Zero-Downtime Patching in Lakebase Part 1: Prewarming
DatabricksLakehouseAI
Ensuring customer databases are always available is one of the most important things we do in Lakebase...
Data Engineering, AI Strategy, And Market SignalsFrom Market Pressure To
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Airbnb Rebuilt Alert Development After Discovering It Wasn’t a Culture Problem
AIData PlatformModern Data Stack
Airbnb has revealed how it significantly improved its observability practices by rethinking how alerts are developed and validated, concluding that what appeared to be a "culture problem" was actually a tooling and wo...
VCs are betting billions on AI’s next wave, so why is OpenAI killing Sora?
AIData PlatformModern Data Stack
When an 82-year-old Kentucky woman was offered $26 million from an AI company that wanted to build a data center on her land, she said no. Sure, that same company can try to rezone 2,000 acres nearby anyway, but as AI...
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...
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Agentic Data Pipeline with Claude MCP and Data Quality
Build an agentic data pipeline with Claude MCP to automate schema recovery, eliminating on-call triage and improving...
Agentic data pipeline with Claude MCP architecture
Deploy an agentic data pipeline with Claude MCP to resolve schema drift autonomously and eliminate manual recovery ta...
Agentic Data Pipeline with Claude MCP for Self-Healing
Implement an agentic data pipeline with Claude MCP to resolve schema drift and data quality issues automatically, low...