In the modern world of data, accuracy becomes everything and in analytics, software’s speed comes decisively. Every industry is collecting countless data points every day and it becomes significant to interpret this information and refined it into operational data, as soon as possible. Choosing appropriate data handling tools is a difficult task and it becomes more difficult for large volumes of data.

Faster data analysis

In the roaring competitive world of business, real-time data analytics allows a businessman to react without delay. It analyses the data as soon as it comes available. That is the users can be alert immediately when the data enters into their system. They get consumable insights in decision-making without any detainment. Fearing the associated challenges in the industry, companies are moving towards real-time data analytics which earns them a competitive advantage and makes them profit. Examples of real-time analytics include:

● Real-time credit scoring in order to help the financial institutions

● Customer Relationship Management(CRM) to maximize satisfaction and business results

● Fraud detection at the point of sales

● Targeting individual customers in retail outlets

Businesses are extensively changing to digital and real-time big data analytics handles the growing diversity of data through big data ingestion.

What is Data Ingestion?

The process of importing, transferring, loading and processing data that comes from a large number of sources and stores this data for further use is data ingestion. In other words, it is the transferring of unstructured data to a system where it can be stored and analyzed as Hadoop. Data ingestion in Hadoop is the beginning of the data transfer where data is taken from various data sources and files and put into the Hadoop. Many companies found it as an intricate task as Hadoop is an open source.

Based on the source and the destination, data ingestion in Hadoop can be continuous, asynchronous, real-time or batched. Even the source and destination may differ in format or protocol and requires some sort of transformation or conversion. Diverse data ingestion tools provide a framework that permits the company to collect and process data from a wide range of sources. In addition, it modifies and formats the data for analytics and storage purposes. The features which data ingestion tools must incarnate are:

● Data extraction and Processing

● Dataflow visualization

● Scalability

● Multiplatform support and integration

● Advanced security features

Streamanalytix: A Big Data Analytics Platform

With all the characteristics required for better data ingestion, Streamanalytix provides a platform for the companies who want to deal with substantial amounts of data from various sources in an effective manner

Streamanalytix is the industry’s first open-source based, enterprise-grade, multi-engine platform. Through their first-class applications, they provide a rapid and easy development in real-time analytics. It is designed in a way to build and deploy the streaming analytics applications quickly for any industry or for any data.

The big data ingestion offered by Streamanalytix can be connected with any data source and any data storage system for both streaming and batched.