for hybrid Cloud. DSE integrates graph, search, analytics, administration, developer tooling, and monitoring into a unified platform.

A widely adopted in-memory data grid

A Wide Column Store for rapid development using massive semi-structured datasets

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

Secondary database models

Document store
Graph DBMS
Spatial DBMS
Search engine
Vector DBMS

Document store

JSON support with IMDG 3.12

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

Score	5.93
Rank	#56	Overall
	#4	Wide column stores

Score	5.46
Rank	#61	Overall
	#7	Key-value stores

Score	4.04
Rank	#77	Overall
	#6	Wide column stores

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Website

www.datastax.com/products/datastax-enterprise

hazelcast.com

azure.microsoft.com/en-us/services/storage/tables

www.snowflake.com

Technical documentation

docs.datastax.com

hazelcast.org/imdg/docs

docs.snowflake.net/manuals/index.html

Developer

DataStax

Hazelcast

Microsoft

Snowflake Computing Inc.

Initial release

2011

2008

2012

2014

Current release

6.8, April 2020

5.3.6, November 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; commercial licenses available

commercial

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.

Datastax Astra DB: Astra DB simplifies cloud-native Cassandra application development for your apps, microservices and functions. Deploy in minutes on AWS, Google Cloud, Azure, and have it managed for you by the experts, with serverless, pay-as-you-go pricing.

Implementation language

Java

Server operating systems

Linux
OS X

All OS with a Java VM

hosted

Data scheme

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

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

the object must implement a serialization strategy

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements (CQL); Spark SQL

SQL-like query language

yes

APIs and other access methods

Proprietary protocol

CQL (Cassandra Query Language)
TinkerPop Gremlin

with DSE Graph

JCache
JPA
Memcached protocol
RESTful HTTP API

RESTful HTTP API

CLI Client
JDBC
ODBC

Supported programming languages

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

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

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

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

Event Listeners, Executor Services

user defined functions

Triggers

yes

Events

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

no "single point of failure"

Sharding

Implicit feature of the cloud service

yes

Replication methods

Methods for redundantly storing data on multiple nodes

configurable replication factor, datacenter aware, advanced replication for edge computing

yes

Replicated Map

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency
Tunable Consistency

consistency level can be individually decided with each write operation

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomicity and isolation are supported for single operations

one or two-phase-commit; repeatable reads; read commited

optimistic locking

ACID

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

Access rights for users can be defined per object

Role-based access control

Access rights based on private key authentication or shared access signatures

Users with fine-grained authorization concept, user roles and pluggable authentication

More information provided by the system vendor

Specific characteristics

DataStax Enterprise is scale-out data infrastructure for enterprises that need to...
» more

Competitive advantages

Supporting the following application requirements: Zero downtime - Built on Apache...
» more

Typical application scenarios

Applications that must be massively and linearly scalable with 100% uptime and able...
» more

Key customers

Capital One, Cisco, Comcast, eBay, McDonald's, Microsoft, Safeway, Sony, UBS, and...
» more

Market metrics

Among the Forbes 100 Most Innovative Companies, DataStax is trusted by 5 of the top...
» more

Licensing and pricing models

Annual subscription
» more

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.