Unify Your Data Warehouse and Data Lake with AWS

Level: 300

As the amount of data accumulates, customers have stored it in different silos, making it difficult to do analytics. To make it easier, customers want all of their data in a single repository, i.e., a data lake. However, even well implemented data lakes do not replace the need for a data warehouse for well-defined, high-concurrency, high-speed, responsive analytics and reporting, using their existing SQL skills and applications. Most data warehouses impose the limitation of loading data into a proprietary format inside the data warehouse before it can be analyzed, perpetuating the silos that customers wanted to break by implementing the data lake in the first place. This tech talks demonstrates a new paradigm for data warehousing that enables customers to answer the most complex analytical questions by querying exabytes of data in their Amazon S3 data lakes and Amazon Redshift data warehouse with familiar SQL and no data movement or transformation required. It also demonstrates how you can break data silos among various analytical engines as well as among various lines of business in your organization.