Big Data. Where would we be without it? Businesses would certainly not be nearly as competitive. In fact, businesses would look more like they did back in the ’90s or even earlier, when companies were constantly under the thumb and guidance of a marketing department that had rudimentary tools to handle monumental tasks.

Fortunately, every business had the same tools, so it didn’t matter if your business grew at a snail’s pace, because that was de rigueur at the time.

That was then, this is now, and in the now businesses have tools that can do the job entire marketing departments couldn’t do a decade ago. Those tools all come together in the form of SAP.

What is SAP?

SAP, as applied to data processing, stands c level contact list for Systems Applications and Products. Many often use SAP and ERP (Enterprise Resource Planning) interchangeably, because those two paradigms often have the same goal. But SAP is more about how the data is collected, stored, and used. To some, however, ERP is an integral component of SAP.

But why?

Simply put, ERP is the real-time management of business processes that is mediated by technology. But here’s the thing: if a business is using ERP tools to manage business processes, and they then use SAP tools to manage Big Data, it becomes impossible for one to inform the other if they don’t come together. That’s why you so often see SAP and ERP as interchangeable ideas.

Apache Hadoop

Apache Hadoop (often referred to as generate brand positioning or website visits simply Hadoop) might well be one of the most significant tools in the SAP toolkit. Hadoop is a framework for storing and managing data on clusters of off-the-shelf hardware. Hadoop offers massive storage for nearly any kind of data. Unlike many standard databases, the data storage portion of Hadoop can work with both structured and unstructured data.

Of course, Hadoop is more than just about storing data.

• Hadoop Common – the collection of utilities and libraries that support all other modules in the framework.
• Hadoop Distributed File System – is the Hadoop file system designed to run on commodity hardware.
• Hadoop YARN – is the resource management and job scheduling component for Hadoop. YARN stands for Yet Another Resource Negotiator.
• Hadoop MapReduce – is the framework for writing applications to work with Hadoop.

Hadoop is so popular for Big Data because it:

• Has the ability to store and quickly process massive amounts of any kind of data.
• Provides data and processing with protection against hardware failure.
• Is flexible with the data it stores.
• Is highly scalable.

Hadoop is also open-source and free to use.

 

MongoDB

This open-source database can handle real-time mobile list data analysis and features, uses a distributed key-value store, scales horizontally (while preserving as much functionality as possible), and works with MapReduce calculation.

But one of the most important aspects that make MongoDB so important to Big Data is that it blends seamlessly with a number of the most popular programming languages (such as JavaScript, Ruby, and Python).