#data-engineering
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Querying and Acting on Cloud Data with Data Agent Kit
The Data Agent Kit provides a unified framework of MCP servers, agent skills, and IDE integrations that allow AI agents to securely query, analyze, and modify data across BigQuery, Cloud SQL, and Cloud Storage.
Google Cloud TechThe Economics of Web Context: Renting vs. Owning for AI Agents
For high-frequency AI knowledge work, renting context via APIs becomes prohibitively expensive. Building an owned data pipeline often reaches a cost-efficiency tipping point at surprisingly low volumes (around 15,000 queries).
AI EngineerLarge Database Models: Bringing AI Directly to SQL Data
Large Database Models (LDMs) allow AI to perform semantic analysis directly within relational databases, eliminating the need to move data to external platforms for machine learning and enabling SQL-based similarity searches.
IBM TechnologyOntology-Guided Extraction for Knowledge Graph Construction
A framework for building knowledge graphs from heterogeneous documents by using ontologies to guide entity extraction and integrating deduplication directly into the extraction layer to ensure data consistency.
Powering Intelligent Agents with AI-Native Databases
Google Cloud is evolving databases into 'Agentic Data Clouds' by integrating AI primitives—like vector search, graph retrieval, and forecasting—directly into the SQL layer to provide agents with high-fidelity, secure, and real-time enterprise context.
Google Cloud TechMapping Data Science: A Periodic Table Approach
Data science can be decoded by organizing its concepts into a periodic table where rows represent data maturity (from raw to insights) and columns represent analytical activities (from acquisition to evaluation).
Preventing Silent Data Failures in DBT Pipelines
Silent data failures occur when pipelines run successfully but produce incorrect outputs. You can prevent these by implementing generic and singular tests alongside clear model documentation to enforce data contracts.
High-Demand Data Engineering Skills for 2026
Modern data engineering requires moving beyond simple ETL to mastering streaming, cloud-native orchestration, and data quality to build reliable systems that drive business value.
Debugging Silent Production Failures in Python
Production failures often stem from environmental drift and invisible assumptions rather than logic errors. To prevent silent failures, prioritize explicit configuration and defensive data validation.
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