The purpose of this workshop is to make a introduction of the Data Ops framework as a tool to analyze companies’ data architectures, and show, some “Best Practices” outstanding data modeling architectures such as DataVault, which enhance the efficiency of our information consumption systems.
Addressed to:
– Analysts and experts in data modeling, – Data architecture, – Data warehouse, – Datalake, – Data management, – Data engineers, – Data governance managers, – People close to the CDO area, etc.
Workshop dynamics:
At the beginning of the session there will be two theoretical lessons, with a break at the end. Subsequently, some practical exercises will be carried out, and finally a short summary and space for questions and comments.
- The first lesson will be about Data Ops; We will discuss how a framework can help us establish the foundations of the key elements of our data architecture, and see what points we could enhance or add strategic value. We will also see some cases where we apply the use of some technologies in some clients in which we use the DataOps Framework. Finally, we will allow for questions and comments.
- The second lesson will be about Data Vault; We will show how by applying these “Best Practices”, which are increasingly being used in large companies today and not only abroad, we can structure and model data to provide a broad, versatile and efficient view of consuming data. We will also see how the world of Data Vault links together with Data Ops and thus get the maximum benefit versus other techniques such as Inmon / Kimball. Finally, we will allow for questions and comments.
- We will take a 10-minute break.
- In the third part of the session we will do a practical and simple modeling exercise of a pre-established example, applying DataVault methodology using DataOps techniques.
- Next, we will see through an introduction and practical demonstration how by bringing together both worlds, DataOps and DataVault and through metadata, the DV model can be built, how to ingest and automate information by building a DWH in an analytical environment on the Snowflake platform.
- Finally, we will recap the topics covered, and we will open the way for questions or comments so that we can delve deeper into a specific topic.