DBMS > Datastax Enterprise vs. Redis vs. TDengine

System Properties Comparison Datastax Enterprise vs. Redis vs. TDengine

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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.

Popular in-memory data platform used as a cache, message broker, and database that can be deployed on-premises, across clouds, and hybrid environments

Redis focuses on performance so most of its design decisions prioritize high performance and very low latencies.

Time Series DBMS and big data platform

Primary database model

Wide column store

Key-value store

Multiple data types and a rich set of operations, as well as configurable data expiration, eviction and persistence

Time Series DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS
Search engine
Vector DBMS

Document store

with RedisJSON
Graph DBMS

with RedisGraph
Spatial DBMS
Search engine

with RediSearch
Time Series DBMS

with RedisTimeSeries
Vector DBMS

Relational DBMS

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

Score	4.48
Rank	#64	Overall
	#4	Wide column stores

Score	153.36
Rank	#7	Overall
	#1	Key-value stores

Score	1.96
Rank	#117	Overall
	#9	Time Series DBMS

Website

www.datastax.com/products/datastax-enterprise

redis.com
redis.io

github.com/taosdata/TDengine
tdengine.com

Technical documentation

docs.datastax.com

docs.redis.com/latest/index.html
redis.io/docs

docs.tdengine.com

Developer

DataStax

Redis project core team, inspired by Salvatore Sanfilippo

Development sponsored by Redis Inc.

TDEngine, previously Taos Data

Initial release

2011

2009

2019

Current release

6.8, April 2020

7.2.5, May 2024

3.0, August 2022

License

Commercial or Open Source

commercial

Open Source

source-available extensions (modules), commercial licenses for Redis Enterprise

Open Source

AGPL V3, also commercial editions available

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

Linux
OS X

BSD
Linux
OS X
Windows

ported and maintained by Microsoft Open Technologies, Inc.

Linux
Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

partial

Supported data types are strings, hashes, lists, sets and sorted sets, bit arrays, hyperloglogs and geospatial indexes

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

with RediSearch module

SQL

Support of SQL

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

with RediSQL module

Standard SQL with extensions for time-series applications

APIs and other access methods

Proprietary protocol

CQL (Cassandra Query Language)
TinkerPop Gremlin

with DSE Graph

proprietary protocol

RESP - REdis Serialization Protocol

JDBC
RESTful HTTP API

Supported programming languages

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

C
C#
C++
Clojure
Crystal
D
Dart
Elixir
Erlang
Fancy
Go
Haskell
Haxe
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Objective-C
OCaml
Pascal
Perl
PHP
Prolog
Pure Data
Python
R
Rebol
Ruby
Rust
Scala
Scheme
Smalltalk
Swift
Tcl
Visual Basic

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

Server-side scripts

Stored procedures

Lua; Redis Functions coming in Redis 7 (slides and Github)

Triggers

yes

publish/subscribe channels provide some trigger functionality; RedisGears

yes, via alarm monitoring

Partitioning methods

Methods for storing different data on different nodes

Sharding

no "single point of failure"

Sharding

Automatic hash-based sharding with support for hash-tags for manual sharding

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

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

Multi-source replication

with Redis Enterprise Pack
Source-replica replication

Chained replication is supported

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

through RedisGears

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
Causal consistency can be enabled in Active-Active databases
Strong consistency with Redis Raft
Strong eventual consistency with Active-Active

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

Atomic execution of command blocks and scripts and optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

Data access is serialized by the server

yes

Durability

Support for making data persistent

yes

Configurable mechanisms for persistency via snapshots and/or operations logs

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

Access Control Lists (ACLs): redis.io/docs/management/security/acl
LDAP and Role-Based Access Control (RBAC) for Redis Enterprise
Mutual TLS authentication: redis.io/docs/management/security/encryption
Password-based authentication

yes

More information provided by the system vendor

Specific characteristics

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

TDengine™ is a time-series database designed to help traditional industries overcome...
» more

Competitive advantages

Supporting the following application requirements: Zero downtime - Built on Apache...
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High Performance at Any Scale: With its distributed scalable architecture that grows...
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Typical application scenarios

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

TDengine is purpose-built for Industry 4.0 and the Industrial IoT (IIoT) and particularly...
» 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

TDengine has garnered over 23,000 stars on GitHub and is used in over 50 countries...
» more

Licensing and pricing models

Annual subscription
» more

TDengine OSS is free, open-source software released under the AGPLv3. TDengine Enterprise...
» more

News

TDengine’s Stream Processing in IoT: Typical Applications in Connected Cars
20 January 2025

TDengine + Manufacturing: How to Achieve Efficient OPC UA Data Integration
17 January 2025

Accelerating Digital Transformation: The Critical Role of Time-Series Databases
10 January 2025

Time-Series Database: Cost-Effective Solution for Modernizing Data Operations in Manufacturing
9 January 2025

TDengine 3.3.5.0 Release Notes
8 January 2025

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