You've mastered individual ingestion methods—files, APIs, streaming, and replication. Now let's combine them into a cohesive architecture! Real systems rarely use just one approach.
Instead, you'll architect hybrid solutions that match each data source to its optimal ingestion method.
Engagement Message
What is one data source that might need a different ingestion approach than your main business database?
Requirements drive design, not the other way around. Business needs like "fraud alerts within 30 seconds" or "daily compliance reports by 8 AM" determine your architecture choices.
You'll map each requirement to latency, volume, and reliability specifications before choosing technologies.
Engagement Message
What's one business requirement that would demand real-time processing over batch processing?
Let's architect ingestion for a multi-source e-commerce platform. You need data from: payment processor APIs, customer service databases, web analytics files, inventory management systems, and marketing platforms.
Each source has different characteristics, update frequencies, and business criticality.
Engagement Message
Which of these sources would you prioritize for real-time processing?
SLA (Service Level Agreement) requirements define your success metrics. "99.9% uptime" means less than 9 hours of downtime per year. "Data available within 1 hour" means your batch processing has tight deadlines.
SLAs directly impact your architecture complexity and operational costs.
Engagement Message
Assuming the batch job takes 2 hours to run, what's the latest it can start to still deliver the reports by 8 AM?
Source-method matching optimizes each data flow. Payment transactions need streaming for fraud detection, while marketing files work fine with daily batch transfers.
