Data Engineering | John Nessime

Data Lake vs Data Warehouse for CRM Analytics: Volume Is the Wrong Question

Post category:Cloud Computing DevOps Technical Guides

Everyone argues this one on data volume, and volume is the argument that matters least: CRM data is small enough that both architectures handle it comfortably. What actually decides data lake vs data warehouse for CRM analytics is how much point-in-time history you need, how fast the schema churns, what shape your queries are, and who is going to maintain the thing. Includes a decision procedure you can run in an afternoon.

AWS Glue Data Quality for SaaS Data: Catching the Breakage Nobody Deployed

Post category:Cloud Computing DevOps Technical Guides

A SaaS admin changes a field and your pipeline stays green while the numbers drift. A practical guide to AWS Glue Data Quality for SaaS sources: where to run the checks, why nested payloads need flattening before DQDL can see them, which rule catches which failure, and the dynamic rules that pass silently because they have no history yet.

Apache Airflow on AWS: Building SaaS and API Pipelines That Don’t Lie to You

Post category:Cloud Computing DevOps Technical Guides

Most API pipeline failures are green DAGs producing incomplete data. A practical guide to running Apache Airflow on AWS for SaaS and API extraction: choosing between MWAA provisioned, MWAA Serverless and self-managed, the pool setting that silently stops throttling when you go deferrable, retry and pagination design, secrets handling, and the four cost lines that actually move.

Shopify Sales Dashboard with AWS: Build One That Actually Reconciles

Post category:Cloud Computing Technical Guides

Most Shopify dashboards built on AWS work perfectly for about three weeks, then quietly drift away from the numbers in the Shopify admin. Here is why that happens, and how to design the ingestion, storage and query layers so your totals still reconcile six months in.

Building a Jira Analytics Pipeline with AWS Lambda and Athena (Without Double-Counting Everything)

Post category:Cloud Computing DevOps Technical Guides

Jira's built-in reports stop at the board boundary. This guide walks through a Jira analytics pipeline built on AWS Lambda, S3 and Athena, organised around the four failure families that actually bite: the removed search endpoint, silently truncated changelogs, incremental loads that duplicate rows, and an S3 layout that quietly inflates your query bill.

Redshift Cost Optimization for SaaS Analytics: The Levers That Actually Move the Bill

Post category:Cloud Computing DevOps Technical Guides

In a SaaS analytics product, the Redshift bill tracks how often queries arrive, not how much data they touch. Here is how the meter actually works, why connection pools bill you while nobody is using the product, how to attribute spend to a tenant, and which isolation choices quietly cost more than they save.

Building an AI Construction Claims Platform on AWS That Holds Up Under Scrutiny

Post category:Case Studies Cloud Computing Technical Guides

Semantic search finds the most persuasive document, not the earliest one. Here is how to architect an AI construction claims and dispute intelligence platform on AWS so retrieval respects the contractual clock, schedule data stays out of the vector index, every answer resolves to a page, and privileged material never shares a retrieval path with project records.

Run It Twice, Get Two Answers: Building an ETL Pipeline From Zoho CRM to Amazon S3

Post category:Cloud Computing DevOps Technical Guides

You re-run the same extract for the same window and get a different set of rows. Nothing errored. You were paginating a result set that kept changing while you read it. Here's how to build a Zoho CRM to S3 pipeline whose runs are repeatable, from closed read windows to Bulk Read and deletions.