The Fast Way to Make Your Epic Data Work for You

Building performant ETL pipelines to address analytics requirements is hard as data volumes and variety grow at an explosive pace. With existing technologies, data engineers are challenged to deliver data pipelines to support the real-time insight business owners demand from their analytics. Databricks Delta is the next generation of evolution in big data processing from Databricks, the company founded by the original creators of Apache Spark.

As enterprises become more agile, centralization appears more and more a thing of the past waterfall world. This need is equally true with data platforms. This paradigm drove us to build a Data Mesh, this next generation of data platforms for PayPal Credit. This talk will detail quickly go over the evolution of data platforms, highlight the problems of current data platforms, and explain why we decided to build a Data Mesh. I will detail the four principles of the Data Mesh, how we got started, and describe some current and future challenges and how we plan to solve them. Part of the Expert Hour series.

Moving on-premises legacy data and analytics workloads to the cloud is unavoidable if you want to overcome infrastructural constraints, facilitate proactive analytics, and lower costs. You need a service that enables seamless scalability for petabyte-scale data processing, interactive analytics, and machine learning. However, end-to-end, automated workload transformation and optimization on serverless services is not straightforward.

Rapidly evolving business needs drive unprecedented demand for the benefits of master data management (MDM) across a wider array of data, processes and operating entities. They also drive more questions from data and analytics leaders about what they must do to meet those needs and take full advantage of master data management. Join this complimentary data and analytics webinar as Gartner expert Thornton Craig fields your and your peers’ most-pressing questions about MDM, including about the essentials of MDM, implementation red flags and the importance of data governance.