Cassandra vs Cloudera Enterprise

Overview

Cloudera Enterprise

Stacks126

Followers172

Votes5

Cassandra

Stacks3.6K

Followers3.5K

Votes507

GitHub Stars9.5K

Forks3.8K

Cassandra vs Cloudera Enterprise: What are the differences?

Introduction

Cassandra and Cloudera Enterprise are both popular technologies used in big data and analytics. However, they have key differences that set them apart from each other. In this article, we will explore these differences and understand how they impact their respective use cases.

Scalability: One of the main differences between Cassandra and Cloudera Enterprise lies in their scalability. Cassandra is designed to handle large-scale distributed environments and can easily scale horizontally by adding more nodes to the cluster. On the other hand, Cloudera Enterprise focuses on scalable data management and processing, leveraging technologies like Apache Hadoop and Spark for distributed computing. While both are scalable, Cassandra is primarily focused on data storage and retrieval, while Cloudera Enterprise provides a comprehensive platform for managing and analyzing big data.
Data Model: Another significant difference between Cassandra and Cloudera Enterprise is their data model. Cassandra follows a wide-column data model inspired by the Google Bigtable, which means it can handle large amounts of structured, semi-structured, and unstructured data. Cloudera Enterprise, on the other hand, follows a schema-on-read model, where the structure of the data is inferred during the read operation. This allows for more flexibility in handling different data formats and types.
Consistency vs. Eventual Consistency: Cassandra and Cloudera Enterprise also differ in terms of data consistency. Cassandra favors high availability and partition tolerance over strict consistency and follows an eventual consistency model. This means that the data can be inconsistent for a short period but eventually converges to a consistent state. On the other hand, Cloudera Enterprise, being built on technologies like Apache Hadoop and HBase, provides strong consistency guarantees, ensuring that data is always in a consistent state. This makes Cloudera Enterprise suitable for use cases that require strict data consistency.
Query Language: Cassandra and Cloudera Enterprise also differ in their query language. Cassandra uses CQL (Cassandra Query Language), which is similar to SQL with some differences to support the wide-column data model. This makes it easier for developers familiar with SQL to work with Cassandra. On the other hand, Cloudera Enterprise predominantly uses Hive for querying data with its SQL-like language, HiveQL. HiveQL is optimized for data warehousing and analytics, making it suitable for complex analytical queries.
Security and Authentication: When it comes to security and authentication, Cassandra and Cloudera Enterprise have different approaches. Cassandra provides basic authentication and authorization mechanisms out-of-the-box, allowing administrators to control access to data and perform user management functions. Cloudera Enterprise, on the other hand, provides a more comprehensive security framework with features like Kerberos-based authentication, role-based access control, and fine-grained authorization. This makes it suitable for enterprise-grade security requirements.
Ecosystem and Integration: Lastly, Cassandra and Cloudera Enterprise have different ecosystems and integration capabilities. Cassandra integrates well with various technologies in the Apache ecosystem like Apache Spark and Apache Kafka. It also has wide language support with drivers available in many programming languages. Cloudera Enterprise, being an enterprise data platform, offers a comprehensive ecosystem with integration with various tools like Apache Hadoop, Apache Spark, and Apache Impala. It also provides connectors to popular BI and visualization tools, making it easier to build end-to-end data pipelines and analytics solutions.

In summary, Cassandra and Cloudera Enterprise differ in terms of scalability, data model, consistency, query language, security, and ecosystem. While Cassandra is geared towards high scalability, flexible data modeling, and eventual consistency, Cloudera Enterprise focuses on scalable data management and processing, strong consistency, advanced security features, and a comprehensive ecosystem of tools and integrations.

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Advice on Cloudera Enterprise, Cassandra

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.

