Pipeline Design | John Nessime

Dockerfile Layer Caching: Why Every Build Reinstalls Everything

Post category:DevOps Docker Technical Guides

Your Dockerfile copies the lockfile before the source, and CI still reinstalls every dependency on every commit. That gap between a correctly ordered Dockerfile and a build that behaves as if it isn't comes down to five distinct failure families: ordering traps, a build context that poisons checksums, a cache that never reaches CI, cache mounts that vanish with the runner, and multi-stage export modes. Here is how invalidation actually works and how to prove which one is costing you.

CI/CD Pipeline Bottlenecks: Where Your Build Minutes Actually Go

Post category:Continuous Delivery DevOps Troubleshooting

The build duration graph looks fine and shipping is still slow. Most CI/CD pipeline bottlenecks are waiting, not working: queue time, caches that report a hit and deliver nothing, stage ordering nobody revisited, and required checks stuck at pending. Here is how to find yours.

Turning External Documents Into Structured Data With Amazon Textract

Post category:Cloud Computing Data Engineering Technical Guides

Amazon Textract almost never fails loudly. A working guide to turning external PDFs and scans into structured data: picking the right operation per document family, parsing the block graph, routing on per-field confidence, and catching the limits that silently truncate your records.

Green Pipeline, Stale Data: Tracking Latency, Freshness and Failure Rates

Post category:Data Engineering DevOps Monitoring

Every task green, zero errors, and the dashboard still shows yesterday's numbers. Run status describes your code, not your data. Here is how to instrument freshness, latency and failure rate so your pipeline tells you before an analyst does.

One Home, Six Portals: Property Listing Deduplication Without False Merges

Post category:Data Engineering Real Estate Technology Technical Guides

Two portals, one house, two records. Merging them is easy. Merging the wrong two is invisible and expensive. A practical look at blocking, match scoring, survivorship rules and the time axis most listing pipelines get wrong.

Backfilling Historical API Data into S3 Without Silent Gaps

Post category:Cloud Computing DevOps Technical Guides

A backfill that exits zero can still be missing a week of data, and nothing will tell you. This is a practical guide to the failure families behind silent gaps: pagination drift under a mutating source, retries that duplicate pages, prefix layouts designed for writes instead of reads, the seam where backfill meets live ingest, and the storage class rules that make mistakes expensive. Includes deterministic key derivation, S3 conditional writes, Athena partition projection, and a per-window manifest pattern that turns completeness into something you can query.

Customer Sentiment Analysis From CRM and Support Data: Building a Score You Can Actually Trust

Post category:Cloud Computing Technical Guides

Most customer sentiment analysis pipelines run perfectly and still produce a number nobody should act on. Here are the failure families that cause it, from labels stamped on the wrong message to averages taken over a scale that was never numeric, plus a pipeline shape and a legal boundary worth knowing before you ship.

Grafana Monitoring for AWS Data Pipelines: The Green Dashboard Problem

Post category:Cloud Computing DevOps Technical Guides

A Glue job that stops running emits no metrics, so the dashboard stays green and the alert quietly resolves itself. Here is why Grafana monitoring for AWS data pipelines misses that failure, and the heartbeat, metric math and no-data configuration that closes the gap, along with the CloudWatch query costs and IAM boundaries nobody warns you about.

Where PHI Escapes: Building a Secure Medical Lab Data Pipeline on AWS

Post category:Cloud Computing Cloud Security Compliance Data Engineering

Encryption is the part everyone gets right. The PHI sitting in your logs, dead letter queues and object key names is the part that fails an audit. A boundary-by-boundary guide to building a medical lab data pipeline on AWS that survives review.

Amazon Textract Data Extraction: What Breaks on Real Contracts and Reports

Post category:Cloud Computing DevOps Technical Guides

Textract rarely fails loudly. It returns a plausible result that is quietly incomplete: a truncated result set, a tick box read as an empty string, a clause split across a page break. A practitioner's guide to the failure modes that actually bite when you point Amazon Textract at contracts, technical reports and correspondence, plus how to choose between sync and async, which feature types are worth paying for, and where Textract stops being the right tool.