Welcome to Data Consumption & Operations! In this course, you'll learn to build production data pipelines that serve real users reliably.
But first, let's understand something crucial: not all data consumers are the same. Each has unique needs and expectations.
Have you ever wondered why some data requests need instant responses while others can wait hours?
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Think of two examples: one needing instant data, one tolerating delay. What makes their requirements different?
Data consumption patterns are the different ways users and systems access and use your data. Think of them as distinct "appetites" for information.
A dashboard user wants fresh data every morning. A machine learning model needs consistent feature formats. An API caller expects millisecond responses.
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Can you see how each has different requirements?
Understanding these patterns is critical because they drive every design decision in your pipeline. Response time, data freshness, format, and reliability all depend on who's consuming your data.
Get this wrong, and you'll build expensive systems that don't serve anyone well.
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What consumption pattern challenges have you encountered before?
Let's explore three major consumption patterns. Business Intelligence users need aggregated, clean data for reports and dashboards. They typically can tolerate some delay but need high accuracy.
Machine Learning systems require consistent feature schemas and often need historical data for training.
How do you think API consumers differ from these two?
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What's one key way API consumers differ from BI or ML users?
API consumers need fast, predictable responses with minimal latency. They're often serving live applications where users are waiting.
Each pattern has different Service Level Agreements (SLAs). A dashboard might need daily updates, while an API might need 99.9% uptime.
