Complete 15–20 projects around market-relevant coding requirements and Data Engineering scenarios. With mentorship, work through live projects step by step.
The examples below show project themes within the broader learning journey.
Read from files, APIs or databases. Transform and validate the data, then load it into a warehouse.
Work with batch and streaming concepts, schema changes, historical data and governed analytical tables.
Schedule tasks, validate data, handle retries and investigate failed pipeline runs.
In one-to-one mentorship, your mentor guides your implementation, explains difficult concepts and helps you work through issues. You build the project and learn the reasoning behind it.
Explore one-to-one mentorshipDescribe the source, expected output and coding requirements before selecting the tools.
Build the pipeline, investigate failures and understand the decisions you make.
Walk through your architecture, quality checks, results and lessons in a technical interview.
Build 15–20 projects across industry scenarios. The 18 examples below illustrate how the portfolio can be structured; the final project mix and cloud stack are agreed for your learning path.
Combine synthetic appointment, visit and hospital reference data; standardize identifiers and dates, check duplicates and build visit/utilization tables.
Join synthetic claims, provider and policy data; track rejected records, reconciliation totals and processing status.
Deliver: source contract, working pipeline, quality checks, analytical tables and an architecture walkthrough.
Ingest sample account and transaction feeds, remove duplicates and reconcile opening balance, movements and closing balance.
Process simulated payment events, handle late arrivals and produce time-windowed transaction summaries.
Deliver: source contract, working pipeline, quality checks, analytical tables and an architecture walkthrough.
Combine orders, returns, product and stock feeds; build sales facts and product/store dimensions for inventory reporting.
Process browsing and checkout events; connect sessions with orders and create conversion-ready datasets.
Deliver: source contract, working pipeline, quality checks, analytical tables and an architecture walkthrough.
Combine dispatch, scan and delivery events; deduplicate scans and calculate delivery time and delayed-shipment datasets.
Ingest simulated vehicle location and sensor events, validate timestamps and aggregate route and vehicle activity.
Deliver: source contract, working pipeline, quality checks, analytical tables and an architecture walkthrough.
Transform sample sensor readings into machine-level time series, flag missing readings and prepare monitoring tables.
Join production batches, inspection results and downtime logs to build traceable yield and defect summaries.
Deliver: source contract, working pipeline, quality checks, analytical tables and an architecture walkthrough.
Process sample call/usage records and plan data, validate usage totals and create subscriber-level billing summaries.
Group simulated network events by location and time window; handle duplicate events and delayed delivery.
Deliver: source contract, working pipeline, quality checks, analytical tables and an architecture walkthrough.
Model sample policies, customers and premium schedules; preserve policy changes and build renewal reporting tables.
Track synthetic claim submissions and status changes to produce processing-time and outstanding-claim datasets.
Deliver: source contract, working pipeline, quality checks, analytical tables and an architecture walkthrough.
Join sample subscription, payment and usage events to create active-user and renewal datasets.
Transform simulated viewing events into session and content-level engagement tables with late-event handling.
Deliver: source contract, working pipeline, quality checks, analytical tables and an architecture walkthrough.
Combine synthetic course, enrollment and assessment data to build completion and progress datasets.
Process sample lesson events, validate ordering and build daily participation summaries.
Deliver: source contract, working pipeline, quality checks, analytical tables and an architecture walkthrough.
Training projects use public, synthetic or approved anonymized datasets. “Live project” means guided implementation and problem-solving; these examples do not imply access to a client production system.
Our illustrative project briefs draw on published healthcare, retail, financial-services and fleet/IoT architecture patterns. They are training scenarios, not claims of client projects.
We assess your capability and learning intent before offering enrollment.
