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Introduction to Big Data Platform

Big Data Platform refers to IT solutions that combine severaBig Data Tools and utilities into one packaged answer, and this is then used further for managing as well as analyzing Big Data. The emphasis on why this is needed is taken care of later in the blog, but know how much data is getting created daily. This Big Data if not maintained well, enterprises are bound to lose out on customers. Let's get started with the basics.

ElixirData It provides Flexibility, Security, and Stability for an Enterprise application and Big Data Infrastructure to deploy on-premises and Public Cloud with cognitive insights using ML and AI. Taken From Article: Big Data Integration and Management Platform

Why we need Big Data Platform?

This solution combines all the capabilities and every feature of many big data applications into a single solution. It generally consists of big data servers, management, storage, databases, management utilities, and business intelligence.

It also focuses on providing their user with efficient analytics tools for massive datasets. These platforms are often used by data engineers to aggregate, clean, and prepare data for business analysis. Data scientists use this platform to discover relationships and patterns in large data sets using a Machine learning algorithm. The user of such platforms can custom build applications according to their use case like to calculate customer loyalty (E-Commerce user case), and so on, there are countless use cases.

What are the best Big Data Platforms?

This aims around four letters which are S, A, P, S; which means Scalability, Availability, Performance, and Security. There are various tools responsible to manage hybrid data of IT systems. Some of them are listed below:

  1. Hadoop Delta Lake Migration Platform
  2. Data Catalog Platform
  3. Data Ingestion Platform
  4. IoT Analytics Platform
  5. Data Integration and Management Platform
  6. ETL Data Transformation Platform

Hadoop - Delta Lake Migration Platform

It is an open-source software platform managed by Apache Software Foundation. It is used to manage and store large data sets at a low cost and with great efficiency. 

Data Catalog Platform

It provides a single self-service environment to the users, helping them find, understand, and trust the data source. It also helps the users to discover the new data sources if there are any. Discovering and understanding data sources are the initial steps for registering the sources. Users search for the Data Catalog Tools based on the needs and filter the appropriate results. In Enterprises, Data Lake is needed for Business Intelligence, Data Scientists, ETL Developers where the right data needed. The users use catalog discovery to find the data which fits their needs.

Data Ingestion Platform

This layer is the first step for the data coming from variable sources to start its journey. This means the data here is prioritized and categorized, making data flow smoothly in further layers in this process flow.

Building a Data Ingestion Platform using Apache Nifi could be tedious. Click to explore about, Building Data Ingestion Platform Using Apache Nifi

IoT Analytics Platform

It provides a wide range of tool to work upon big data; this functionality of it comes handy while using it over the IoT case.

Big Data Integration and Management Platform

Our ElixirData provides a highly customizable solution for Enterprises. ElixirData provides Flexibility, Security, and Stability for an Enterprise application and Big Data Infrastructure to deploy on-premises and Public Cloud with cognitive insights using Machine Learning and Artificial Intelligence. Explore to know more about Data Integration and Management Platform

ETL Data Transformation Platform

This Platform can be used to build pipelines and even schedule the running of the same for data transformation. Get more insight on ETL

What are the essential components of Big Data Platform?

There are many essential components which are given as follows:

  • Data Ingestion, Management, ETL, and Warehouse – It provides these resources for effective data management and effective data warehousing, and this manages data as a valuable resource.
  • Stream Computing – Helps compute the streaming data that is used for real-time analytics.
  • Analytics/ Machine Learning – Features for advanced analytics and machine learning.
  • Integration – It provides its user with features like integrating big data from any source with ease.
  • Data Governance – It also provides comprehensive security, data governance, and solutions to protect the data.
  • Provides Accurate Data – It delivers with analytic tools which in turn helps to omit any inaccurate data that has not been analyzed. This also helps the business to make the right decision by utilizing accurate information.
  • Scalability – It also helps scale the application to analyze all time climbing data; it sizes to provide efficient analysis. It offers scalable storage capacity.
  • Price Optimization – Data analytics with the help of a big data platform provides insight for B2C and B2B enterprises which helps the business to optimize the prices they charge accordingly.
  • Reduced Latency – With the set of the warehouse, analytics tools, and efficient Data transformation, it helps to reduce the data latency and provide high throughput.

Java vs Kotlin
Our solutions cater to diverse industries with a focus on serving ever-changing marketing needs. Click here for our Big Data Consulting Services and Solutions

Big Data Platform Use Cases

  • Insurance Fraud Detection – Companies handling a large number of financial transactions use tools provided by this platform to look for any fraud that’s happening.
  • In Real Life – It can be used for various use cases of real-time stream processing like in the field of Media and Entertainment, Weather patterns, the Transportation industry, Banking sector, and so on.


In this section, we provided you with the details of platforms where it is being used in the Big Data environment. Based on your requirement, you can choose from these technologies in managing, operating, developing, and deploying your organization's Big Data securely.

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