Remove tags data mesh
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Enabling Data Mesh Principles for Organizational Agility

Snowflake

With demonstrable success across a range of industries, organizations are increasingly pursuing cutting-edge data mesh architectures to enhance self-service data use. Data-as-a-product: By considering data resources through a product lens, teams can adopt practices centered around quality and ease of use.

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Using DataOps To Build Data Products and Data Mesh

Monte Carlo

At our latest IMPACT summit, Roche shared their data mesh strategy for creating reliable data products. The goal of the Roche data team is to maximize the outcomes of customers and patients through data and analytics products. So they agreed to leverage Data Vault 2.0

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How we cut our tests by 80% while increasing data quality: the power of aggregating test failures in dbt

dbt Developer Hub

Testing the quality of data in your warehouse is an important aspect in any mature data pipeline. One of the biggest blockers for developing a successful data quality pipeline is aggregating test failures and successes in an informational and actionable way. However, ensuring actionability can be challenging. on BigQuery.

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A Complete Guide to Scale Your Data Pipelines and Data Products with Contract Testing and Dbt

Towards Data Science

Not too long ago, almost all data architectures and data team structures followed a centralized approach. As a data or analytics engineer, you knew where to find all the transformation logic and models because they were all in the same codebase. There was only one data team, two at most.

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PinCompute: A Kubernetes Backed General Purpose Compute Platform for Pinterest

Pinterest Engineering

In this article, we discuss the PinCompute primitives, architecture, control plane and data plane capabilities, and showcase the value that PinCompute has delivered for innovation and efficiency at Pinterest. For example, removing managed fields will reduce up to 50% data size for PinCompute API calls.

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Data Mesh Implementation: Your Blueprint for a Successful Launch

Ascend.io

Ready or not, data mesh is fast becoming an indispensable part of the data landscape. As data leaders, the question isn’t if you’ll cross paths with this emerging architectural pattern. Consider this your primer to stop overthinking, start acting, and truly harness the power of data mesh.

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Announcing New Innovations for Data Warehouse, Data Lake, and Data Lakehouse in the Data Cloud 

Snowflake

Over the years, the technology landscape for data management has given rise to various architecture patterns, each thoughtfully designed to cater to specific use cases and requirements. These patterns include both centralized storage patterns like data warehouse , data lake and data lakehouse , and distributed patterns such as data mesh.