Real-time data processing is one of the greatest strengths of NoSQL databases. Better known as stream processing, this real-time activity enables the system to execute data processing at the input time to allow for continuous output. In contrast, other databases process this data in batches and can output the data in bulk later.

The problem with batch data processing is that it doesn’t allow for real-time processing for systems like an ATM, online booking systems, credit card applications, or e-commerce platforms. Many modern methods and applications require far faster processing times to deliver the user experience needed.

And with stream processing, you’ll get better NoSQL real-time analytics to make informed decisions and power impressive application features and processes.

1. What is a NoSQL Real-time Database?

A real-time NoSQL database allows you to process data in motion or as you receive or produce data within an application. It’s far simpler and faster than the old way of querying and computing data once you store it within a database.

What is a NoSQL Real-time Database

With stream processing, you can continuously pull analytics and query the data. Plus, you’ll be able to react to the data immediately with actions or machine learning that will remember the event and train the system on what to do if that event occurs again.

For developers, a NoSQL real-time database provides the following opportunities.

  • React to events instantly: all actions and analytics update automatically to provide the most meaningful and valuable experience.
  • Ability to process more data at faster rates: You can process higher data volumes at faster speeds because you don’t have to wait for batch data processing.
  • Decentralized and decoupled infrastructure: you no longer need massive shared databases since each stream processing application can maintain its data.

2. Use Cases for Real-time Data Processing

To better understand the need for stream processing, here’s a look at a few valuable use cases where real-time data can have a significant impact on your application and processes.

  • Recognizing fraudulent credit card charges at the event and blocking them immediately requires real-time data processing and intelligence.
  • They are triggering push notifications to users based on their behaviors and actions within an application.
  • I am making application adjustments based on real-time analysis or sensor data.

3. NoSQL Open Source and Commercial Platforms

If you’re looking for a database that powers real-time processing, the best NoSQL options for your applications are here.

a. BangDB

BangDB is a real-time NoSQL database that features native AI and stream processing with a multi-model approach to provide a robust database for modern applications. Stream data in real-time while training models and completing predictions based on the new data.

b. DynamoDB

DynamoDB allows you to modify records as you ingest them while triggering events. As an Amazon product, DynamoDB is a powerful tool to help you be more agile and deliver a seamless user experience, just like the online retail giant.

c. MongoDB

MongoDB has powerful real-time analytics capabilities so that you can act on your data and information quickly. It’s a popular option for financial services, government, high-tech, and retail businesses. It helps companies to scale rapidly while building a structure for real-time insights and actions.

d. Cassandra

Cassandra came from Facebook and its need for real-time processing to power the social media giant. It is the ideal real-time NoSQL database for applications where you write more data than you read. It is a column-based database with high performance and strong data consistency.

e. HBase

HBase provides real-time access to data using multi-structured approaches to storing it. Process the data in real-time and call upon stored data quickly for responsive applications. This database is frequently used in medical, sports, e-commerce, and the oil industries.