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What is a Big Data Platform?

It refers to IT solutions that combine severs BigData 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 Data if not maintained well, enterprises are bound to lose out on customers. 

What is the need of it?

This solution combines all the capabilities and every feature of many big data applications into a single solution. It generally consists of its 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 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. The list of platforms 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
There are various major challenges that come into the way while dealing with Big Data which need to be taken care of with Agility. Click to explore about our, Big Data Challenges

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. 

IoT Analytics Platform

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

Learn how to Build a IoT Analytics Platform

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.

Data Mesh Platform

xenonstack-elixirdata
ElixirData is a Data Mesh Platform for Enterprise Customers to build and gain insights from data. It comes with layers of Data Catalog and Data Governance based on Enterprise Data strategy built over Virtualisation Layer.

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.

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

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

What are the essential components of it?

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 it from any source with ease.
  • Data GovernanceData Governance 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.

xenonstack-big-data-testing-solution
Building efficient and scalable data platform for real-time and streaming data ingestion and management for driving data-centric business outcomes and actionable insights. Click here for details 

What are the Big Data Analytics Use Cases?

Recommendation engines
  • 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.

Conclusion

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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