Traditional data warehouses were built with online transaction processing (OLTP) technology and architecture that today can be 15-20 years old. These data warehouses were right for the time, but not designed to handle the volume, variety and velocity of information that businesses are faced with today. Over the years we tried to fit more and more data into these warehouses, and the result has been an over-burdened system offering a not very optimal way to access data in today's data-driven world.

Companies can benefit from new technologies and innovative approaches to modernizing the warehouse for improved performance. There are new and existing technologies that, when blended effectively, can deepen business understanding through data analytics and support business needs. Moving to the data lake paradigm can mean scalable, agile performance and storage, and processing solutions for all data – whether structured, semi-structured, or even streaming data. In this session, I share:

• Strategies to move from traditional data warehouse culture to the Big Data Lake
• Guidance for designing a process for how the EDW can respond to the Big Data Analytics need
• Understanding and maximizing the relationship of big data, Hadoop, and analytics
• Steps for Using the Data Lake to “Put it all Together” - Ingest, Organize/Define/Complete, Blend and Learn, Report
• Case study examples of how establishing a Data Lake can be a game-changer for today’s organization
• Lessons Learned from client engagements