Build production-grade Snowflake pipelines with Snowpipe, Iceberg tables, Streams, and Tasks to pass the SnowPro Advanced Data Engineer exam.
Curriculum covers
24 modules · 60 hours · 24 live sessions
After this course, you will
Hands-on learning
3 industry capstone projects · built from scratch · yours to keep
Build a production-ready CDC pipeline: configure an external AWS S3 stage, use Snowpipe for continuous micro-batch ingestion, apply STANDARD Streams to capture row-level changes, and orchestrate nightly MERGE operations via a monitored task tree with dependency scheduling and error handling.
Ingest raw JSON and Parquet files from an S3 external stage into VARIANT columns, apply LATERAL FLATTEN to normalise deeply nested JSON arrays, and build a multi-step transformation pipeline that stores clean, structured results in downstream Snowflake tables.
Create managed Iceberg tables on AWS with a Snowflake external volume and catalog integration, build a declarative refresh pipeline using Dynamic Tables, provision a zero-copy clone as a testing environment, and demonstrate time travel recovery from an accidental data deletion.
Plus 16 guided mini-projects across the curriculum — Q&A bots, resume screeners, SQL helpers, and more.
⚡ Seats filling fast — only 5 remaining across all batches
| Batch Type | Schedule | Duration | Next Start Date | Availability |
|---|---|---|---|---|
| Weekend | Sat & Sun, 10am–1pm | 10 weeks | 29 Aug 2026 | Available |
| Weekday | Mon–Fri, 7–9pm | 8 weeks | 31 Aug 2026 | Available |
| Fast-track | Mon–Sun, 6 hours/day | 2 weeks | 7 Sept 2026 | Only 5 seats left |
Snowpipe loads micro-batches of files from external stages with sub-minute latency triggered by cloud storage events or REST API calls. Snowpipe Streaming uses a low-latency SDK that ingests data row by row at second-level latency for truly real-time use cases without file staging.
Streams capture a log of row-level changes — inserts, updates, and deletes — on a source table. Tasks then read that stream on a schedule and execute MERGE statements to apply those changes to target tables, enabling incremental and fully automated change data capture.
Dynamic Tables let you define the target query and Snowflake automatically refreshes the result incrementally — no manual task scheduling required. Use them for simpler pipeline logic. Use Streams and Tasks when you need fine-grained control over exactly how and when CDC changes are applied.
Yes. The curriculum covers all exam domains: data ingestion and pipeline construction, data transformation, performance optimisation, security and access control, and troubleshooting — each supported by dedicated hands-on lab exercises.
Zero-copy cloning creates instant, storage-efficient snapshots of tables, schemas, or full databases. In pipelines it is used to provision dev and test environments from production data, checkpoint state before risky transformations, and enable rapid rollback without duplicating data.
Yes. The course covers creating stored procedures in JavaScript and Snowflake Scripting, writing JavaScript and Python UDFs, and integrating them as reusable transformation components inside task trees and data pipeline workflows.
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