🚀 Data Engineering And Strategy Learning Tracks

Back To The Basics

• Data Pipelines

  • Data Sharing in the Real World: Why SFTP Remains Essential for Companies

  • From Basics to Challenges: A Data Engineer's Journey with APIs

  • Understanding the “T” in ETL: A Back-to-Basics Guide to Data Transformations

  • 🔒 Why Your Data Pipeline Probably Isn’t Production-Ready

• SQL Basics

  • Behind the Scenes of SQL: Understanding SQL Query Execution

  • Back To The Basics With SQL: Understanding Hash, Merge, and Nested Joins

  • 🔒 Best Practices for Simplifying Complex SQL

• Data Modeling

  • 🔒 Data Warehousing Essentials: A Precursor

  • The Challenges You Will Face When Data Modeling

  • What Is Query Driven Data Modeling

  • 🔒 Normalization Vs Denormalization - Taking A Step Back

  • 🎥 Data Modeling - Walking Through How To Data Model As A Data Engineer - Dimensional Modeling 101

• Embrace Being A Beginner: The Importance of Building a Strong Technical Foundation

Growing As A Data Engineer

• 🔒 Becoming a Data Engineering Force Multiplier

• 🔒 Becoming A Better Data Engineer - Tips On Translating Business Requirements

• 🔒 What Does It Take To Get Promoted As An Engineer?

• 🔒 Getting Unstuck In Your Data Career .

• The Never Ending Epidemic of Resume Driven Development

• How To Fast Track Your Career As An Engineer

Technology, Tools, Best Practices And Definitions

• 🔒 Mistakes I Have Seen When Data Teams Deploy Airflow

• 🔒 Should You Use Apache Airflow?

• 🔒 Introduction to Presto/Trino for Data Engineers

• 🔒 Operational Data Stores Vs Data Lakehouses And All The Other Data Management Methods

• 🔒 OLTP vs OLAP - Transactions Vs Analytics

• 🔒 Normalization Vs Denormalization - Taking A Step Back

• 🔒 How And Why We Need To Implement Data Quality Now!

• Cataloging Data Catalogs

• What Is Change Data Capture

• Why is Polars All The Rage?

• Behind the Rust Hype: What Every Data Engineer Needs to Know

• Data Engineering Vs Machine Learning Pipelines

Private Webinars

Managing Data Teams - (Videos Included)

Technology is only one pillar of having a successful data team. You also need solid processes and people to ensure that you can use your data successfully. So I put together several articles and interviewed several directors and managers in the data world to understand how to better manage data teams.

• 🔒 Centralized vs Decentralized vs Federated Data Teams

• 🔒 Onboarding For Data Teams 

• 🔒 Elevating Your Data Team: From Service Providers to Strategic Partners 

• 🔒 Tips For Hiring Junior Data Engineers 

• Optimizing Your Data Infrastructure Costs: How To Approach It And Common Issues

• Different Types Of "Data Engineering" Teams 

• Setting Standards For Your Data Team

• 🎥 How Data Analytics Teams Can Deliver What The Business Needs 

• 🎥 The Product Development Life-Cycle For Dashboards - With Abby Kai Liu 

• 🎥 How To Manage Data Teams Successfully - Asking A Director Of Data Architecture And Governance - 

The Data Industry Past Present and Future

• 🔒 A Decade In Data Engineering - What Has Changed?

• 🔒 The Kid Gloves Are Coming Off In The Data World

• 🔒 This Time It's Different - ChatGPT

• 🔒 Let's Move Fast And Get Rid Of Data Engineers

• What I Don't Want To See In The Data World In 5 Years

• How Far Can We Separate Storage And Compute

• The State Of Data Engineering - Part 1

Other

• 🔒 Onboarding For Data Teams

• 🔒 Navigating Your First 90 Days in a Data Job: A Comprehensive Guide

• Realities of Being A Data Engineer - Migrations

• What Is Shadow IT?