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

Allows multiple attempts in case of failure, so at least one attempt is successful.

A message is delivered zero or one times only.

Which option is at-most-once message delivery semantic?

Which option is at-most-once message delivery semantic?

Common use cases are chat applications, ML models

Which description is *not* correct for at-least-once message delivery semantic?

Which description is not correct for at-least-once message delivery semantic?

Does WebSocket use a push or pull mechanism?

Does WebSocket use a push or pull mechanism?

The consumer wants to have real-time data.

The consumer wants to have a customized flow control.

The source system wants to be more secure.

The consumer wants to ensure data integrity.

In which scenario will you use the pull mechanism?

In which scenario will you use the pull mechanism?

**(True or False)** Log-based CDC method requires maintaining an extra table.

(True or False) Log-based CDC method requires maintaining an extra table.

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 Ingestion