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Explores production agents, covering observability, type safety, and practical MCP challenges relevant to data engineering workflows.

Pigment's GenAI team employs an architecture with OpenAI, LangChain, and Langfuse, addressing performance-latency trade-offs in product-focused AI projects.

Application of visual language models to structure product image data at marketplace scale, highlighting data engineering approaches for large-scale processing.

Explores enterprise AI governance challenges, emphasizing scalable processes, continuous observability, and platforms like OneTrust for managing data responsibility and AI agents.

Exploration of the F3 file format designed to address Parquet and ORC limitations through decoupled layout and self-decoding WebAssembly kernels, enhancing efficiency and interoperability for data lake workloads.

Discussion on 2026 predictions in data engineering and the Kimball vs Inmon debate, covering data modeling and future trends.

Podcast featuring twelve non-fiction books on technology, power, and manufacturing reality, recommended by industry experts.

Risk-based data quality approach to optimize testing, focusing on critical aspects and pipeline automation for data engineers.

Interview with Jeremy Cohen from dbt Labs about dbt's adoption as a data transformation tool, covering its origins, success factors, GenAI impact, and trends like Iceberg and Semantic Layer.

Tiankai Feng discusses creating musical parodies about data engineering, using humor to address data governance, analytics, and pipeline challenges, including their debut at the Coalesce 2022 conference.

This podcast compiles key 2025 insights on AI's impact in data engineering, covering role evolution, human-AI hybrid teams, data governance, and agentic systems.

Discussion on ad retargeting and machine learning engineering, providing technical insights into data systems and ML applied to marketing.

Discussion on how metadata catalogs like OpenMetadata and Collate evolve into a context layer for agentic AI systems, enabling discovery, observability, governance, and automation through AI agents for documentation and data quality.

This article discusses the transition from data analyst to product manager, focusing on mastering the distinction between problem definition and solution design, with implications for data project management.

Discussion on the importance of building with good taste and shipping loved solutions, as AI models advance rapidly making today's work potentially obsolete tomorrow.

This article discusses shifting mindsets in data engineering from measuring outputs to optimizing outcomes for greater impact, focusing on technical practices and methodologies.

Bauplan and Orchestra simplify Apache Iceberg adoption with a Python-native serverless platform for creating Iceberg tables, managing Git-for-data branches, and orchestrating Write-Audit-Publish workflows without Spark or complex clusters.

Discussion on the vision for AGI and technical challenges including simulation models and balancing scientific rigor with commercialization in AI advancement.

Explores challenges and strategies for transitioning from AI excitement to tangible value creation, covering technical and data engineering aspects.

Technical discussion on building vector search systems with Chroma, covering storage architectures, local-to-cloud APIs, hybrid search, and the importance of RAG for data engineers.

Examines the impact of AI on SEO, discussing how data engineers can adapt data strategies to optimize search engines in response to technological advancements.

Guide for data engineers on LLM security, covering prompt injection, supply chains, and excessive agency risks in AI systems.

Discussion on building AI-powered data products for differentiation, with technical insights on data engineering architectures and tools.

Explores bootstrapping a data consultancy, covering pricing models, sales challenges for engineers, and the current state of AI in the tech industry, with insights on leadership and industry dynamics.

Podcast discussing AI's transformation of retail, finance, and manufacturing by 2026, featuring Snowflake experts on data and analytics impacts.