I just realized: if Airflow and other orchestration engines emit open-lineage data to S3 Files and enable Claude to search them, you've got a data catalog.

So, are we debating the semantic layer again? docs.getdbt.com/blog... What do you call a semantic layer from your perspective?

Whether you like or dislike Apache Kafka, its KIPs are among the best learning materials for distributed systems. KIP-848 is an excellent read cwiki.apache.org/con...

Most data platform failures don’t start with bad infra. They start at the team boundary. My new post argues platforms scale through operating interfaces: contracts, ownership, communication, and adoption design, not tooling alone.

More ETL pipelines will run next year than ever before. And ETL is still dead. Not dead, like nobody uses it. Dead like landlines — they work, but nobody builds their strategy around one.

The data engineer job title is due for an update. Not because AI is replacing the role, but because AI is finally revealing what the role was always actually about. Moving data was never the point. Meaning it is. Read more:

At the end of 2026, we will talk about "AI Fan Effect" [en.wikipedia.org/wik...] and the invention of a new field: Psychology for AI. Perhaps, I feel this is the future of software engineering.

As we move from dashboards to autonomous agents, something breaks. Systems of record capture what happened, not why. Why data platforms need Truth Registries + Context Graphs for the agentic era 👇 www.dataengineeringw... #DataEngineering #AgenticAI #Graphs #LLMs

Data Engineering Weekly's 254th edition is out. Context Graph is the new talk of the town!!

The companies that build the most boring data stack often win the market!!! Prove me wrong.

Data Contract: There was no shortage of activity around the topic. Definitions were proposed and refined. Conceptual boundaries were drawn and redrawn. I pen down a reflection of the Data Contracts here www.dataengineeringweekly.com/p/data-contr...

How to build a scalable shopping agent? Here's a wild thought: What if—and hear me out—we let humans click that Buy Now button? Just throwing ideas out there.

This week, it is mostly about Multi-Agent Architecture. Do you think the data infrastructure is ready for a multi-agent architecture? Where is the gap?

Is semantic Spec Good enough to run an enterprise system? I listed challenges to adopting the Iceberg Rest Catalog

Continuing our yearly tradition of Year in Review Data Engineering Weekly, we published the 2025 Year in Review. What do you think is the most notable trend of 2025?

Look at the tech stack IBM now controls: 🐧 Compute: Red Hat (Linux/OpenShift) ☁️ IaC: HashiCorp (Terraform) 💰 FinOps: Kubecost 🌊 Streaming: Confluent (Kafka) 🧠 Vector/AI: DataStax (Cassandra) ⚡ Query Engine: Ahana (Presto) 🔄 Ingest: StreamSets

LinkedIn moves FishDB to Rust, DoorDash builds AI swarms, and Dropbox masters context engineering. 🤯 Data Engineering Weekly #247 is packed with system design deep dives from the best engineering teams.

If the Data Catalog is the answer for AI, the question was wrong.

We stopped asking if data was useful because storage got cheap. Now, "Dark Data" is actively poisoning your AI context windows with hallucination vectors. Read about the Data Sustainability index

The open source companies built their success on top of open-source platforms, benefited from community contributions and adoption, but now must abandon open-source principles to survive commercially.

🚀 The 244th edition of Data Engineering Weekly dives into: AI agents as execution engines, LLM inference economics, databases for AI, personalization, and product evidence. Read more 👉 www.dataengineeringw... #DataEngineering #AI #LLMs

Cricket has been India’s greatest force in overcoming centuries of colonial suppression. Today’s Women’s World Cup win echoes the spirit of 1983 — a triumph that will inspire generations to come. 🇮🇳🏆

This is the most personal essay that I have written in Data Engineering Weekly. I shared a few key moments in my life and how fortunate I was to meet mentors along my professional journey, which shaped my career.

🚀 Data Vault vs. Dimensional Modeling vs. Medallion Architecture — When viewed through a modern enterprise data lens, these techniques interlock. I break down how in Part 2 of my “Revisiting the Medallion Architecture” series.

Fivetran and dbt form a strong foundation for modern data infrastructure, known for bringing simplicity to complex engineering workflows. That said, calling it “open” data infrastructure feels like a stretch.

Should we update the definition of an "Analytical Engineer"?

As a data engineer, you can't treat zero-party (consent) and third-party (inferred) data the same way. This distinction is critical for building systems that are scalable, private, and trustworthy. Here’s my guide:

Airbnb: Real-Time Key-Value Store Airbnb’s next-gen key-value store supports real-time ingestion and bulk uploads with sub-second latency, powering feature stores and fraud detection. Read the full story here: www.dataengineeringw...

