How to Build an End‑to‑End E‑Commerce Data Warehouse for Interview Success

Project Overview

The case study focuses on an e‑commerce data‑warehouse (named OneData ) that captures user browsing, cart, order, and payment behaviors to provide operational insights such as GMV trends, user activity, and conversion rates.

Technical Architecture

Offline pipeline : MySQL → DataX → HDFS → Hive (MapReduce/Spark) → MySQL

Real‑time pipeline (optional) : Flink → Kafka → ClickHouse/Doris

Scheduling : Azkaban / DolphinScheduler

Visualization : Superset / FineBI

Stage 1 – ODS Layer (Raw Data)

Data sources : MySQL tables (user, product, order, payment) and JSON logs (app start, page view, button click).

Log collection : Configure a Flume agent to monitor log directories, ensuring breakpoint‑resume and file‑rolling.

ODS modeling : Create Hive tables partitioned by dt='2025-12-22', store data compressed (Snappy/Gzip), and load logs directly without cleaning.

Stage 2 – DWD Layer (Detail Data)

Data cleaning : Remove nulls, filter test accounts or bot traffic.

Degenerate dimensions : Split wide ODS tables; e.g., join order detail with product dimension to produce a wide fact table, reducing downstream joins.

Slowly changing dimensions (SCD) : Use Hive SQL to build a “link table” that records start_date and end_date for attributes such as user address, preserving historical values.

Stage 3 – DWS Layer (Aggregated Data)

Business processes : Define domains—traffic (page view, session), transaction (order, payment), user (registration, activity).

Summary tables : Daily active users (DAU), product order wide table (order count, buyer count, amount), regional sales summary (sales by province).

Metric definitions : Clarify “new user” (first login) and “active user” (any activity on the day).

Stage 4 – ADS Layer (Reporting)

Core metrics : GMV (total transaction amount, including unpaid orders), Sales (actual paid amount), UV/PV, Funnel conversion (view → add‑to‑cart → order → payment).

Data export : Use Sqoop or DataX to move final Hive results to MySQL for BI tools.

Stage 5 – Real‑Time Warehouse (Optional Bonus)

Data source : Capture MySQL binlog via Canal or simulate logs to Kafka.

Real‑time computation : Flink consumes Kafka, performs ETL cleaning, applies sliding/rolling windows to compute metrics such as real‑time GMV, top‑10 hot products, per‑minute order count.

Result storage : Write outcomes to ClickHouse (real‑time analytics) or Redis (caching for dashboards).

Stage 6 – Scheduling & Monitoring

Write Shell scripts to wrap Hive SQL execution commands.

Configure workflows in Azkaban/DolphinScheduler with task dependencies (ODS → DWD → DWS → ADS) and schedule nightly runs to avoid peak business‑DB load.

Resume Writing Tips (STAR Method)

Project Name : E‑commerce User Behavior & Transaction Analysis Data Warehouse Project Description : Built an offline data‑warehouse on the Hadoop ecosystem covering user behavior and transaction modules to analyze GMV trends, user activity, and product conversion, supporting data‑driven decisions. Responsibilities : Designed ODS‑DWD‑DWS‑ADS layered architecture based on dimensional modeling theory. Implemented Hive SQL to process ~10 million daily logs, solved data skew with hive.groupby.skewindata=true , and reduced job time from 2 h to 40 min. Developed DWS summary tables calculating DAU, repurchase rate, and produced 20+ business reports. Configured daily Azkaban workflows ensuring timely data delivery.

Common Pitfalls & Solutions

Data skew : Enable hive.groupby.skewindata=true, add random prefixes (salting), use map‑join for small tables, or split SQL.

Data quality : Apply non‑null, uniqueness, and volatility checks (e.g., alert if daily volume changes >20%).

SCD implementation : Use start_date and end_date columns to track attribute lifecycles.

By following this end‑to‑end workflow and documenting the impact with concrete metrics, candidates can demonstrate deep technical competence and stand out in data‑engineer interviews.