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The Open-Source NoSQL DBMS Your Company is Missing

Data is at the heart of your company’s digital transformation and growth. Without data, your company would have trouble keeping up with client and customer information, wouldn’t be able to predict trends or shifts in supply chains, mine for valuable marketing information, keep track of product inventory, meet consumer demands, scale applications, or deploy useful mobile and web applications.

In today’s world of enterprise business, working with data isn’t an option—it’s a must. But with modern applications, the old school relational databases won’t do. Databases such as MySQL, MariaDB, and PostgreSQL don’t scale well enough and they aren’t nearly fast enough to meet the high demands of mobile and cloud-native applications. Nor are those applications capable of delivering the necessary data required by modern businesses.

That’s where the NoSQL database comes into play.

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What is a NoSQL Database?

A NoSQL is a non-relational database that makes it possible to store and retrieve data, using a variety of models that are optimized specifically for applications and services that require large data volume and low latency. NoSQL databases are ideal for enterprise companies looking for databases capable of massive scale.

NoSQL databases are widely used due to their flexibility, scalability, high performance, and highly functional APIs and data types. A NoSQL can accommodate a number of data models, including key-value, document, columnar, and graph. These databases tend to be distributed over multiple servers to accommodate scale, availability, and failover.

Unlike how relational databases store data in tables (with rows and columns), NoSQL stores data in JSON documents.

Types of NoSQL Databases

There are 4 different types of NoSQL databases.

Document databases

This type of NoSQL database stores data in a semi-structured format with descriptions of the data in a document format. These types of databases are widely used with JavaScript and with content management and mobile application data.
Graph database

This type of NoSQL database stores organized data as nodes and edges (which represent connections between nodes). Graph databases are often used in systems that depend on relationships (such as social media and customer relationship management).
Key-value stores

This type of NoSQL database implements a model that pairs a unique key with a value. This simplistic take on NoSQL makes it perfect for use cases that require massive scale (such as session management, web application caching, and multiplayer gaming).
Wide-column stores

This type of NoSQL database uses tables, columns, and rows (as are used in relational databases), but the column naming and formatting can vary from row to row and each column is stored separately on a disk. These databases are widely used in recommendation engines, fraud detection, and logging.

Apache Cassandra is a wide-column NoSQL database.

Where Did Cassandra Come From?

Cassandra began as an internal project at Facebook, by developers Avinash Lakshman and Prashant Malik. The purpose of the project was to power the Facebook index search. In July of 2008, Cassandra was released as an open-source project and in March 2009, it became an official Apache Incubator project.

Why Do You Need a Cassandra Development Company?

Apache Cassandra is the only NoSQL database that offers always-on availability, unmatched read-write speeds, and unlimited scalability. Cassandra also includes features that are certain to attract enterprise businesses, including:

Replication and multi data center replication
Fault tolerance
Read and write tunability
MapReduce support
Cassandra Query Language is simple to use

But most important of all, Cassandra is capable of handling the massive scale enterprise businesses require from a NoSQL database.

What is the Cassandra Query Language?

The Cassandra Query Language (aka CQL) adds an abstraction layer to provide native syntax for collections and common encodings. Language drivers for CQL are available for Java, Node.js, Go, and C++.

The biggest difference between CQL and SQL (which is the most commonly used query language for relational databases) is that CQL can manage both structured and unstructured data, whereas SQL can only manage structured data. Another difference is that, with CQL, data arrives from many sources, whereas SQL data typically arrives from one or few sources.

Both CQL and SQL share some features, such as:

CREATE

Creates objects in a database.
ALTER

Changes the structure of a database.
DROP

Deletes objects from a database.
TRUNCATE

Removes all records from a database table.
RENAME

Renames an object.

There are, however, some subtle differences between how CQL and SQL handle INSERT and UPDATE. In SQL, INSERT inserts data into a table and UPDATE updates existing data within a table.

With CQL, on the other hand, INSERT inserts data into a table if a row doesn’t exist in a table. If, however, the row exists it will simply update the existing row values. UPDATE updates existing data within a table if a row exists. If, however, the row doesn’t exist. it will insert data into a table.

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There are several possible use cases for Cassandra, such as:

Transaction logging, such as purchases, test scores, movies watched, and movie latest location viewed
Storing time-series data
Tracking (such as order status, packages, and billing)
Health tracker data storage
Web and mobile app service history
IoT status and event history
Telematics (IoT for vehicles)
Social network search indexing.
Globally distributed data

Companies That Use Cassandra

There are quite a large number of enterprise companies that use Cassandra, such as Uber, Facebook, Netflix, Instagram, Spotify, Instacart, Reddit, Accenture, and eBay. If your business opts to go with Cassandra, know that you are in good company.

Conclusion

Apache Cassandra is the NoSQL database your company needs for massive data that’s highly available, reliable, fast, and resilient. And because it’s open-source, you can deploy this powerhouse database for free in both on-premise data centers and cloud-hosted virtual servers.