Data Engineering Design Patterns
 

Go from building portfolio projects to designing production-ready data products
Code first lessons, exercises, and live office hours, so you can immediately start applying data design principles
How to go from knowing just SQL to building pipelines from scratch?
In production, you need to handle complex queries, perform performance tuning, and make ambiguous design decisions. No amount of YouTube videos will teach you how to think about their tradeoffs.
Anyone can build a pipeline that works, but getting it to produce the right dataset and ensuring it's maintainable requires knowing exactly how to build one for your use case.
You know you can put in the work to land a well-paying data job. But you don't have a step-by-step guide to get you there.
Confidently build data products that delight stakeholders
Solve business problems with technology. Enable data-driven decision making for any org.
Demonstrate expertise by focusing on business outcomes.
Guide stakeholders from "hey, can I get revenue data?" to data products that are intuitive for decision-making.
Make your stakeholders' lives easy, and you will go far in your career.
Apply the right data design principles for your use case
Learn the key data engineering design patterns & how they fit together.
Data Warehousing: Build tables analysts actually want to use
Pipeline Design: Handle late events, backfills, and failures gracefully
Data Flow(Medallion): Standardize how data flows through your system
Data Quality: Ensure that the data you present to your stakeholders is correct
Scheduling and Orchestration patterns: Create pipelines that produce output data on time
Data Storage Patterns: Choose the right storage strategy to make analytics fast and cost-effective
Distributed Data Processing Patterns: Scale your pipeline confidently as data volume grows
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