Modern Data Engineering Essentials By Christina Taylor

This video equips you with the knowledge and skills needed to engineer data for today's large systems.
Overview
Modern Data Engineering Essentials presents the concepts and issues surrounding today's big data and AI systems. You also gain hands-on experience with data tools and workflows. The video finishes with a discussion of education, experience, and credentials needed to develop your skills in the modern data world.
About the Instructor
Christina Taylor is a seasoned data engineer who is passionate about open-source, multi-cloud, scalable, and efficient data pipelines. She makes data informed architectural decisions to build modern systems that support advanced analytics, machine learning, and customer-facing product use cases. She has a keen interest in interdisciplinary areas such as DevOps, MLOps, and Cloud FinOps. She loves to learn, share, and contribute to the open source community.
Learn How To
Apply data engineering concepts
Load data into DuckDB
Set up and use dbt
Orchestrate data engineering workflows using Airflow, Dagster, and Prefect
Put it all together integrating Dagster and dbt
Maximize your education, experience, and skills to work in the modern data engineering world
Who Should Take This Course
Developers, data scientists, and engineers who are interested in the data side of big data systems
Course Requirements
Previous experience with data and data models
Some knowledge of SQL and Python
Lesson Descriptions
Lesson 1: Modern Data Lakes
Lesson 1 lays the groundwork for the course by discussing the history of big data and the modern data landscape. It explores data warehouses and data lakes in the context of today's AI. By the end of the lesson, you will have more of a big-picture understanding of how data engineering fits into today's modern systems.
Lesson 2: Fundamentals of Database Engineering
In Lesson 2 you dive into a discussion of fundmental concepts in database enginerring: data models and data stores. This sets you up for the hands-on portion of the lesson: loading data into DuckDB, an open source, column-oriented relational database management system.
Lesson 3: Data Transformation
Lesson 3 introduces data transformation. Unfortunately, data in its raw format is typically not read for processing and analysis. It needs to be transformed into something usable, and that is where the extract, transform, and load concepts come in. Christina also discusses SQL and Python in this context. The lesson finishes up with another hands-on demonstration, introducing dbt, a tool for building, testing, and deploying robust data pipelines.
Lesson 4: Workflow Orchestration
Lesson 4 tackles workflow orchestration, the coordinating of multiple tasks across applications. Christina discusses tools such as Airflow, Dagster, and Prefect before turning to a hands-on demo on Dagster assets. She finishes up by helping you put it all together with Dagster and dbt.
Lesson 5: Future of Data Engineering
Christina finishes the video discussing where data engineering is going and helping tie that into where you are going with your data engineering career. She discusses topics like whether or not you need a degree related to computer science and/or data analytics or whether it is more important to dig in and get experience with data. She finishes up by briefly discussing next steps for you.

https://www.informit.com/store/modern-data-engineering-essentials-video-course-9780135563366

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