Grab: Partner Gateway Metrics at Sub-Second Speed Real-time partner analytics at scale is tough. Grab uses Apache Pinot, Kafka–Flink ingestion, partitioning, and Star-tree indexing to cut query latency to <300 ms, enabling efficient API monitoring and fast issue resolution.

Netflix Muse: Scaling Analytics at Trillion-Row Scale Netflix evolved its Muse architecture to handle huge datasets efficiently: HyperLogLog sketches, Hollow in-memory feeds, and Druid optimizations cut query latency by ~50% and reduced concurrency load.

⚡ Latency Every Data Streaming Engineer Should Know “Real-time” has limits—disk, network, and replication delays add up. StreamNative explains latency tiers, common costs, and tuning levers like batching & async processing. 💡 Must-read for data streaming engineers!

I enjoyed this post by @ananthdurai.bsky.social. Does a great job tying a bunch of recent papers and concepts together.

MCP (Model Context Protocol) promises a new way for LLMs to use tools. Chris Riccomini argues it mostly reinvents OpenAPI, gRPC & CLIs. Resources = docs Tools = RPC Prompts = configs So… could MCP have just been a JSON file? 💡 More insights: www.dataengineeringw...

How Tables Got Smarter: Iceberg → DuckLake. From static snapshots to stream-native updates and catalog-first metadata, tables are evolving fast. Choose by intent, not hype. Subscribe → www.dataengineeringw... Full story → medium.com/fresha-da...

I wrote my thoughts on Supporting Our AI Overlords.

How Tables Grew a Brain: Iceberg → DuckLake Snapshots → incremental → stream-native → catalog-first. Metadata is the bottleneck. More insights → www.dataengineeringw... Full story → medium.com/fresha-da...

BlaBlaCar scales like a pro! dbt Core → Transform like a champ Airflow → Orchestrate effortlessly CI/CD → Deploy instantly Dev Containers → Standardized dev 📖 Full story →medium.com/blablacar... 💡 More insights → Subscribe to DEW #DataEngineering #dbt #Airflow #CICD #DevContainers

🚀 AI adoption is booming—but most data isn’t ready! AI-ready data is: Unified Real-time Human-verified Governed Without it, AI can confidently fail. With it? Reliable, scalable results. 📖 Read More 💡 More insights → Data Engineering Weekly #AI #AIReady #DataEngineering

Stripe’s Real-Time Billing Analytics ⚡ Content: Stripe wanted real-time visibility into subscriptions. Traditional batch systems weren’t fast enough. ⏱️ They built a pipeline using Flink, Spark, and Pinot v2. Now, analytics arrive in minutes, not hours. Queries return in <300ms. 🚀

The 238th edition of Data Engineering Weekly is available, featuring exciting Data & AI articles. Read more: www.dataengineeringw...

Apache Iceberg is now entering the classic paradox. Reference: www.dataengineeringw... www.warpstream.com/b...

From Firefighting to Proactive DB Reliability 🚀 Databricks engineers used Databricks to revolutionize DB reliability: Query/Schema Scorer in CI pipelines Delta Tables + DLT pipelines Database Usage Scorecard across thousands of DBs Efficiency ✅ Anti-patterns ❌

Parquet paradox: supports pluggable indexing and bloom filters, but you must rewrite entire files to use them. Meanwhile, LanceDB rebuilds indexes independently. Is "self-contained" showing off its age to "composable" data architectures? 🤔

AI Hallucinations = confident answers that are flat-out wrong. Why they happen 👇 🎯 Training rewards sounding right, not being right 🎲 Guessing > “I don’t know” 📉 Missing data → confident fiction The fix? Retrieval grounding + truth-focused training.

For years, I've been a skeptic of the Medallion Architecture naming convention. However, despite my initial reservations about the naming, I have come to appreciate the value of a bounded definition. I've shared my thoughts on Medallion Architecture.

Building a Search Engine at Scale 3 B embeddings. 2 months. From content parsing to vector indexing. Wilson Lin shares how—and why chunking is modeling. 📖 www.dataengineeringw... 💡 Subscribe → www.dataengineeringw...

Netflix is redefining data engineering. With LanceDB + Media ML, the Lakehouse now powers media intelligence, not just metrics. 📖 netflixtechblog.com 💡 Subscribe: dataengineeringweekl...