Data warehousing used to be IT’s weapon of choice for corralling the “islands of data” and bringing order to the decentralized information chaos. However, the data explosion that organizations are experiencing has illustrated that data integration is no longer a specialized skill restricted to the data warehouse, but an enterprise skill necessity required to support the movement of data across the company’s processing infrastructure.
The growth of real-time analytics, Big Data, the Internet of Things, and cloud-based applications has challenged the traditional approach data gathering and integration. Complexity is no longer confined to data conversion and transformation; the growing quantity of data sources has challenged the existing methods for capturing and managing source data content. Data access and integration technologies such as Data Virtualization, Event Stream Processing, the Enterprise Service Bus, MDM (master data management), and ETL (extract, transformation, and load) are offering companies new and creative ways to address data capture, integration and delivery.
In this session, Evan Levy will identify the architectural trade-offs and issues associated with each solution—from performance and functionality to flexibility and efficiency. He will present examples and case studies where these new integration architectures and methods have been implemented. Along the way, he’ll pepper the course with architectural examples that illustrate new ways of solving often age-old data integration dilemmas.
You Will Learn
- Core data integration functions
- Tools of the trade: ETL, Data Virtualization, Event Stream Processing, Enterprise Service Bus, and MDM
- Architecture, design, and implementation concepts
- Supporting data integration beyond the data warehouse
- Database and Data Warehouse architects, Data Management staff; Data Warehouse Developers; IT architects, Project Managers