ACA Big Data case studies
ACA Big Data · Put your knowledge to work in context.
The ACA Big Data certification is an Alibaba Cloud associate-level credential aimed at data engineers, analytics developers, and cloud practitioners who build and operate big data workloads on Alibaba Cloud. The exam is delivered as multiple choice and is organised into five equally weighted domains of 20% each: big data fundamentals and the Alibaba Cloud big data product landscape; data integration and development with DataWorks; data storage and computation with MaxCompute; real-time and streaming data processing; and data analytics, visualisation, and security operations. The ACA Big Data case studies are 5 cases and about 3,181 words.
1Nightly batch SLO breach after upstream volume spike
A retail team runs a nightly MaxCompute pipeline in DataWorks. Raw orders land in an OSS-backed external table; a SQL task joins it to a 2 TB internal dimension table and writes a partitioned result table (daily partition, ~150 GB/day).
500 words, about 3 min2Real-time dashboard shows stale counts during a burst
A logistics company streams GPS pings from ~40,000 vehicles into a managed streaming service. A Flink job windows the pings into 1-minute tumbling windows per vehicle, computes distance traveled, and writes results to a serving table that powers a live map.
661 words, about 3 min3PII leaking into an analytics dataset
A financial services firm ingests customer records into MaxCompute. A data engineer grants a broad analyst role SELECT on a curated table so the analytics team can build dashboards.
665 words, about 3 min4DataWorks scheduling dependency causes silent data gaps
A media company runs a DataWorks workflow: task A extracts from an upstream API, task B cleans and writes to MaxCompute, task C aggregates into a reporting table. A depends on a daily trigger at 02:00.
642 words, about 3 min5Choosing storage and compute for mixed analytical workloads
A startup is building its first data platform on Alibaba Cloud. It has three workloads: (1) a nightly ETL that joins 500 GB of raw logs with reference data and writes a cleaned table; (2) an interactive BI tool where ~20 analysts run ad-hoc queries throughout the day, expecting sub-10s responses on the cleaned table; (3) a small real-time feed of app events that must be queryable within seconds for a live operations screen.
713 words, about 4 min