SnowPro Advanced Data Engineer case studies
SnowPro Advanced Data Engineer · Put your knowledge to work in context.
The SnowPro Advanced: Data Engineer certification is Snowflake's advanced-level credential for practitioners who build and operate data pipelines on the Snowflake Data Cloud. It sits above the core SnowPro Core certification and assumes you already know the platform's fundamentals - virtual warehouses, stages, roles, and basic SQL - so the exam focuses on the harder engineering decisions: designing idempotent ingestion with Snowpipe and continuous loading, choosing file formats and copy options that actually perform, building transformation layers with streams, tasks, and Dynamic Tables, modeling schemas for both analytical and semi-structured workloads, and tuning warehouses and queries when volume grows. The SnowPro Advanced Data Engineer case studies are 5 cases and about 3,184 words.
1The 55,000-file day: Snowpipe cost and latency under small-file telemetry
An IoT platform drops device telemetry into S3 as JSON: 55,000 objects per day, 12–20 KB compressed each (about 900 MB/day), arriving continuously. A Snowpipe with AUTO INGEST lands them in RAW TELEMETRY in roughly 40 seconds, and the team treats that latency as a hard requirement.
620 words, about 3 min2Dynamic Tables versus streams and tasks on a mutable feed
RAW EVENTS is append-only at 60M rows/day and feeds two Dynamic Tables: DT SILVER with a 10-minute target lag, and DT GOLD, which reads DT SILVER with its own 10-minute lag. A separate team runs a nightly MERGE updating about 2% of the prior 30 days' rows, and occasionally re-inserts the last 7 days wholesale.
655 words, about 3 min3Clustering key choice on a 4 TB tenant-partitioned fact table
EVENTS FACT is 4.2 TB (roughly 150,000 micro-partitions) loaded in near-event-time order, with 2,000 tenant id values. 85% of production queries filter a single tenant id plus a 7-day event date range and should touch well under 1 GB; in practice they scan 40–60 GB.
685 words, about 3 min4One warehouse, two workloads: spilling ELT and a 08:30 dashboard spike
ETL WH is an XS warehouse running a 14-task nightly pipeline that starts at 01:00 and finishes around 01:55. Three of those tasks account for 40 minutes of the runtime and spill 30–60 GB per run; the largest is a window-function join that repartitions an 800M-row staging table.
502 words, about 3 min5PII, regional row access, and a partner share that leaked
ORDERS (800M rows) holds EMAIL, PHONE, and REGION. PII columns carry a PII tag and a tag-based masking policy; REGION is protected by a row access policy that maps CURRENT ROLE() to a region via a mapping table.
722 words, about 4 min