If you want to bring Big Data into your business, you should definitely take a look at these options.
Big data. Where would we be without it? Companies would certainly not be as competitive. In fact, companies would be more like those in the 90s or even earlier, when companies were constantly under the control and guidance of a marketing department that had rudimentary tools to handle monumental tasks.
Fortunately, all companies had the same tools, so it didn't matter if your business grew at a snail's pace, because that was mandatory at the time.
That was then, this is now, and now companies have tools that can do the work that entire marketing departments couldn't do a decade ago. All these tools come together in the form of SAP.
What is SAP?
SAP, applied to data processing, stands for Systems Applications and Products. They often use SAP and ERP (Enterprise Resource Planning) interchangeably, because these two paradigms often have the same goal. But SAP is more about how data is collected, stored and used. For some, however, ERP is an integral component of SAP.
But why?
Simply put, ERP is the real-time management of business processes mediated by technology. But the problem is this: if a company uses ERP tools to manage business processes and then uses SAP tools to manage Big Data, it becomes impossible for one to inform the other if they don't come together. This is why you often see SAP and ERP as interchangeable ideas.
However, let's focus on the SAP side.
Although SAP is also software for managing business operations and customer relationships, we want to address Systems Applications and Products (not the European multinational company).
For SAP to work, several parts must be used together. As you probably suspect, there is a fairly large selection of these tools, but we're going to focus on some of the most popular options so you have a better idea of where to start your search for the pieces to put your SAP solutions into. together.
Once you know what you're looking for, you can make it happen through your own developers or hire an outsourced team of developers to do the work. Without further ado, let's take a look at some SAP tools.
Apache Hadoop
Apache Hadoop (often called simply Hadoop) may well be one of the most important 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 virtually any type of data. Unlike many standard databases, the data storage part of Hadoop can work with both structured and unstructured data.
Of course, Hadoop is more than just storing data. Hadoop is composed 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 of Hadoop. YARN stands for Yet Another Resource Negotiator.
- Hadoop MapReduce – is the framework for writing applications to work with Hadoop.
Hadoop is very popular for Big Data because:
- It has the ability to quickly store and process large amounts of any type of data.
- Provides data and processing with protection against hardware failures.
- It is flexible with the data it stores.
- It is highly scalable.
Hadoop is also open source and free to use.
MongoDB
MongoDB is a NoSQL database, meaning it is not tied to the structure of typical SQL databases. MongoDB is often considered the database for Big Data. This open source database can handle real-time data analytics and capabilities, uses a distributed key-value store, scales horizontally (preserving as much functionality as possible), and works with MapReduce computation.
But one of the most important aspects that make MongoDB so important for Big Data is that it pairs seamlessly with a number of the most popular programming languages (like JavaScript , Ruby , and Phyton ).
SAP HANA
SAP HANA (High-Performance Analytic Appliance) is a relational database management system developed by the SAP company. The main purpose of HANA is to store and retrieve data as needed by applications.
In addition to HANA's ability to perform analytical queries on transactional data as 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 skills without having to sacrifice the tools you already use.
Apache Spark
We're back with Apache. This time, the tool in question is Spark, which is a general-purpose, distributed computing framework employed as a unified analytical engine for large-scale data processing.
Spark is capable of performing processing tasks on massive sets of data, distributing the task across a cluster of computers. Due to its cluster nature, Spark has become one of the most reliable frameworks in Big Data. And thanks to native bindings for Java Escala, 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 perform assigned tasks.
Spark typically runs on Hadoop YARN, for a robust cluster management system for on-demand worker allocation.
Elasticsearch
Elasticsearch enables companies to search, analyze and report on the massive amounts of data they have collected. This software offers a distributed RESTful search and analysis engine capable of being employed across multiple use cases. Elasticsearch can be used for web search, log analysis, and big data analysis.
The main features of Elasticsearch are:
- Horizontal scalability
- Shelf Awareness
- Cross-cluster replication
- Audit log
- CLI Tools
- Multiple 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 most important aspect of Elasticsearch is its ability to make Big Data analysis 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 usage, and all types of online activity. With Elasticsearch, you can overcome many of the challenges of Big Data more easily.
Conclusion
We've just scratched the surface of the tools used in Big Data, but what you see in this list are some of the most widely used. If you want to bring Big Data into your business, you should definitely take a look at these options.
Source: BairesDev
