Скачать или смотреть Why Data Modeling Is So Important For Data Mesh

Many organizations are looking at creating reusable data products in a data mesh to help shorten time to value and support multiple analytical workloads. Data mesh relies on a decentralized approach to data product development where domain-oriented citizen data engineers build the pipelines to integrate data to produce data products as opposed to central IT doing everything. However, despite the attraction of the data mesh approach there are several barriers to adoption.

These include lack of governance, and lack of skills especially in areas like data modeling. For data mesh to succeed, citizen data engineers need to understand data concept modeling and how to create a data model representing the schema of each data product. The objective is to create data products that can snap together like lego bricks so that consumers can quickly select and assemble the data products they need and deliver value.

This video looks at data mesh and explains why data modeling is so important in building data products. It also shows how data product data models represent ‘schema components’ that will always work together allowing consumers to quickly combine data products to create the insights they need. Learn how data modeling, coordination across teams and governance can help create data products in a data mesh that become reusable building blocks for use in multiple analytical workloads in a data-driven enterprise.

The Presenters: Mike Ferguson is Managing Director of Intelligent Business Strategies Limited. As an independent IT industry analyst and consultant, he specializes in BI/Analytics and data management. With over 40 years of IT experience, Mike has consulted for dozens of companies on BI/Analytics, data strategy, data architecture, data governance, and data management.

Jamie Knowles is the Senior Product Marketing Manager at IDERA with 20+ years experience in the field including his involvement in the evolution of enterprise data architecture and modeling and data governance.
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