Tuesday – dfakto Hands On: Data Mesh in Action

Tuesday – dfakto Hands On: Data Mesh in Action

Data Mesh in Action:

Leveraging Data Vault for Next-Generation Data Integration using dFakto’s dataFactory

dFakto chose more than 10 years ago the Data Vault Standard to operate for its intrinsic qualities, data model growth without additional complexity, storage of historized data and data quality improvement. WorldWide Data Vault Consortium is the annual gathering to meet and exchange with peers and share dFakto’s point of view on data vault methodology and its evolution.

We’re thrilled to announce our participation once again at the Worldwide Data Vault Consortium (WWDVC). We look forward to engaging with the Data Vault 2.0 community.

In this hands on session, we introduce dataFactory, the first certified Data Vault automation tool, designed to transform the implementation of domain models within a Data Mesh architecture. Led by Vincent and Marc, this session offers a practical, step-by-step guide to efficiently construct data models and develop information marts, tailored for participants of all levels of expertise, including those new to our technology.

One of the key advantages of Data Mesh is its ability to foster autonomy in data governance within individual domains. In this session, you’ll learn how to be self-sufficient in managing data within your domain, ensuring that your data assets are governed effectively, fully-compliant all the time with the DV2, while enabling seamless collaboration and access across domains.

Moreover, an integral part of this session is dedicated to the application of data quality controls, aimed at enhancing the integrity and reliability of data products over time.

Whether you’re a seasoned Data Vault practitioner, a data architect, engineer, or scientist, this session will equip you with the tools and knowledge to take your data integration efforts to the next level.

Major Topics Covered:

  • Implement the data model of a domain
  • Map the source to the model
  • Build your first data quality control
  • Create an information mart to build the first data product
  • Deploy and automate all data processes
Technical Track