A blueprint for democratic governance of frontier AI
AIData PlatformModern Data Stack
OpenAI outlines a blueprint for U.S. governance of frontier AI, proposing a federal framework for safety, resilience, and national security.
Data Engineering, AI Strategy, And Market SignalsFrom Market Pressure To
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Article: Two Misconfigurations That Caused Spark OOM Failures on Kubernetes
AIData PlatformModern Data Stack
After migrating Spark pipelines to Azure Kubernetes Service, two infrastructure settings interacted destructively: spark.kubernetes.local.dirs.tmpfs=true backed shuffle spill with RAM instead of disk, and a hard podAf...
Lakehouse Convergence
Data PlatformLakehousedbt
The data and AI landscape is undergoing a significant shift towards lakehouse architectures, driven by the need for interoperability and AI-ready data transformation. This trend has major implications for data enginee...
Real Problems. Real Pipelines.
Every project below started with a business bottleneck — not a technology wish list. Scroll through to see how the right data architecture turned operational chaos into measurable clarity.
01
The Challenge
“Operational teams needed fresher analytics without buying a heavy black-box ELT layer. The real risk was solving latency by adding a stack nobody could explain, test locally, or evolve safely once...”
Solution: PostgreSQL + Debezium
Real-Time CDC Analytics Pipeline
A runnable CDC stack that captures PostgreSQL WAL changes with Debezium, normalizes events in Python, and publishes analytics-ready bronze, silver, and gold layers with dbt and...
Read Case Study02
The Challenge
“Many teams want lakehouse scale but start with fragile scripts and unclear storage ownership. The hidden cost is coupling storage, compute, and governance so tightly that every new use case feels l...”
Solution: AWS + S3
AWS And Databricks Lakehouse
A lakehouse case that provisions AWS storage with Terraform, lands simulated event data in S3, and processes silver and gold Delta layers in Databricks with PySpark.
Read Case Study03
The Challenge
“Warehouse work often decays into undocumented SQL and manual cloud setup. That slows onboarding, weakens trust in the numbers, and makes every model change feel riskier than it should.”
Solution: GCP + BigQuery
GCP Modern Data Stack
A cloud-native analytics workflow that provisions BigQuery and storage with Terraform, ingests market data with Python, and tests warehouse models with dbt and GitHub Actions.
Read Case StudyWhat Gets Built
Dashboards anyone trusts. Pipelines that don't break on Monday morning. Data that arrives before the meeting starts — not after.
cdc
Real-Time CDC Analytics Pipeline
A runnable CDC stack that captures PostgreSQL WAL changes with Debezium, normalizes events in Python, and publishes a...
Read Case Study
lakehouse
AWS And Databricks Lakehouse
A lakehouse case that provisions AWS storage with Terraform, lands simulated...
Case DetailsOpinionated asset
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What I'm Thinking About
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