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Scaling Uber’s Batch Data Platform: a Journey to the Cloud with Data Mesh Principles
Some months ago, Uber started the migration to the cloud, on Google Cloud Platform (GCP), of its batch data analytics and machine learning platform. In a recent post on its engineering blog, Uber provided additional information regarding its batch data cloud migration that incorporated crucial data mesh principles.
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Setting up a Data Mesh Organization
A data mesh organization: producers, consumers, and the platform. According to Matthias Patzak, the mission of the platform team is to make the lives of the producer and consumers simple, efficient and stress free. Data must be discoverable and understandable, trustworthy, and shared securely and easily across the organization.
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How Data Mesh Platforms Connect Data Producers and Consumers
A challenge that companies often face when exploiting their data in data warehouses or data lakes is that ownership of analytical data is weak or non-existent, and quality can suffer as a result. A data mesh is an organizational paradigm shift in how companies create value from data where responsibilities go back into the hands of producers and consumers.
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Thoughtworks’ VP of Data and AI Shares Insights for Building a Robust Data Product at QCon London
During his QCon London presentation, Danilo Sato, vice president of data & AI at Thoughtworks, reemphasized the importance of using domain-driven design and Team Topologies principles when implementing data products. This ensures effective data encapsulation in a more complex landscape where data responsibilities are “shifting left” towards the developer.
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Next Generation of Data Movement and Processing Platform at Netflix
Netflix engineering recently published in a tech blog how they used data mesh architecture and principles as the next generation of data platform and processing to unleash more business use cases and opportunities. Data mesh is the new paradigm shift in data management that enables users to easily import and use data without transporting it to a centralized location like a data lake.
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Six Governance Topologies for Data Mesh
Piethein Strengholt, author of Data Management at Scale, recently published an article presenting six data-mesh governance topologies and domain granularity. Each topology adapts the data mesh strategy to balance requirements like data ownership, organization structure, pace of change, technology, and others.
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Data Mesh Principles and Logical Architecture Defined
The concept of a data mesh provides new ways to address common problems around managing data at scale. Zhamak Dehghani has provided additional clarity around the four principles of a data mesh, with a corresponding logical architecture and organizational structure.
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The Distributed Data Mesh as a Solution to Centralized Data Monoliths
Instead of building large, centralized data platforms, corporations and data architects should create distributed data meshes.