DBMS > Datastax Enterprise vs. Elasticsearch vs. Google Cloud Bigtable vs. Kdb vs. Microsoft Azure Cosmos DB

System Properties Comparison Datastax Enterprise vs. Elasticsearch vs. Google Cloud Bigtable vs. Kdb vs. Microsoft Azure Cosmos DB

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 distributed, RESTful modern search and analytics engine based on Apache Lucene

Elasticsearch lets you perform and combine many types of searches such as structured, unstructured, geo, and metric

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

High performance Time Series DBMS

Globally distributed, horizontally scalable, multi-model database service

Primary database model

Wide column store

Search engine

Key-value store
Wide column store

Time Series DBMS
Vector DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Document store
Graph DBMS
Spatial DBMS
Search engine
Vector DBMS

Document store
Spatial DBMS
Vector DBMS

Relational DBMS

Spatial DBMS

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

Score	5.93
Rank	#56	Overall
	#4	Wide column stores

Score	132.83
Rank	#7	Overall
	#1	Search engines

Score	3.15
Rank	#95	Overall
	#14	Key-value stores
	#8	Wide column stores

Score	7.71
Rank	#49	Overall
	#2	Time Series DBMS
	#1	Vector DBMS

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

Website

www.datastax.com/products/datastax-enterprise

www.elastic.co/elasticsearch

cloud.google.com/bigtable

kx.com

azure.microsoft.com/services/cosmos-db

Technical documentation

docs.datastax.com

www.elastic.co/guide/en/elasticsearch/reference/current/index.html

cloud.google.com/bigtable/docs

code.kx.com

learn.microsoft.com/azure/cosmos-db

Developer

DataStax

Elastic

Google

Kx Systems, a division of First Derivatives plc

Microsoft

Initial release

2011

2010

2015

2000

kdb was released 2000, kdb+ in 2003

2014

Current release

6.8, April 2020

8.6, January 2023

3.6, May 2018

License

Commercial or Open Source

commercial

Open Source

Elastic License

commercial

free 32-bit version

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

Linux
OS X
Solaris
Windows

hosted

Data scheme

schema-free

Flexible type definitions. Once a type is defined, it is persistent

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

JSON types

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 search fields are automatically indexed

yes

table attribute 'grouped'

yes

All properties auto-indexed by default

SQL

Support of SQL

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

SQL-like query language

SQL-like query language (q)

SQL-like query language

APIs and other access methods

Proprietary protocol

CQL (Cassandra Query Language)
TinkerPop Gremlin

with DSE Graph

Java API
RESTful HTTP/JSON API

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

HTTP API
JDBC
Jupyter
Kafka
ODBC
WebSocket

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

Supported programming languages

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

.Net
Groovy
Community Contributed Clients
Java
JavaScript
Perl
PHP
Python
Ruby

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

C
C#
C++
Go
J
Java
JavaScript
Lua
MatLab
Perl
PHP
Python
R
Scala

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

Server-side scripts

Stored procedures

yes

user defined functions

JavaScript

Triggers

yes

by using the 'percolation' feature

yes

with views

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

no "single point of failure"

Sharding

horizontal partitioning

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

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

yes

Internal replication in Colossus, and regional replication between two clusters in different zones

Source-replica replication

yes

Implicit feature of the cloud service

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

ES-Hadoop Connector

yes

similar paradigm used for internal processing

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency
Tunable Consistency

consistency level can be individually decided with each write operation

Eventual Consistency

Synchronous doc based replication. Get by ID may show delays up to 1 sec. Configurable write consistency: one, quorum, all

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session 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

Atomic single-row 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

Memcached and Redis integration

yes

User concepts

Access control

Access rights for users can be defined per object

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

rights management via user accounts

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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Integrated columnar database & programming system for streaming, real time and historical...
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Competitive advantages

Supporting the following application requirements: Zero downtime - Built on Apache...
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provides seamless scalability; runs on industry standard server platforms; is top-ranked...
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Typical application scenarios

Applications that must be massively and linearly scalable with 100% uptime and able...
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tick database streaming sensor data massive intelligence applications oil and gas...
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Key customers

Capital One, Cisco, Comcast, eBay, McDonald's, Microsoft, Safeway, Sony, UBS, and...
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Goldman Sachs Morgan Stanley Merrill Lynch J.P. Morgan Deutsche Bank IEX Securities...
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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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kdb+ performance and reliability proven by our customers in critical infrastructure...
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Licensing and pricing models

Annual subscription
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upon request
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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.