What SAP Tools Are Most Popular in Big Data?

If you're looking to bring Big Data into your business, you should definitely take a look at these options.
November 13, 2020
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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 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.

However, we’re going to focus on the SAP side of things. 

Although SAP is also a software for managing business operations and customer relations, we want to address Systems Applications and Products (and not the European multinational company). 

In order for SAP to work, a number of pieces must be used in conjunction with one another. As you probably suspect, there is quite a large selection of such tools, but we’re going to focus on some of the more popular options, so you have a better idea of where to start your search for the pieces to put your SAP solutions together.

Once you know what you’re looking for, you can either make it happen via your own developers or hire an outsourced team of developers to get the job done. Without further ado, let’s take a look at some SAP tools.


Apache Hadoop

Apache Hadoop (often referred to as 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 is comprised of the modules:

  • 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 is a NoSQL database, which means it isn’t bound by the structure in typical SQL databases. MongoDB is often considered the database for Big Data. This open-source database can handle real-time 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).  



SAP HANA (High-Performance Analytic Appliance) is a Relational Database Management System developed by the SAP company. The primary purpose of HANA is to store and retrieve data as it is needed by applications. 

Aside from HANA’s ability to perform analytical queries on transactional data as the data is added in real-time, the most beneficial aspect of this tool is its compatibility with other technologies (databases, hardware, and software). This versatility means your company can employ powerful analytical abilities without having to sacrifice the tools you already use.


Apache Spark

We’re back with Apache. This time around the tool in question is Spark, which is a distributed, general-purpose computing framework employed as a unified analytics engine for large-scale data processing. 

Spark is capable of performing processing tasks on massive data sets, by distributing the task across a cluster of computers. Because of its clustering nature, Spark has become one of the most relied-upon frameworks in Big Data. And thanks to native bindings for Java, Scala, Python, and R, there’s no limit to what your development team can do with this tool.

Spark consists of two main components:

  • Driver – converts code into multiple tasks to be distributed to worker nodes.
  • Executors – run on nodes and execute assigned tasks.

Spark most often is run on top of Hadoop YARN, for a robust cluster management system for allocating on-demand workers. 



Elasticsearch makes it possible for companies to actually search, analyze, and report on the massive amounts of data they’ve collected. This software offers a distributed RESTful search and analytics engine capable of being employed for numerous use cases. Elasticsearch can be used for web search, log analysis, and Big Data analytics.

The primary features of Elasticsearch are:

  • Horizontal scalability
  • Rack awareness
  • Cross-cluster replication
  • Audit logging
  • CLI tools
  • Numerous database clients available
  • Scalable and resilient
  • Integrates with Hadoop and Spark
  • Includes a robust plugin system
  • Single Sign-On
  • Third-party security integration
  • Snapshot and restore

But the single most important aspect of Elasticsearch is its ability to make Big Data analytics easier for businesses. With real-time analytics at the heart of Elasticsearch, businesses can monitor (and act on) things like page views, website navigation, shopping cart use, and all types of online activity. With Elasticsearch,  you can overcome many of the challenges of Big Data more easily.



We’ve only just scratched the surface of the tools used in Big Data, but what you see in this list are some of the most widely employed. If you’re looking to bring Big Data into your business, you should definitely take a look at these options.

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