Field notes from our engagements: how we build data platforms, content platforms and the AI foundations beneath them — and what actually holds up in production.
Being AI-ready is not about connecting an app to a model. It is about the data, APIs, semantics, provenance and authorization underneath — and where we are investing next.
Read the article →A one-word typo took eleven days to fix. That is an architecture problem, not a people problem — and headless is how you hand publishing back to the team that owns the message.
Read the article →Pull terabyte-scale vendor files straight into S3 without a Spark cluster — directory listing, connector transfers, the exact EventBridge event contract, and Step Functions hand-off.
Read the article →Where custom logic actually runs — DynamicFrame transforms, Glue Studio nodes, Python UDFs, pandas UDFs and applyInPandas — with the performance trade-offs and a decision tree.
Read the article →A field guide to serverless Spark at terabyte scale: storage layout, job bookmarks, shuffle tuning, skew handling, worker sizing and the cost levers that actually matter.
Read the article →They are not competitors — one orchestrates, one computes. A dimension-by-dimension comparison of Glue Workflows, Airflow and Step Functions, with a decision framework.
Read the article →Start with a free data platform assessment — we'll map your pipelines, surface quick wins, and give you a 90-day engineering roadmap.
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