Data plays a crucial role in the commercial sector today. In fact, without the abundant availability of data, the industry would be akin to the marketing limitations of the 1990s or earlier, when departments lacked the resources required to execute intricate operations.
Fortunately, during that period, every company had similar resources at its disposal. Hence, the pace of a company’s expansion didn’t have much impact as it was the standard practice then.
A decade ago, marketing teams were the sole drivers in completing intricate tasks. But today, thanks to technological advancements, companies have access to a variety of tools that facilitate these operations. SAP, an inclusive suite of tools, simplifies the management of marketing activities like never before.
What is SAP and how does it work?
SAP stands for “Systems, Applications and Products” and is typically used synonymously with ERP (Enterprise Resource Planning), as both represent a similar idea. SAP primarily focuses on compiling, storing and analysing data, while some argue that ERP and SAP cannot be distinguished from one another.
Why is that so?
ERP (Enterprise Resource Planning) is a technology-based strategy to manage business processes in a live environment. However, a company may face challenges if it uses both ERP tools and SAP technologies to handle Big Data, as lack of integration between these systems can cause communication issues, often leading to interchangeable use of the terms SAP and ERP.
However, we should focus on SAP-related problems.
We would like to talk about Systems, Applications and Products (SAP). This software is employed to regulate business procedures and customer relations, and should not be confused with the eponymous European multinational company. For more on this topic, please refer to our articles How to be well prepared and rapidly address problems during celery manufacturing and 5 biggest roadblocks to a successful CRM rollout, and how to overcome them.
Different components are necessary for effective SAP implementation. Although numerous resources exist, we will discuss some of the more prevalent ones to assist you in pinpointing any missing elements in your SAP solutions.
Once your requirements are well-defined, you can either choose to employ in-house programmers to develop the required solutions or partner with a third-party company to acquire the necessary programming know-how. Let us now explore some SAP applications.
Hadoop: An Apache Project
SAP may find Apache Hadoop an indispensable resource. Hadoop is a software framework that permits the storage and administration of vast amounts of data spanning distributed commodity computer nodes. It is a highly compatible tool for big data as it can handle any data set with its extensive storage capacity. Besides, its data storage element can accommodate both structured and unstructured data, making it a viable alternative to many traditional databases. For more on this topic, visit our article on Alternatives to AngularJS in 2023.
Hadoop serves other purposes beyond data storage. Some of its components include:
- Hadoop Common comprises the library and collection of utilities providing backend support to the remaining components in the Hadoop framework.
- Hadoop DFS is the Hadoop file system that can operate on standard computers.
- Hadoop YARN: This is the resource management and job scheduling component of Hadoop. It is also referred to as Yet Another Resource Broker (YARN).
- The Hadoop MapReduce framework allows the creation of Hadoop-compatible software.
What makes Hadoop a popular choice for big data?
- Effortlessly handles and manipulates massive amounts of data of any type.
- Provides data security and computational process stability in the event of hardware failures.
- Adapts to diverse data requirements.
- Its greatest feature is scalability.
Moreover, Hadoop is an open-source software option that comes at no cost.
MongoDB
MongoDB is a NoSQL database engine, which indicates that it is not restricted to the schema used by relational databases. It is widely recognised as a top solution for big data, providing complementary support for MapReduce calculation, extensive horizontal scaling, and real-time data analysis.
MongoDB is an indispensable constituent of big data as it is compatible with several popular programming languages including JavaScript, Ruby, and Python.
System Analysis and Planning using SAP HANA
The SAP High-Performance Analytic Appliance (HPA) is a Relational Database Management System (RDBMS) developed to ensure data security and accessibility to applications whenever required.
HANA’s most valuable feature is its adaptability and compatibility with other technologies like (databases, hardware, and software). This enables companies to leverage potent analytical capabilities without having to change their current resources. Furthermore, HANA enables the execution of analytical queries on transactional data in real-time, as it is being updated.
Using Apache Spark for Cloud Computing
Apache is making a comeback, and Spark is the preferred tool as it can act as a unified analytics engine for processing immense data volumes.
Spark is celebrated for its ability to efficiently handle enormous datasets by breaking them into small, manageable components. This is why it is among the most favoured Big Data frameworks available, as it includes native bindings for Java, Scala, Python, and R, giving developers the capability to accomplish any objective.
Spark is composed of two primary components:
- The driver is the segment of the system that takes source code and converts it into various tasks that can be directed to different worker nodes.
- Executors, which are processes that operate on nodes and execute specific commands, carry out tasks.
To utilize Hadoop’s YARN, a potent cluster management system for deploying on-demand workers, Spark is frequently deployed over it.
Elasticsearch
Elasticsearch enables companies to efficiently search, analyse, and report on their vast amounts of accumulated data. This platform offers a RESTful distributed search and analytics engine that is suitable for diverse applications. It is highly adaptable, making it an ideal option for Big Data analytics, online search, and log analysis.
The primary functions of Elasticsearch include:
- Horizontal scalability
- Awareness of the rack
- Multi-cluster replication
- Logging of audits
- CSV (Comma Separated Values) tools
- A vast array of database client options are available.
- Durable and capable of scaling
- Support for both Hadoop and Spark ecosystems
- Constructed with a robust extension architecture
- Single sign-on
- Integration of security systems with third-party applications
- Data backup and restoration
Elasticsearch is an invaluable tool for aiding enterprises in the analysis of vast datasets. Its real-time analytics capabilities enable organizations to monitor real-time customer activity, such as website navigation, page views, and shopping cart usage. If you are grappling with big data, Elasticsearch can provide a viable solution.
Conclusion
This catalogue offers a glimpse into some of the most widely used tools for handling big data. If you are planning to introduce big data into your organization, it is advisable to investigate these alternatives in greater depth.
