The enterprise data lake: Better integration and deeper analytics

AgileIss — DataLake

Business Data Lakes have served a great impetus to enterprises by scaling at the rate of cloud for better integrations and deeper analytics resulting in improved business decisions. Unlike traditional data warehouses, enterprises in various industries are favoring a single Hadoop based repository to input and provide data for analytics. It eliminates the need of converting the data needed in case of a data warehouse. Data lake Architecture Experts and industry giants like Facebook, Yahoo, Netflix have taken up enterprise data lake to manage large volumes of data in a flexible and cost effective way.

Improved integration

Enterprises are increasingly getting involved in mobile and cloud based applications and the sensor controlled Internet of Things. Enterprise Data Lake tackles the challenges of data integration in an effective way.

Integration difficulties faced for data proliferation, data silos, etc can be solved by Data Lakes. Enterprises have started to put data extracts in a big and single repository and structuring only the required aspects. Data science groups can form their own opinions on data and structure without placing schemas beforehand.

Applications and services including monolithic systems and fine grained microservices can be added or replaced easily. Data Lake Architecture makes top notch high performance applications.

Deeper analytics

Data programmers are able to tap the stream data and gain valuable real time analytics. The new age tracking tools like Apache Falcon or Revelytix Loom enables them to use one another’s purpose customized data schemas. Analytics derived from Data Lakes are really important because the metadata detailing various views of data keep on collecting.

Data Lake Consulting Experts use the system to get better visibility and develop a complete understanding of customers or deciphering social media trends. Some organizations even use big data sandboxes which are similar to Data Lakes for analysis, though they are restricted in their scope.

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