gcp_auth

application_default_credentials.json from GCP SDK installation

project_id

Gain insights into data engineering foundations, explore data life cycle stages, and delve into creating data pipelines using Python, Kafka, PySpark, Airflow, and dbt.

UDoc.tar.gz

BigQuery

Airflow

Airflow Custom

Dagster

Airflow-copy

Airflow-custom-copy

Data engineering is currently one of the most in-demand fields in data and technology. It intersects software engineering, DataOps, data architecture, data management, and security. Data engineers, such as analysts and data scientists, lay the foundation to serve data for consumers. 

In this course, you will learn the foundation of data engineering, covering different parts of the entire data life cycle: data warehouse, ingestion, transformation, orchestration, etc. You will also gain hands-on experience building data pipelines using different techniques such as Python, Kafka, PySpark, Airflow, dbt, and more. 

By the end of this course, you will have a holistic understanding of data engineering and be able to build your data pipelines to serve data for various consumers.

Data Engineering Foundations in Python

Which task is *not* part of the data engineering life cycle?

Which task is not part of the data engineering life cycle?

**(True or False)** Data warehouses and data lakes can not coexist in data architecture.

(True or False) Data warehouses and data lakes can not coexist in data architecture.

It is generally slow to retrieve data from a data lake.

It is a challenge to manage data in a data lake due to little schema management, data catalog, and discovery tools.

A data lake follows a schema-on-write principle.

Which one describes data lakes accurately?

Which one describes data lakes accurately?

Which storage is the best for reporting?

**(True or False)** Transformation is always after ingestion.

(True or False) Transformation is always after ingestion.

To introduce more automation to the process

Test your knowledge of data engineering life cycle.

Getting Started

Data Team Structure

Data Engineering Life Cycle

Cloud Data Architecture

Data Ingestion

Data Modeling

Data Orchestration

Mastering Airflow: Building an ETL Pipeline

Data Quality

Build an End-to-End Data Pipeline for Formula 1 Analysis

Epilogue

Appendix

Quiz: Data Engineering Life Cycle