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Data Mesh: Implications for Data Product Teams and Business Outcomes

Data Mesh architecture from 30,000 foot view
Data Mesh architecture from 30,000-foot view
Obstacles that delay analytics cycle time
Obstacles that delay analytics cycle time

Delegate as Much to the Cloud Provider

Data preparation, ETL and integration between multiple systems takes up 70 percent of a project’s cost. Every hour spent on integration of different solutions or infrastructures is time lost on data monetization and business value creation. To quickly cut down on deployment time and costs, leverage cloud native solutions, like:

  • AWS: Redshift
  • Google: GCP
  • Microsoft: Synapse or Databricks

Understanding Two Different Worlds: Applications and Data/AI

In the application world, there’s DevOps, an approach to software development that accelerates the build lifecycle by automating integration, testing, and deployment of code. Software developers building applications understand this quite well.

Data Mesh architecture from 30,000 foot view
Data Mesh architecture from 30,000 foot view
  • The data producer has business and the domain knowledge, while
  • The central team has the data engineering knowledge, and
  • The data consumer has the analytical and ML knowledge.
  • Remove bottlenecks in the central team,
  • Reduce data duplication,
  • Reduce ETL data pipeline,
  • Improve speed to market and rapid prototyping,
  • Reduce stale data, and
  • Centralize data security.

Four Principals to Consider

This paradigm establishes data teams by domain, where data producers, consumers, and the central team form one collaborative team across the entire business and IT department. The founder of Data Mesh, Zhamak Dehghani, takes a principled approach to close the divide between the operational and analytical worlds:

  • Principle 1: Domain oriented decentralization of data ownership and architecture.
  • Principle 2: Data as a product.
  • Principle 3: Create a self-serve data platform to enable autonomous domain oriented data teams.
  • Principle 4: Create a federated governance to enable ecosystem and interoperability.
The 30,000 ft view of the monolithic data platform

A Note on No/Low Code Data Management Tools

Emerging data management tools have native integration with data governance, DataOps, AI/ML. The no code data visualization interface allows anyone to access and manage data across the organization, thereby satisfying requirements of a Data Mesh platform.

Siloed hyper-specialized data platform team
Siloed hyper-specialized data platform team

A Note on Enterprise Knowledge Graphs

In the modern data warehouse approach, you’re pulling data on premise from external data sources. For example, you get the data in your cloud provider onsite from a data lake, and then you clean that data. Then you serve the different use cases or analytics that need this data.



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Abdus Salaam Muwwakkil

Abdus helps data leaders and innovation teams deploy problem solving solutions to unlock the talents of their people and establish competitive advantages.