Design end-to-end AWS data pipelines — from Kinesis ingestion to QuickSight dashboards — and earn the DAS-C01 specialty certification.
Curriculum covers
10 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 streaming analytics pipeline that ingests clickstream data via Kinesis Data Streams, enriches it with Managed Service for Apache Flink, stores results in S3 (Parquet) via Kinesis Firehose, and serves aggregated metrics to a QuickSight dashboard updated every 5 minutes.
Design a multi-source data lake on S3 using AWS Lake Formation for access control. Ingest from RDS via DMS, raw logs via Kinesis Firehose, and third-party data via AWS Data Exchange. Run Glue Crawlers to build the Glue Data Catalog, then query with Athena using workgroup cost controls.
Build a production-grade Redshift cluster with optimised table design (distribution keys, sort keys), load data from S3 using COPY commands, federate queries to S3 with Redshift Spectrum, and connect QuickSight for executive reporting. Implement row-level security and column-level encryption.
Plus 16 guided mini-projects across the curriculum — Q&A bots, resume screeners, SQL helpers, and more.
⚡ Seats filling fast — only 4 remaining across all batches
| Batch Type | Schedule | Duration | Next Start Date | Availability |
|---|---|---|---|---|
| Weekend | Sat & Sun, 10am–1pm | 10 weeks | 5 Sept 2026 | Available |
| Weekday | Mon–Fri, 7–9pm | 8 weeks | 7 Sept 2026 | Available |
| Fast-track | Mon–Sun, 6 hours/day | 2 weeks | 14 Sept 2026 | Only 4 seats left |
AWS recommends 5+ years of data analytics experience and 2+ years of AWS experience. At minimum, you should be comfortable with S3, IAM, SQL, and basic Python or PySpark before starting.
Kinesis (Streams, Firehose, Analytics), Redshift, Glue, Athena, Lake Formation, EMR, and QuickSight form the core of the exam. Security and governance across all these services is also heavily tested.
Yes. There is a hands-on lab where you submit a PySpark job to an EMR cluster running on EC2, process S3 data, and write results back — covering the full EMR workflow.
Candidates with strong data engineering experience typically need 8–10 weeks. The 60-hour course, combined with hands-on labs and two practice exams, gives you comprehensive coverage.
Absolutely. Redshift gets an entire module covering architecture, node types, distribution styles, sort keys, Spectrum, performance tuning, DDL/DML/DCL, security, and scaling strategies.
Data Engineer, Data Architect, Analytics Engineer, BI Developer, and ML Engineer roles all benefit significantly from DAS-C01, especially in organisations heavily invested in the AWS data ecosystem.
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