Cassandra vs Greenplum Database

Overview

Cassandra

Stacks3.6K

Followers3.5K

Votes507

GitHub Stars9.5K

Forks3.8K

Greenplum Database

Stacks47

Followers111

Votes0

GitHub Stars6.2K

Forks1.7K

Cassandra vs Greenplum Database: What are the differences?

Introduction

Cassandra and Greenplum Database are both popular choices for managing large amounts of data. However, there are several key differences between the two that make them suitable for different use cases.

Data Model: Cassandra is a NoSQL database that follows a columnar data model, while Greenplum Database is a relational database that follows a row-oriented data model. This means that Cassandra organizes data into columns, making it suitable for write-heavy workloads with flexible schema requirements, while Greenplum Database organizes data into rows, making it better suited for complex queries and analysis.
Scalability: Cassandra is designed to be highly scalable and can distribute data across multiple nodes in a cluster, allowing it to handle large amounts of data and high read and write loads. Greenplum Database, on the other hand, is also scalable but uses a shared-nothing architecture, where data is partitioned across different nodes, making it suitable for handling complex analytical queries on large datasets.
Consistency Model: Cassandra offers eventual consistency, meaning that updates to data are propagated asynchronously, resulting in lower latency but potentially allowing for inconsistent data across different nodes. Greenplum Database, on the other hand, provides strong consistency, ensuring that all queries see the most up-to-date data, but potentially at the cost of higher latency.
Data Distribution: Cassandra uses consistent hashing to distribute and replicate data across nodes in a cluster, ensuring fault tolerance and high availability. Greenplum Database uses a distribution key to partition data across segments in a cluster, optimizing query performance by minimizing data movement across nodes.
Query Language: Cassandra uses CQL (Cassandra Query Language), which is similar to SQL but also includes specific commands for working with Cassandra's data model, such as support for wide rows and compound primary keys. Greenplum Database, being a relational database, uses SQL as its query language, allowing for standard SQL queries and operations.
Use Cases: Due to its scalable and flexible nature, Cassandra is often used for real-time applications, such as online banking systems, social media platforms, and IoT (Internet of Things) applications, where high availability and low latency are critical. Greenplum Database, on the other hand, is well-suited for data warehousing and analytical workloads, where complex queries and analysis on large datasets are required.

In summary, Cassandra is a NoSQL database with a columnar data model, designed for write-heavy workloads and scalable real-time applications, while Greenplum Database is a relational database with a row-oriented data model, suitable for complex analytical queries and data warehousing.

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Advice on Cassandra, Greenplum Database

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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Comments

Detailed Comparison

Cassandra	Greenplum Database
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.	It is a massively parallel processing (MPP) database server with an architecture specially designed to manage large-scale analytic data warehouses and business intelligence workloads. It is based on PostgreSQL open-source technology.
-	Core SQL Conformance; MPP Architecture; Innovative Query Optimization; Polymorphic Data Storage; Integrated In-Database Analytics
Statistics
GitHub Stars 9.5K	GitHub Stars 6.2K
GitHub Forks 3.8K	GitHub Forks 1.7K
Stacks 3.6K	Stacks 47
Followers 3.5K	Followers 111
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	PostgreSQL Kong Slick Heroku Apache Hive Clever Cloud Couchbase Sequelize Sails.js Metabase

What are some alternatives to Cassandra, Greenplum Database?

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 Greenplum Database: What are the differences?

Introduction

Data Model: Cassandra is a NoSQL database that follows a columnar data model, while Greenplum Database is a relational database that follows a row-oriented data model. This means that Cassandra organizes data into columns, making it suitable for write-heavy workloads with flexible schema requirements, while Greenplum Database organizes data into rows, making it better suited for complex queries and analysis.
Scalability: Cassandra is designed to be highly scalable and can distribute data across multiple nodes in a cluster, allowing it to handle large amounts of data and high read and write loads. Greenplum Database, on the other hand, is also scalable but uses a shared-nothing architecture, where data is partitioned across different nodes, making it suitable for handling complex analytical queries on large datasets.
Consistency Model: Cassandra offers eventual consistency, meaning that updates to data are propagated asynchronously, resulting in lower latency but potentially allowing for inconsistent data across different nodes. Greenplum Database, on the other hand, provides strong consistency, ensuring that all queries see the most up-to-date data, but potentially at the cost of higher latency.
Data Distribution: Cassandra uses consistent hashing to distribute and replicate data across nodes in a cluster, ensuring fault tolerance and high availability. Greenplum Database uses a distribution key to partition data across segments in a cluster, optimizing query performance by minimizing data movement across nodes.
Query Language: Cassandra uses CQL (Cassandra Query Language), which is similar to SQL but also includes specific commands for working with Cassandra's data model, such as support for wide rows and compound primary keys. Greenplum Database, being a relational database, uses SQL as its query language, allowing for standard SQL queries and operations.
Use Cases: Due to its scalable and flexible nature, Cassandra is often used for real-time applications, such as online banking systems, social media platforms, and IoT (Internet of Things) applications, where high availability and low latency are critical. Greenplum Database, on the other hand, is well-suited for data warehousing and analytical workloads, where complex queries and analysis on large datasets are required.

Cassandra vs Greenplum Database

Overview