DBMS > Apache Impala vs. Datastax Enterprise vs. Microsoft Azure Cosmos DB vs. Newts

System Properties Comparison Apache Impala vs. Datastax Enterprise vs. Microsoft Azure Cosmos DB vs. Newts

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

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.

Globally distributed, horizontally scalable, multi-model database service

Time Series DBMS based on Cassandra

Primary database model

Relational DBMS

Wide column store

Document store
Graph DBMS
Key-value store
Wide column store

Time Series DBMS

Secondary database models

Document store

Document store
Graph DBMS
Spatial DBMS
Search engine
Vector DBMS

Spatial DBMS

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	5.80
Rank	#60	Overall
	#4	Wide column stores

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Website

impala.apache.org

www.datastax.com/products/datastax-enterprise

azure.microsoft.com/services/cosmos-db

opennms.github.io/newts

Technical documentation

impala.apache.org/impala-docs.html

docs.datastax.com

learn.microsoft.com/azure/cosmos-db

github.com/OpenNMS/newts/wiki

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

DataStax

Microsoft

OpenNMS Group

Initial release

2013

2011

2014

Current release

4.1.0, June 2022

6.8, April 2020

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

Apache 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.

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Implementation language

C++

Java

Server operating systems

Linux

Linux
OS X

hosted

Linux
OS X
Windows

Data scheme

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.

Secondary indexes

yes

All properties auto-indexed by default

SQL

Support of SQL

SQL-like DML and DDL statements

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

SQL-like query language

APIs and other access methods

JDBC
ODBC

Proprietary protocol

CQL (Cassandra Query Language)
TinkerPop Gremlin

with DSE Graph

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

HTTP REST
Java API

Supported programming languages

All languages supporting JDBC/ODBC

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

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

Java

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

JavaScript

Triggers

yes

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

no "single point of failure"

Sharding

Implicit feature of the cloud service

Sharding

based on Cassandra

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

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

yes

Implicit feature of the cloud service

selectable replication factor

based on Cassandra

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency
Tunable Consistency

consistency level can be individually decided with each write operation

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Foreign keys

Referential integrity

Transaction concepts

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

Atomicity and isolation are supported for single operations

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

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, groups and roles

based on Apache Sentry and Kerberos

Access rights for users can be defined per object

Access rights can be defined down to the item level

More information provided by the system vendor

Specific characteristics

DataStax Enterprise is scale-out data infrastructure for enterprises that need to...
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Competitive advantages

Supporting the following application requirements: Zero downtime - Built on Apache...
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Typical application scenarios

Applications that must be massively and linearly scalable with 100% uptime and able...
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Key customers

Capital One, Cisco, Comcast, eBay, McDonald's, Microsoft, Safeway, Sony, UBS, and...
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Market metrics

Among the Forbes 100 Most Innovative Companies, DataStax is trusted by 5 of the top...
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Licensing and pricing models

Annual subscription
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