Most companies today are storing data and running applications in a hybrid multi-Cloud environment. Analytical systems tend to be centralized and siloed like Data Warehouses and Data Marts for BI, Hadoop or Cloud storage Data Lakes for Data Science and stand-alone streaming analytical systems for real-time analysis.
These centralized systems rely on Data Engineers and Data Scientists working within each silo to ingest data from many different sources, clean and integrate it for use in a specific analytical system or Machine Learning models. There are many issues with this centralized, siloed approach.
To address these issues, a new approach has emerged attempting to accelerate creation of data for use in multiple analytical workloads. That approach is to create Data Products.
This is a decentralized business domain-oriented approach to data ownership and data engineering to create a mesh of reusable Data Products that can be created once and shared across multiple analytical systems and workloads. Multiple Data Architecture options are available to create a Data Products can be implemented in a number of ways.
This 2-day class looks at Data Products in detail and examines its strengths, and weaknesses. It also looks at the strengths and weaknesses of Data Product implementation options.
It also looks at the organizational implications of democratized Data Product development.
Technologies discussed includes Data Catalogs, Data Fabric for collaborative development of data integration pipelines to create Data Products, DataOps to speed up the process, Data Orchestration automation, Data Marketplaces and Data Governance platforms.
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