Data Engineering

I think that you should be able to select more than one reason for rating. This team is

The Spreadsheet Problem Every finance team we’ve worked with has the same origin story. Margin analysis started in a single Excel workbook. Someone built a clever set of formulas. It worked for a while. Then the business grew, and that workbook became a monster —

The ETL versus ELT debate has been running for over a decade, and the conventional wisdom has shifted dramatically during that time. Five years ago, the default recommendation was almost always ETL: extract data from sources, transform it in a dedicated processing layer (typically Spark

Procurement teams spend an extraordinary amount of time on research before they can make sourcing decisions. Before issuing an RFP, they need to understand the supplier landscape for the category. Before negotiating a contract renewal, they need current market rates and competitive alternatives. Before approving

PDF is the cockroach of file formats. It was designed in 1993 by Adobe to faithfully reproduce printed documents on screen, and it has survived every attempt to replace it. Every business uses PDFs. Every developer eventually has to process them. And every developer who

Businesses run on documents. Invoices, contracts, purchase orders, spec sheets, compliance filings — the operational backbone of most companies is a sprawling mess of PDFs, Word files, scanned images, and emails with attachments. The information locked inside these documents is critical for operations, compliance, and

Every data engineer has the story. Your phone buzzes at 3:14 AM. The nightly ETL job failed. Upstream changed a column name, or the API started returning 429s, or a single malformed record caused the entire batch to abort. You open your laptop in bed,