DBMS > Apache Hive vs. Datastax Enterprise vs. RDF4J

System Properties Comparison Apache Hive vs. Datastax Enterprise vs. RDF4J

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

data warehouse software for querying and managing large distributed datasets, built on 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.

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

Primary database model

Relational DBMS

Wide column store

RDF store

Secondary database models

Document store
Graph DBMS
Spatial DBMS
Search engine
Vector DBMS

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

Score	53.09
Rank	#18	Overall
	#12	Relational DBMS

Score	4.74
Rank	#64	Overall
	#4	Wide column stores

Score	0.87
Rank	#202	Overall
	#7	RDF stores

Website

hive.apache.org

www.datastax.com/products/datastax-enterprise

rdf4j.org

Technical documentation

cwiki.apache.org/confluence/display/Hive/Home

docs.datastax.com

rdf4j.org/documentation

Developer

Apache Software Foundation

initially developed by Facebook

DataStax

Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.

Initial release

2012

2011

2004

Current release

3.1.3, April 2022

6.8, April 2020

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

Cloud-based only

Only available as a cloud service

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

All OS with a Java VM

Linux
OS X

Linux
OS X
Unix
Windows

Data scheme

yes

schema-free

yes

RDF Schemas

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

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

APIs and other access methods

JDBC
ODBC
Thrift

Proprietary protocol

CQL (Cassandra Query Language)
TinkerPop Gremlin

with DSE Graph

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

C++
Java
PHP
Python

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

Java
PHP
Python

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

no "single point of failure"

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

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

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

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

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

ACID

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

in-memory storage is supported as well

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

Access rights for users can be defined per object

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.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

Recent citations in the news

Unlock efficient data processing with Iceberg
11 November 2024, SiliconANGLE News

Design a data mesh pattern for Amazon EMR-based data lakes using AWS Lake Formation with Hive metastore federation
10 June 2024, AWS Blog

Pinot for Low-Latency Offline Table Analytics
29 August 2024, Uber

Must-Know Techniques for Handling Big Data in Hive
14 August 2024, Towards Data Science

What Is Apache Iceberg?
26 February 2024, ibm.com

provided by Google News

DataStax previews new Hyper Converged Data Platform for enterprise AI
15 May 2024, VentureBeat

DataStax to launch AI-integrated data platforms HCDP & DSE 6.9
16 May 2024, IT Brief Asia

DataStax Acquires Aurelius and its TitanDB Graph Database
31 May 2024, Data Center Knowledge

DataStax Launches New Hyper-Converged Data Platform Giving Enterprises the Complete Modern Data Center Suite Needed for AI in Production
15 May 2024, FinTech Futures

DataStax acquires the startup behind low-code AI builder Langflow
4 April 2024, TechCrunch

provided by Google News

Amazon Neptune review: A scalable graph database for OLTP
13 May 2019, InfoWorld

provided by Google News

Share this page