DBMS > Apache Spark (SQL) vs. Datastax Enterprise vs. ReductStore

System Properties Comparison Apache Spark (SQL) vs. Datastax Enterprise vs. ReductStore

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

Apache Spark SQL is a component on top of 'Spark Core' for structured data processing

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.

Designed to manage unstructured time-series data efficiently, providing unique features such as storing time-stamped blobs with labels, customizable data retention policies, and a straightforward FIFO quota system.

Primary database model

Relational DBMS

Wide column store

Time Series DBMS

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	21.62
Rank	#29	Overall
	#18	Relational DBMS

Score	4.00
Rank	#66	Overall
	#4	Wide column stores

Score	0.06
Rank	#355	Overall
	#37	Time Series DBMS

Website

spark.apache.org/sql

www.datastax.com/products/datastax-enterprise

github.com/reductstore
www.reduct.store

Technical documentation

spark.apache.org/docs/latest/sql-programming-guide.html

docs.datastax.com

www.reduct.store/docs

Developer

Apache Software Foundation

DataStax

ReductStore LLC

Initial release

2014

2011

2023

Current release

3.5.0 ( 2.13), September 2023

6.8, April 2020

1.9, March 2024

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

Business Source License 1.1

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.

Implementation language

Scala

Java

C++, Rust

Server operating systems

Linux
OS X
Windows

Linux
OS X

Docker
Linux
macOS
Windows

Data scheme

yes

schema-free

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

Proprietary protocol

CQL (Cassandra Query Language)
TinkerPop Gremlin

with DSE Graph

HTTP API

Supported programming languages

Java
Python
R
Scala

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

C++
JavaScript (Node.js)
Python
Rust

Server-side scripts

Stored procedures

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

yes, utilizing Spark Core

Sharding

no "single point of failure"

Replication methods

Methods for redundantly storing data on multiple nodes

none

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

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

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

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

More information provided by the system vendor

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

Introducing AWS Glue 5.0 for Apache Spark
4 December 2024, Amazon Web Services (AWS)

Scala vs Python for Apache Spark: An In-depth Comparison With Use Cases For Each
21 April 2025, Simplilearn.com

How to run Pandas code on Spark
25 January 2025, Theodo Data & AI

The 6 Best Apache Spark Courses on Udemy to Consider for 2025
1 January 2025, solutionsreview.com

18 top big data tools and technologies to know about in 2025
22 January 2025, TechTarget

provided by Google News

IBM to acquire DataStax, helping clients bring the power of unstructured data to enterprise AI applications
25 February 2025, IBM

Big Blue To Acquire Datastax in Enterprise AI Play
28 February 2025, Redmond Channel Partner

IBM to Acquire DataStax to Power Generative AI, Tapping into 93% of Enterprise Data
26 February 2025, eWEEK

IBM To Buy DataStax, Expand Watsonx AI Portfolio’s Data Management Capabilities
25 February 2025, CRN Magazine

IBM strengthens AI portfolio with acquisition of DataStax
26 February 2025, techzine.eu

provided by Google News

Share this page