Cassandra vs Datastax Enterprise

Overview

Cassandra

Stacks3.6K

Followers3.5K

Votes507

GitHub Stars9.5K

Forks3.8K

DataStax Enterprise

Stacks48

Followers53

Votes0

Cassandra vs Datastax Enterprise: What are the differences?

Introduction

Cassandra and Datastax Enterprise are both popular database management systems, but they have some key differences that set them apart. In this article, we will explore these differences to help you understand which system may be better suited for your specific needs.

Scalability and Performance: One key difference between Cassandra and Datastax Enterprise is in their scalability and performance capabilities. Cassandra is known for its ability to scale horizontally, allowing for seamless expansion across multiple nodes. It can handle large amounts of data and high read and write throughput. On the other hand, Datastax Enterprise builds upon Cassandra's scalability by offering additional features such as advanced analytics, real-time search, and graph processing. This makes it suitable for ultra-high-performance use cases where speed and efficiency are critical.
Security and Administration: Another difference lies in the security and administration capabilities of Cassandra and Datastax Enterprise. Cassandra provides basic security features, such as authentication and authorization, but lacks more advanced security measures like encryption at rest or in transit. Datastax Enterprise, on the other hand, enhances Cassandra's security with features like Advanced Security, which includes role-based access control, transparent data encryption, and auditing capabilities. It also provides a user-friendly graphical interface for administration, making it easier to manage and monitor your database clusters.
Analytics and Search: Datastax Enterprise goes beyond Cassandra's capabilities when it comes to analytics and search functionality. Cassandra is primarily designed for transactional workloads and lacks built-in support for complex analytics and search operations. Datastax Enterprise, on the other hand, incorporates Apache Spark and Apache Solr to provide powerful analytics and search capabilities. This allows users to perform real-time analytics on their data and build advanced search functionalities atop their Cassandra database.
Data Modeling and Schema Flexibility: Cassandra and Datastax Enterprise also differ in terms of data modeling and schema flexibility. Cassandra follows a schema-less data model, allowing for flexible and dynamic data structures. It does not enforce strict schema definitions, which can be advantageous for certain use cases. However, this flexibility comes with some trade-offs, such as potentially complex queries and the need for careful data modeling. Datastax Enterprise introduces the concept of DataStax Enterprise Graph, which extends Cassandra's data model to support graph-oriented data structures and querying. This can be useful for applications that require complex relationship-based queries.
Support and Professional Services: While Cassandra is an open-source project with an active community, Datastax Enterprise offers additional support and professional services for users. Datastax provides enterprise-level technical support, training, and consulting services, which can be beneficial for organizations that require a higher level of support and expertise. This can be particularly important for mission-critical applications that need prompt assistance and tailored solutions.
Licensing and Cost: One final difference is in the licensing and cost structure of Cassandra and Datastax Enterprise. Cassandra is open-source and free to use, making it a cost-effective choice for many applications. Datastax Enterprise, on the other hand, is a commercial product that requires a paid subscription. While it offers advanced features and support, this comes at a cost that may not be suitable for all budgets.

In Summary, Cassandra and Datastax Enterprise differ in terms of scalability, performance, security, analytics, data modeling, support, and cost.

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Advice on Cassandra, DataStax Enterprise

Vinay

Head of Engineering

Sep 19, 2019

Needs advice

The problem I have is - we need to process & change(update/insert) 55M Data every 2 min and this updated data to be available for Rest API for Filtering / Selection. Response time for Rest API should be less than 1 sec.

The most important factors for me are processing and storing time of 2 min. There need to be 2 views of Data One is for Selection & 2. Changed data.

174k views174k

Comments

Detailed Comparison

Cassandra	DataStax Enterprise
Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.	Scale-out NoSQL for any workload Built on Apache Cassandra™, DataStax Enterprise adds NoSQL workloads including search, graph, and analytics, with operational reliability hardened by the largest internet apps and the Fortune 100.
-	Hybrid; Lightning Fast; Distributed
Statistics
GitHub Stars 9.5K	GitHub Stars -
GitHub Forks 3.8K	GitHub Forks -
Stacks 3.6K	Stacks 48
Followers 3.5K	Followers 53
Votes 507	Votes 0
Pros & Cons
Pros 119 Distributed 98 High performance 81 High availability 74 Easy scalability 53 Replication Cons 3 Reliability of replication 1 Size 1 Updates	No community feedback yet
Integrations
No integrations available	Kubernetes Apache Spark Kafka Apache Solr

What are some alternatives to Cassandra, DataStax Enterprise?

MongoDB

MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

InfluxDB

InfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running. InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out.

Related Comparisons

Cassandra vs Datastax Enterprise: What are the differences?

Introduction

Scalability and Performance: One key difference between Cassandra and Datastax Enterprise is in their scalability and performance capabilities. Cassandra is known for its ability to scale horizontally, allowing for seamless expansion across multiple nodes. It can handle large amounts of data and high read and write throughput. On the other hand, Datastax Enterprise builds upon Cassandra's scalability by offering additional features such as advanced analytics, real-time search, and graph processing. This makes it suitable for ultra-high-performance use cases where speed and efficiency are critical.
Security and Administration: Another difference lies in the security and administration capabilities of Cassandra and Datastax Enterprise. Cassandra provides basic security features, such as authentication and authorization, but lacks more advanced security measures like encryption at rest or in transit. Datastax Enterprise, on the other hand, enhances Cassandra's security with features like Advanced Security, which includes role-based access control, transparent data encryption, and auditing capabilities. It also provides a user-friendly graphical interface for administration, making it easier to manage and monitor your database clusters.
Analytics and Search: Datastax Enterprise goes beyond Cassandra's capabilities when it comes to analytics and search functionality. Cassandra is primarily designed for transactional workloads and lacks built-in support for complex analytics and search operations. Datastax Enterprise, on the other hand, incorporates Apache Spark and Apache Solr to provide powerful analytics and search capabilities. This allows users to perform real-time analytics on their data and build advanced search functionalities atop their Cassandra database.
Data Modeling and Schema Flexibility: Cassandra and Datastax Enterprise also differ in terms of data modeling and schema flexibility. Cassandra follows a schema-less data model, allowing for flexible and dynamic data structures. It does not enforce strict schema definitions, which can be advantageous for certain use cases. However, this flexibility comes with some trade-offs, such as potentially complex queries and the need for careful data modeling. Datastax Enterprise introduces the concept of DataStax Enterprise Graph, which extends Cassandra's data model to support graph-oriented data structures and querying. This can be useful for applications that require complex relationship-based queries.
Support and Professional Services: While Cassandra is an open-source project with an active community, Datastax Enterprise offers additional support and professional services for users. Datastax provides enterprise-level technical support, training, and consulting services, which can be beneficial for organizations that require a higher level of support and expertise. This can be particularly important for mission-critical applications that need prompt assistance and tailored solutions.
Licensing and Cost: One final difference is in the licensing and cost structure of Cassandra and Datastax Enterprise. Cassandra is open-source and free to use, making it a cost-effective choice for many applications. Datastax Enterprise, on the other hand, is a commercial product that requires a paid subscription. While it offers advanced features and support, this comes at a cost that may not be suitable for all budgets.

In Summary, Cassandra and Datastax Enterprise differ in terms of scalability, performance, security, analytics, data modeling, support, and cost.

Cassandra vs Datastax Enterprise

Overview