The 'data lakehouse' is quickly becoming popular in the data analytics community. Data lakehouse architecture combines the benefits of a data warehouse and a data lake. It aims to merge the data warehouse’s data structure and management features along with the flexibility and relatively low cost of the data lake. Watch this panel discussion to learn how the data lakehouse can address the limitations of the data lake and data warehouse architecture to deliver significant value for organizations. Explore why the data lakehouse is an ideal option for enterprise data storage initiatives.

Building a data warehouse is an expensive affair and it often takes months to build one from scratch. There is also a constant struggle to keep up with the large volumes of data that is constantly generated. On top of that, setting up a strong architectural foundation, working on repetitive and mundane data validation tasks and ensuring data accuracy is another challenge. This puts tremendous stress on data teams and data warehouses. Data warehouse automation is intended to handle this growing complexity.

Product analytics is the most critical and complex task for any product team. There are thousands of data points that have to be analyzed carefully while setting up the product analytics foundation and it enables product teams to use data to track, visualize, and analyze user engagement and behavior that can be used to improve and optimize a product experience. However, managing large data workloads can be very challenging as not all data that is collected can be directly used for analytics.

Data Warehouse Automation helps IT teams deliver better and faster results by getting rid of repetitive design, development, deployment and operational tasks within the data warehouse lifecycle. With automation, organizations can accelerate the data to the analytics journey, work more effectively with large amounts of data and save cost. Join this session with Darshan Wakchaure, Global Data & Analytics Competency Head, Tech Mahindra as he shares his insights on the key benefits of Data Warehouse Optimization and how to achieve Data Warehouse Automation at scale.