#05 Google Cloud Professional Data Engineering (PDE) Guide

Dive into the world of Google Cloud Platform (GCP) with this comprehensive audio overview, designed to give you a solid foundation in data engineering concepts and tools. Whether you're preparing for the Professional Data Engineer certification or just looking to expand your cloud knowledge, this podcast will cover key areas such as:

BigQuery: Explore its features, including native and external tables, federated queries, and how it serves as a foundation for Business Intelligence. Understand the advantages of using external tables for cost savings and faster creation. Real-time Streaming Analytics: Learn about serverless options, change data capture, and replication. AI and ML Integrations: Discover how to leverage Vertex AI, AI Building Blocks, and AutoML to build and deploy machine learning models. Also, understand different machine learning techniques like regression, classification, clustering, and reinforcement learning. Data Storage and Databases: Get an overview of various storage options like Cloud Storage, Bigtable, Firestore, Memorystore, Spanner, and Cloud SQL. Understand the differences between them and when to use each service, including key concepts such as normalization, denormalization, and data migration. Data Ingestion and Processing: Learn about different data ingestion patterns, including Avro, ORC, and JSON. We'll discuss the advantages of Avro for loading data into BigQuery. The podcast also covers Dataflow for stream and batch processing, Pub/Sub for messaging, and Cloud Data Fusion for data integration. Data Transformation and Orchestration: Find out how to clean and prepare data with Dataprep, and orchestrate workflows using Cloud Composer. Model Deployment and Management: Learn how to deploy your machine learning models using AI Platform Prediction, and the differences between online and batch predictions. We also cover hyperparameter tuning, and ways to improve your model’s quality. Key Concepts: Understand concepts like windowing in Dataflow (fixed, sliding, session windows), as well as feature engineering (categorical vs. continuous features). Cost Optimization: Get best practices for controlling BigQuery costs, such as avoiding SELECT *, using partitioned tables, and leveraging caching. Troubleshooting and Performance: Gain insights into causes of slower performance in Bigtable and solutions Additional GCP Services: The overview includes discussions on Stackdriver, Cloud Scheduler, Cloud Spanner, Dataproc, and other important services to complete your GCP understanding. This podcast is your guide to mastering GCP for data engineering, providing an in-depth look at the tools and techniques you need to succeed.