Data Integration | 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.

Agentforce and AWS: Where the Trust Layer Stops and Your Logs Begin

Post category:Cloud Computing DevOps Technical Guides

Agentforce and AWS wire together in four standard patterns, and every one of them has a point where Salesforce's guarantees stop and yours start. This traces a single request across each boundary it crosses, covers the Trust Layer default most write-ups get wrong (LLM data masking is disabled for agents), and sets out what changes the moment a callout lands in your own account: retention, audit trail, and user identity that does not travel.

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.

It Counted the Chunks: Preparing CRM Data for Amazon Bedrock

Post category:Cloud Computing DevOps Technical Guides

Asked how many open opportunities an account had, the assistant said five. It had nineteen. Retrieval returned five chunks and the model counted them. CRM data is structured and RAG is built for text, so the preparation work is mostly deciding which questions are retrieval and which are queries.

It Works Isn’t Ready: A Production Checklist for Salesforce AWS Integrations

Post category:Cloud Computing DevOps Technical Guides

"It works" and "it's ready" are different claims. Integrations rarely fail on launch day; they fail in week six, when the token expires and someone adds a field. A readiness checklist for Salesforce AWS integrations, with a pass test on every item.

Cheapest at Two Flows, Worst at Forty: AppFlow vs Lambda vs MuleSoft on Cost

Post category:Cloud Computing DevOps Technical Guides

AppFlow won the spreadsheet at two integrations. Eighteen months and forty flows later it's a line item that gets mentioned in the monthly review. These three options aren't three prices, they're three cost shapes, and the crossover is the whole decision.

Zero Errors, Zero Records: Monitoring Salesforce Integrations with CloudWatch and Grafana

Post category:Cloud Computing DevOps Technical Guides

The error count was zero every day for three weeks. So was the invocation count. A stopped integration and a healthy one produce identical graphs, and every CloudWatch default is tuned to stay quiet when data stops arriving. Four signals worth emitting, and the alarm config that actually fires.