A deliberate architecture for data integration helps keep projects on schedule and on budget; improves data quality and data governance; and increases business users’ confidence in data. But many companies resort to a trial-and-error method of gathering data that is disorganized and incomplete. In this workshop, you’ll learn how to design a hybrid integration architecture that supports data, application, and cloud integration. Data may be stored in relational, columnar, or in-memory DBMS, NoSQL, or Hadoop; logical DW, data lake, or data hub; data virtualization; data services, both inter- and intra-enterprise; and, within analytical processes (data blending or preparation). We will review and contrast integration use cases and best practices spanning technologies. We will review roles and responsibilities of IT, data science teams, “citizen” data scientists, and business “power” users.

Key topics:

• Underlying concepts and architectures
• Use cases and best fit technologies
• Best and pragmatic practices
• Roles and responsibilities of integrators

Many enterprises have siloed integration efforts that are redundant and overlapping. This tutorial will discuss, contrast, and match various types of integration use cases, architectures, technologies, and products.