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From Spreadsheets to Insights The Data Mart Journey for Associations
Introduction: Life with Spreadsheets Most associations begin their data journey with spreadsheets. - Membership data lives in one system. - Events data lives i...
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Pipe Dreams – The life and times of Yahoo Pipes (2023)
Article URL: https://retool.com/pipes Comments URL: https://news.ycombinator.com/item?id=46503006 Points: 17 Comments: 1...
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Part 7: Gold Layer – Metrics, Watermarks, and Aggregations
!Cover image for Part 7: Gold Layer – Metrics, Watermarks, and Aggregationshttps://media2.dev.to/dynamic/image/width=1000,height=420,fit=cover,gravity=auto,form...
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Data Engineering Isn’t About Tools — It’s About Thinking Like This
Introduction Data engineering is often misunderstood as a discipline driven mainly by tools. New learners are frequently advised to master Airflow, Spark, Kafk...
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ML Systems: The Part They Skip in the Diagram
The Unspoken Reality of Production ML “This classic meme, in all its simplicity, explains more about ML systems in organizations than most articles you’ll find...
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Schema Validation Passed - So Why Did My Pipeline Fail?
The False Comfort of “Validation Passed” Schema validation does one job really well: it checks if your data file is parseable. json // This passes every schema...
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Why We Built FlowSynx — The Story Behind the FlowSynx
Why We Built FlowSynx — The Story Behind a Declarative and Unified Interoperability Orchestrator for Intelligent Information Flow ==============================...
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Data-Architect-Master-Professional-Workbook
Overview I built a modular, audit‑ready data engineering project and wanted to share it with the community. Features - Clean, production‑style Python - SQL pat...
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Choosing an ETL Tool for Salesforce: The Practical Options
The article was initially published on the Skyvia bloghttps://blog.skyvia.com/best-etl-tools-for-salesforce/. Salesforce is one of the most widely used CRMs out...
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Mastering Data Science Skills for Beginners and Aspiring Analysts
Introduction To succeed in data science, beginners must focus on developing essential technical and analytical skills. The field combines statistics, programmi...
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Why Idempotency Is So Important in Data Engineering
In data engineering, failures are the norm: jobs crash, networks timeout, Airflow retries tasks, Kafka replays messages, and backfills rerun months of data. In...
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REST API Calls for Data Engineers: A Practical Guide with Examples
Introduction As a Data Engineer, you rarely work only with databases. Modern data pipelines frequently ingest data from REST APIs—whether it’s pulling data fro...