Practical Guidelines for Implementing a Data Mesh

Data Catalog, Data Fabric, Data Products, Data Marketplace


Most companies today are storing data and running applications in a hybrid multi-Cloud environment.  Analytical systems tend to be centralised 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 centralised 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 centralised, siloed approach including multiple tools to prepare and integrate data, reinvention of data integration pipelines in each silo and centralised data engineering with poor understanding of source data unable to keep pace with Business demands for new data. Also Master Data is not well managed.

To address these issues, a new approach has emerged attempting to accelerate creation of data for use in multiple analytical workloads. That approach is Data Mesh.

This 2-day class looks at Data Mesh in detail and examines its strengths, and weaknesses.  It also looks at the strengths and weaknesses of Data Mesh implementation options. Which Architecture is best to implement this? How do you co-ordinate multiple domain-oriented teams and use common data infrastructure software like Data Fabric to create high-quality, compliant, reusable, data products in a Data Mesh. Also, how can you use a data marketplace to share data products? The objective is to shorten time to value while also ensuring that data is correctly governed and engineered in a decentralised environment.

It also looks at the organisational implications of Data Mesh and how to create sharable data products for Master Data Management and for use in multi-dimensional analysis on a Data Warehouse, Data Science, Graph Analysis and real-time streaming Analytics to drive business value? 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.

What you will learn

  • The problems caused in existing analytical systems by a hybrid, multi-Cloud data landscape
  • Strengths and weaknesses of centralised data Architectures used in Analytics
  • What is a Data Mesh and how does it differ from other approaches?
  • What benefits does Data Mesh offer and what are the implementation options?
  • What are the principles, requirements, and challenges of implementing a Data Mesh?
  • How to organise to create data products in a decentralised environment so you avoid chaos
  • The critical importance of a Data Catalog in understanding what data is available
  • How business glossaries can help ensure data products are understood and semantically linked
  • A Best Practice organisational model for coordinating development of data products across different domains to succeed in implementing a Data Mesh
  • What software is required to build, operate and govern a Data Mesh of data products for use in a Data Lake, a Data Lakehouse, a Data Warehouse and other analytical workloads?
  • What is Data Fabric software, how does it integrate with Data Catalogs and connect to data in your data estate
  • An Implementation methodology to produce ready-made, trusted, reusable data products
  • Collaborative domain-oriented development of modular and distributed DataOps pipelines to create data products
  • How a Data Catalog, Generative AI and automation software can be used to generate DataOps pipelines
  • Managing data quality, privacy, access security, versioning, and the lifecycle of data products
  • Pros and cons of different data Architecture options for implementing a Data Mesh
  • Publishing semantically linked data products in a data marketplace for others to consume and use
  • Federated data Architecture and data products – the emergence of Lakehouses open tables as a way for multiple analytical workloads to access shared data products
  • Persisting Master Data products in an MDM system
  • Consuming and assembling data products in multiple analytical workloads like Data Warehouses, Data Science and Graph Analytics to shorten time to value
  • How to implement federated Data Governance

Main Topics

  • What is a Data Mesh and why is it needed?
  • Methodologies for creating Data Products
  • Using a Business Glossary to define Data Products
  • Standardising development and operations in a Data Mesh
  • Building DataOps Pipelines to create Multi-Purpose Data Products
  • Implementing Federated Data Governance to produce and use compliant Data Products
Mike Ferguson




11 - 12 Nov 2024

Timing: from 9.30 am to 5 pm Italian time


Online event

Book Event

Participation fee - 1200€
Available Places: 100
The "Participation fee - 1200€" ticket is sold out. You can try another ticket or another date.
Registration 30 days before the seminar date: 5% discount - € 1140
Available Places: 100
The "Registration 30 days before the seminar date: 5% discount - € 1140" ticket is sold out. You can try another ticket or another date.
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