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Detailed Comparison

Cloudera Enterprise	Cassandra
Cloudera Enterprise includes CDH, the world’s most popular open source Hadoop-based platform, as well as advanced system management and data management tools plus dedicated support and community advocacy from our world-class team of Hadoop developers and experts.	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.
Unified – one integrated system, bringing diverse users and application workloads to one pool of data on common infrastructure; no data movement required;Secure – perimeter security, authentication, granular authorization, and data protection;Governed – enterprise-grade data auditing, data lineage, and data discovery;Managed – native high-availability, fault-tolerance and self-healing storage, automated backup and disaster recovery, and advanced system and data management;Open – Apache-licensed open source to ensure your data and applications remain yours, and an open platform to connect with all of your existing investments in technology and skills	-
Statistics
GitHub Stars -	GitHub Stars 9.5K
GitHub Forks -	GitHub Forks 3.8K
Stacks 126	Stacks 3.6K
Followers 172	Followers 3.5K
Votes 5	Votes 507
Pros & Cons
Pros 1 Scalability 1 Multicloud 1 Hybrid cloud 1 Easily management 1 Cheeper	Pros 119 Distributed 98 High performance 81 High availability 74 Easy scalability 53 Replication Cons 3 Reliability of replication 1 Updates 1 Size

What are some alternatives to Cloudera Enterprise, Cassandra?

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.

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Cassandra vs Cloudera Enterprise: What are the differences?

Introduction

Scalability: One of the main differences between Cassandra and Cloudera Enterprise lies in their scalability. Cassandra is designed to handle large-scale distributed environments and can easily scale horizontally by adding more nodes to the cluster. On the other hand, Cloudera Enterprise focuses on scalable data management and processing, leveraging technologies like Apache Hadoop and Spark for distributed computing. While both are scalable, Cassandra is primarily focused on data storage and retrieval, while Cloudera Enterprise provides a comprehensive platform for managing and analyzing big data.
Data Model: Another significant difference between Cassandra and Cloudera Enterprise is their data model. Cassandra follows a wide-column data model inspired by the Google Bigtable, which means it can handle large amounts of structured, semi-structured, and unstructured data. Cloudera Enterprise, on the other hand, follows a schema-on-read model, where the structure of the data is inferred during the read operation. This allows for more flexibility in handling different data formats and types.
Consistency vs. Eventual Consistency: Cassandra and Cloudera Enterprise also differ in terms of data consistency. Cassandra favors high availability and partition tolerance over strict consistency and follows an eventual consistency model. This means that the data can be inconsistent for a short period but eventually converges to a consistent state. On the other hand, Cloudera Enterprise, being built on technologies like Apache Hadoop and HBase, provides strong consistency guarantees, ensuring that data is always in a consistent state. This makes Cloudera Enterprise suitable for use cases that require strict data consistency.
Query Language: Cassandra and Cloudera Enterprise also differ in their query language. Cassandra uses CQL (Cassandra Query Language), which is similar to SQL with some differences to support the wide-column data model. This makes it easier for developers familiar with SQL to work with Cassandra. On the other hand, Cloudera Enterprise predominantly uses Hive for querying data with its SQL-like language, HiveQL. HiveQL is optimized for data warehousing and analytics, making it suitable for complex analytical queries.
Security and Authentication: When it comes to security and authentication, Cassandra and Cloudera Enterprise have different approaches. Cassandra provides basic authentication and authorization mechanisms out-of-the-box, allowing administrators to control access to data and perform user management functions. Cloudera Enterprise, on the other hand, provides a more comprehensive security framework with features like Kerberos-based authentication, role-based access control, and fine-grained authorization. This makes it suitable for enterprise-grade security requirements.
Ecosystem and Integration: Lastly, Cassandra and Cloudera Enterprise have different ecosystems and integration capabilities. Cassandra integrates well with various technologies in the Apache ecosystem like Apache Spark and Apache Kafka. It also has wide language support with drivers available in many programming languages. Cloudera Enterprise, being an enterprise data platform, offers a comprehensive ecosystem with integration with various tools like Apache Hadoop, Apache Spark, and Apache Impala. It also provides connectors to popular BI and visualization tools, making it easier to build end-to-end data pipelines and analytics solutions.

Cassandra vs Cloudera Enterprise

Overview

Cassandra vs Cloudera Enterprise: What are the differences?