Case Study: Design a Batch Analytics Platform
A full data-engineering interview answer for the pipeline behind a company's BI and reporting layer: source-to-mart volume math, CDC vs batch extract, warehouse/lakehouse and file-format choice, dbt staging/intermediate/marts layering, Airflow DAG and dependency design, SLAs, backfill strategy, cost control, data-quality gates, and team ownership
Model interview answer for designing the batch analytics platform behind a mid-size company's BI and reporting layer, from OLTP sources through CDC/ELT ingestion, lakehouse/warehouse landing, dbt transformation layers, and Airflow orchestration to dashboards: concrete row-count and byte-volume math that drives every downstream decision, the CDC-vs-batch-extract call made per source rather than uniformly, warehouse-vs-lakehouse and file-format/partitioning choices, staging/intermediate/marts layering in dbt, an Airflow DAG shaped around data-aware dependencies rather than fixed clock time, an SLA built with deliberate slack instead of run at the theoretical minimum, an idempotent partition-scoped backfill strategy, cost control through partition pruning and materialization rather than bigger warehouses, data-quality gates that quarantine rather than silently drop or block, and a team/ownership model that treats the staging layer as a reviewed public interface.
Practice questions (5)
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The Company Just 10x'd Its Data Volume Overnight — What Breaks First?
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Inventory Needs Sub-Minute Freshness for One Metric — Without Going Full Streaming
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A dbt Model Silently Redefines 'Active Customer' — How Do You Catch It Faster?
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A Source Database Has No CDC Support at All — Redesign the Ingestion for It
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The Airflow Scheduler Goes Down for Two Hours During the Nightly Run
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