5-Hour Data Engineering Course: Complete Interview Preparation

Welcome. Over the next 5 hours, we're going to cover data engineering architecture patterns for interviews, all taught through real world production scenarios. Now, here's exactly what we'll be covering in this course today. In the first section of this course, we'll spend over an hour on data pipeline architecture patterns. So, as part of that, we'll cover eight core

patterns, including EDL versus ELT, injection patterns, incremental loading, item potency, and backwell strategies. Alongside that, we'll also cover real world scenario designing a pipeline for 10 million daily user events. This section builds a foundation that every other section in this course depends on. Next, in the second section of the course, we'll spend over an hour on

batch processing patterns. So, as part of that, we'll cover nine processing patterns including map produce, Apache Spark architecture, partitioning, the shuffle problem, and cost optimization. Alongside that, we'll also cover three real world scenarios including daily revenue aggregation from 500 GB of transactions. In the third section of this course, we'll spend an hour on stream processing

patterns. So, as part of that, we'll cover nine streaming patterns including Apache Kafka architecture, consumer groups, delivery guarantees, windowing patterns, and late arriving data with watermarks. In addition to that, we'll also cover two real world scenarios including a realtime fraud detection pipeline. In the fourth section of this course, we'll spend 15 minutes on the batch

versus streaming decision framework. So this is the single most important section of this course because interviews, you know, they test whether you can look at a problem and choose the right architecture. We'll cover lambda, cap, kapa, microbatch and hybrid architectures with a real world scenario. choosing the right architecture for a given set of