Rethinking Data Architecture: Data Warehousing, Data Lakes, and More (NEW)
Duration: Full Day Course
There has been a technology revolution in recent years that has dramatically changed the way organizations design, deploy, and manage data-delivery and analytics systems. Hadoop, NoSQL, and the cloud have ushered in a new era of scale-out, elastic, and real-time computing while new data preparation, catalogs, and analysis tools aided by advanced machine learning and search technologies have radically changed the information supply chain.
The traditional world of data warehousing and business intelligence has been flipped upside down. Instead of serial data pipelines of relational data managed, modeled, and batch loaded exclusively by IT, the modern analytics ecosystem supports multiple, continuous real-time data flows designed by data engineers and data analysts close to the business. Abundant data sources and multiple use cases result in many data pipelines—maybe as many as one for each use case. The ability to find the right data, manage data flow and workflow, and deliver the right data in the right forms and at the right speed is essential to success with modern analytics.
You Will Learn
- How modern analytics reshapes the consumerism, business, and data management
- The shortcomings of legacy architectures in the world of modern analytics
- Stages and processes of the modern information supply chain
- How the “data quake” has shaken the foundations of data management
- A framework for modern data management topology
- Architectural principles for data pipelines and data services
- How to adaptfrom traditional BI architecture to a modern analytics ecosystem
- Data, analytics, and BI architects—anyone in an architect role that intersects with data
- Data engineers who define, design, and develop data warehouses, data lakes, operational data stores, data sandboxes, master data hubs, or other enterprise data stores
- Data integration and preparation professionals who define, design, and develop the data stores and processes that provide data for analytics
- Analytics and BI designers and developers who are dependent on fresh and relevant data for every analytics use case