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

dbt Developer Hub

dbt is the standard for creating governed, trustworthy datasets on top of your structured data. We expect that over the coming years, structured data is going to become heavily integrated into AI workflows and that dbt will play a key role in building and provisioning this data. What is MCP?

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How Apache Iceberg Is Changing the Face of Data Lakes

Snowflake

Data storage has been evolving, from databases to data warehouses and expansive data lakes, with each architecture responding to different business and data needs. Traditional databases excelled at structured data and transactional workloads but struggled with performance at scale as data volumes grew.

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AI and Data Predictions 2025: Strategies to Realize the Promise of AI

Snowflake

The next evolution in data is making it AI ready. For years, an essential tenet of digital transformation has been to make data accessible, to break down silos so that the enterprise can draw value from all of its data. For this reason, internal-facing AI will continue to be the focus for the next couple of years.

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Your Enterprise Data Needs an Agent

Snowflake

Agents need to access an organization's ever-growing structured and unstructured data to be effective and reliable. As data connections expand, managing access controls and efficiently retrieving accurate informationwhile maintaining strict privacy protocolsbecomes increasingly complex.

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Simplifying Multimodal Data Analysis with Snowflake Cortex AI

Snowflake

Bridging the data gap In todays data-driven landscape, organizations can gain a significant competitive advantage by effortlessly combining insights from unstructured sources like text, image, audio, and video with structured data are gaining a significant competitive advantage.

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Is Apache Iceberg the New Hadoop? Navigating the Complexities of Modern Data Lakehouses

Data Engineering Weekly

This ecosystem includes: Catalogs: Services that manage metadata about Iceberg tables (e.g., Compute Engines: Tools that query and process data stored in Iceberg tables (e.g., Maintenance Processes: Operations that optimize Iceberg tables, such as compacting small files and managing metadata. Trino, Spark, Snowflake, DuckDB).

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

Snowflake

To give customers flexibility for how they fit Snowflake into their architecture, Iceberg Tables can be configured to use either Snowflake or an external service like AWS Glue as the tables’s catalog to track metadata, with an easy one-line SQL command to convert to Snowflake in a metadata-only operation.

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