By the end of this two-part series, we will dive into what data warehouse architecture is and how to implement one for your organization. Part one will look at architectural layers and common data warehouse components, while part two dives into multi-tiered data warehouse architecture.
Confluent and AWS Lambda can be used for building real-time, scalable, fault-tolerant event-driven architectures, ensuring that your application logic is executed reliably in response to specific business events. Confluent provides a streaming SaaS solution based on Apache Kafka® and built on Kora: The Cloud Native Apache Kafka Engine, allowing you to focus on building event-driven applications without operating the underlying infrastructure.
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One of my favorite analogies is that data is the lifeblood of the business. Before you roll your eyes at me (I see it now), hear me out. At your annual physical, when you get your blood work done, think of how much information is uncovered about your overall health from a tiny vial of your blood. From those 10 CCs they extract comes back pages of information regarding your cell counts, glucose, cholesterol, and other information.
Apache Kafka® supports incredibly high throughput. It’s been known for feats like supporting 20 million orders per hour to get COVID tests out to US citizens during the pandemic. Kafka's approach to partitioning topics helps achieve this level of scalability. Topic partitions are the main "unit of parallelism" in Kafka. What’s a unit of parallelism? It’s like having multiple cashiers in the same store instead of one.
