Apache Iceberg | 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.

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.

The Row Count That Only Goes Up: Building a Salesforce Data Lake on AWS

Post category:Cloud Computing DevOps Technical Guides

Your dashboard shows four percent more opportunities than Salesforce, and the extra rows were all deleted months ago. Incremental sync on SystemModstamp can never see a record that no longer exists. Here's how to build a Salesforce data lake on AWS that stays equal to the source.