Choosing Storage Across Workloads

Concepts covered: paStorageSelection, paLakehouse, paMultiLayerStorage

A real production system rarely has one workload. The example here is a financial services platform with three concurrent demands on the same logical data: a regulatory archive that must retain seven years of transactions, a customer-facing app that needs single-row lookups under 50 milliseconds, and an analytical BI workload that runs daily aggregations across the entire history. No single storage layer is correct for all three. The right answer is a multi-layer architecture in which each workload reads from the storage shape that matches it, and pipelines move data between the shapes as needed. The Three Workloads The Storage Layer Per Workload How the Pipeline Glues Them Together Each workload reads from the layer that matches its access pattern. The customer-facing app reads from Dynam

About This Interactive Section

This section is part of the Storage Layers and Table Formats: Advanced lesson on DataDriven, a free data engineering interview prep platform. Each section includes explanations, worked examples, and hands-on code challenges that execute in real time. SQL queries run against a live PostgreSQL database. Python runs in a sandboxed Docker container. Data modeling problems validate against interactive schema canvases. All content is framed around what data engineering interviewers actually test at companies like Meta, Google, Amazon, Netflix, Stripe, and Databricks.

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DataDriven combines four interview rounds (SQL, Python, Data Modeling, Pipeline Architecture) with adaptive difficulty and spaced repetition. Easy problems get harder as you improve. Weak concepts resurface until you master them. Your readiness score tracks progress across every topic interviewers test. Every lesson section ends with problems you solve by writing and running real code, not by picking multiple-choice answers.