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Data logs: The latest evolution in Meta’s access tools

Engineering at Meta

Here we explore initial system designs we considered, an overview of the current architecture, and some important principles Meta takes into account in making data accessible and easy to understand. Users have a variety of tools they can use to manage and access their information on Meta platforms. feature on Facebook.

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Octopai Acquisition Enhances Metadata Management to Trust Data Across Entire Data Estate

Cloudera

Cloudera, together with Octopai, will make it easier for organizations to better understand, access, and leverage all their data in their entire data estate – including data outside of Cloudera – to power the most robust data, analytics and AI applications.

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Modern Data Architecture: Data Mesh and Data Fabric 101

Precisely

Data fabric is a unified approach to data management, creating a consistent way to manage, access, and share data across distributed environments. As data management grows increasingly complex, you need modern solutions that allow you to integrate and access your data seamlessly.

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How Apache Iceberg Is Changing the Face of Data Lakes

Snowflake

Iceberg tables become interoperable while maintaining ACID compliance by adding a layer of metadata to the data files in a users object storage. An external catalog tracks the latest table metadata and helps ensure consistency across multiple readers and writers. Put simply: Iceberg is metadata.

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Introducing the dbt MCP Server – Bringing Structured Data to AI Workflows and Agents

dbt Developer Hub

We are committed to building the data control plane that enables AI to reliably access structured data from across your entire data lineage. We believe it is important for the industry to start coalescing on best practices for safe and trustworthy ways to access your business data via LLM. What is MCP?

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Interesting startup idea: benchmarking cloud platform pricing

The Pragmatic Engineer

The startup was able to start operations thanks to getting access to an EU grant called NGI Search grant. Results are stored in git and their database, together with benchmarking metadata. Benchmarking results for each instance type are stored in sc-inspector-data repo, together with the benchmarking task hash and other metadata.  There

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How Meta understands data at scale

Engineering at Meta

Each product features its own distinct data model, physical schema, query language, and access patterns. Understanding DataSchema requires grasping schematization , which defines the logical structure and relationships of data assets, specifying field names, types, metadata, and policies.