• Aishrith P Rao
• Raghavendra JC
• Dr. Sowmyarani CN
• Dr. Padmashree T

The explosive pace of innovation, corporate apps, media platforms, and Internet of things in recent years has resulted in a massive increase of corporate data. The cloud service provider offered expandable storage capacity to handle company data expansion and gave evaluators, customers, and corporate clients with faster access to data. Classifying, organising, and storing most of this data, as well as processing it to offer business intelligence, is a problem. The diversity, speed, size, and usage of data pose a challenge to interpret big data adequately. Sophisticated business requirements are difficult to implement, produce intelligence, and enable smart info based choices in a timely manner. Consumers often examine enterprise-wide data from multiple database schemas since a massive data lake incorporates inflows of data from a variety of business segments. Based on required data models, consumers can perform complicated computation, execute queries, and make large database connections to create needed metrics. Finding insights from information is generally an exhaustive and high labor task. To achieve an ideal balance between price, efficiency, and availability, a big data architecture in the business demands considerable data modelling methodologies. This study tackles these issues by offering a scalable and efficient measure for organising and storing information in Big Data Lake. It explains some of the fundamental ideas and methodologies for creating scalable data architectures in a distributed setting. It also explains how it overcomes typical obstacles and displays the results.

Big data. Data lakes, Data Warehousing, Spark, Databricks

"A Scalable Big Data Processing Architecture for Enterprises", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 6, page no.e742-e747, June-2021, Available :http://www.jetir.org/papers/JETIR2106666.pdf

"A Scalable Big Data Processing Architecture for Enterprises", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 6, page no. ppe742-e747, June-2021, Available at : http://www.jetir.org/papers/JETIR2106666.pdf