Modern data engineering is about integration of all types of data sources to create flexible data lakes and warehouses on cloud. Building a data warehouse is a complex, expensive, and time-consuming exercise. Depending on the scope, it may require a seven-digit expenditure and take months to initially develop and years to become fully enterprise wide in its scope. There is no assurance about the success, as many of these projects are over budget, behind schedule, fail to live up to expectations. The cloud model lowers the barriers to entry-especially cost and complexity wise, that have traditionally limited the adoption and successful use of data warehouses. But it is still enormously time consuming and expensive process to build a truly enterprise wide data warehouse as it involves manual effort and multiple iterations for designing, modeling, integrating, cleansing, transforming and moving massive amounts of data.

Join us to understand the modern data warehousing best practices and how we have helped organizations in reducing latency throughout their data warehousing life cycle. Webinar also covers process to justify and assess data warehousing projects that help organizations arrive at go or no go decision.

Here’s what to expect:

•Modern Data Engineering
•Data lakes, Data warehouses and narrowing scope
•Data Warehousing Life Cycle
•Reference architecture of data warehouse on cloud
•Challenges in Data Warehouse implementation
•Why should a business invest on a data warehouse-Assessing ROI
•best practices I Data Warehousing
•Client Case Study-Marketing data warehouse in 15 days

About the Speaker:

Pradeep Marupati is heading Data Engineering Practice at Sparity, responsible for driving Innovation in the areas of Data Management, Analytics, Cloud and Emerging Technologies.

He has extensive experience in working with Customers across industries to uncover the potential of organization data assets using Next Generation Architecture. He architected several enterprise data platforms to help customers build a strong Data Strategy enabled with advanced analytics to support business decisions. Previously, Pradeep worked as a Functional and Technology Consultant at Cognizant Business Consulting Practice. He has close to 12 years of experience in Business Consulting, Big Data Analytics and Data Engineering with domain expertise in Banking and Capital Markets. He is a thought leader in the enterprise data domain and implemented multiyear warehousing projects.