GCP DE

Section 1: Introduction to GCP and Data Engineering

Objective:Understand the fundamentals of data engineering and GCP cloud services.

Duration:4 hours

Topics:

• Overview of data engineering: Roles, responsibilities, and tools
• Introduction to GCP: Projects, regions, zones, and Identity and Access Management (IAM)
• GCP data engineering ecosystem: Cloud Storage, Dataflow, BigQuery, Dataproc, Databricks
• Setting up a GCP project and using the Cloud SDK/Console
• Best practices for security, billing, and cost management

Hands-On Lab:

• Create a GCP project, configure IAM roles, and enable billing.
• Install and configure the Google Cloud SDK for programmatic access.

Section 2: Google Cloud Storage for Data Lakes

Objective:Learn to use Cloud Storage as the foundation for data lakes and storage.

Duration:6 hours

Topics:

• Cloud Storage fundamentals: Buckets, objects, and storage classes (Standard, Nearline, Coldline, Archive)
• Security: IAM roles, access control lists (ACLs), and encryption
• Data organization: Naming conventions, folder structures, and lifecycle rules
• Event-driven architecture: Pub/Sub notifications for Cloud Storage
• Performance optimization: Requester Pays, transfer services, and caching

Hands-On Lab:

• Create a Cloud Storage bucket, upload sample datasets (e.g., CSV, JSON), and configure lifecycle policies.
• Implement IAM policies to control access and enable encryption.
• Set up Pub/Sub notifications to trigger a Cloud Function on object creation.

Project:

• Design a Cloud Storage-based data lake for a healthcare dataset, partitioned by year/month.

Section 3: Cloud Dataflow for ETL and Stream Processing

Objective:Build scalable ETL and streaming pipelines using Dataflow.

Duration:8 hours

Topics:

• Dataflow overview: Apache Beam and GCP integration
• Batch vs. streaming pipelines: Use cases and architecture
• Writing Dataflow jobs: Python, Apache Beam SDK, and templates
• Integration with Cloud Storage, Pub/Sub, and BigQuery
• Monitoring and debugging: Dataflow UI, Stackdriver, and logging
• Optimizing Dataflow: Autoscaling, shuffling, and cost management

Hands-On Lab:

• Write a batch Dataflow job to transform CSV data from Cloud Storage into Parquet.
• Create a streaming Dataflow pipeline to process real-time data from Pub/Sub.
• Monitor pipeline performance using the Dataflow console and optimize resource usage.

Project:

• Build an ETL pipeline to clean and aggregate a retail sales dataset, storing results in BigQuery.

Section 4: Google BigQuery for Data Warehousing and Analytics

Objective:Query and manage data warehouses with BigQuery’s serverless engine.

Duration:8 hours

Topics:

• BigQuery fundamentals: Datasets, tables, and partitioning/clustering
• Data ingestion: Loading from Cloud Storage, streaming inserts, and federated queries
• SQL in BigQuery: Standard SQL, window functions, and geospatial analysis
• Performance optimization: Partitioning, clustering, and materialized views
• BigQuery integrations: Data Studio, Looker, and external tables
• Cost management: Query pricing, storage costs, and reservations

Hands-On Lab:

• Load a dataset from Cloud Storage into BigQuery and create partitioned tables.
• Write SQL queries to analyze sales data, using clustering for performance.
• Visualize query results in Data Studio with a simple dashboard.

Project:

• Build a BigQuery data warehouse for a customer analytics dataset, generating insights like top regions by revenue.

Section 5: Cloud Dataproc for Big Data Processing

Objective:Process large-scale data with Dataproc’s managed Hadoop and Spark clusters.

Duration:6 hours

Topics:

• Dataproc overview: Managed Hadoop, Spark, and Hive
• Cluster creation: Configuration, scaling, and preemptible VMs
• Running Spark jobs: PySpark, Scala, and job submission
• Integration with Cloud Storage and BigQuery
• Workflow orchestration: Dataproc Workflows and Cloud Composer
• Cost and performance optimization: Ephemeral clusters and job tuning

Hands-On Lab:

• Create a Dataproc cluster and submit a PySpark job to process a dataset from Cloud Storage.
• Use BigQuery Connector to write Spark job output to BigQuery.
• Configure a workflow template to automate a recurring Spark job.

Project:

• Process a large log dataset using Dataproc, transforming it into a summarized format for BigQuery.

Section 6: Databricks on GCP for Advanced Analytics

Objective:Leverage Databricks for big data processing and analytics on GCP.

Duration:8 hours

Topics:

• Databricks on GCP: Setup, integration with Cloud Storage and BigQuery
• PySpark and Delta Lake: DataFrames, transformations, and ACID transactions
• Collaborative notebooks: Data exploration and visualization
• MLflow integration: Tracking experiments and models
• Performance optimization: Adaptive Query Execution, caching, and Delta optimizations
• Security: IAM integration and workspace management

Hands-On Lab:

• Set up a Databricks workspace and connect to Cloud Storage.
• Create a Delta Lake table and perform upserts on a sample dataset.
• Build a notebook to analyze and visualize data using PySpark and Databricks plots.

Project:

• Develop a Databricks pipeline to process streaming data from Cloud Storage, storing results in Delta Lake and querying with BigQuery.

Section 7: End-to-End Data Pipeline and Architecture

Objective:Integrate GCP services to build a scalable data pipeline.

Duration:6 hours

Topics:

• Data lake architecture: Raw, processed, and curated zones
• Orchestrating pipelines: Cloud Composer, Cloud Functions, and Workflows
• Real-time vs. batch processing: Design patterns and tools
• Monitoring and logging: Cloud Monitoring, Logging, and alerts
• Cost optimization: Preemptible VMs, flat-rate pricing, and storage tiers
• Best practices for production-ready pipelines

Hands-On Lab:

• Build a pipeline using Dataflow to ingest data, Databricks to transform it, and BigQuery to store curated data.
• Orchestrate the pipeline with Cloud Composer and monitor using Cloud Monitoring.
• Set up alerts for pipeline failures and optimize costs.

Project:

• Architect an end-to-end pipeline for a logistics dataset, from raw Cloud Storage data to a BigQuery dashboard.

Section 8: Capstone Project and Career Preparation

Objective:Apply skills to a real-world project and prepare for data engineering roles.

Duration:6 hours

Topics:

• Review of key concepts and tools
• Building a portfolio-worthy project
• Career preparation: Resume building, LinkedIn optimization, and interview tips
• Common interview questions: PySpark, SQL, and AWS architecture
• Certification paths: AWS Certified Data Analytics, Databricks Data Engineer Associate

Capstone Project:

• Design and implement a complete data pipeline for a fictional e-commerce company:
  • Ingest raw data into S3.
  • Use Glue to catalog and transform data.
  • Store curated data in Redshift and Delta Lake.
  • Query with BigQuery and visualize insights in Data Studio.
  • Document the architecture and present findings.

Career Prep:

• Mock interview with PySpark coding and GCP architecture questions.
• Create a GitHub repository with project code and documentation.

Additional Resources

• Tools Covered: Google Cloud Storage, Cloud Dataflow, BigQuery, Cloud Dataproc, Databricks, PySpark, Delta Lake, Cloud SDK, Cloud Monitoring
• Sample Datasets: Retail sales, customer data, and streaming logs
• Supplementary Materials: Cheat sheets for PySpark, SQL, and GCP services; access to recorded sessions
• Community Support: Slack/Discord channel for peer collaboration and instructor Q&A

Assessment and Certification

• Quizzes: Weekly quizzes to reinforce concepts
• Labs and Projects: Graded hands-on assignments
• Capstone Evaluation: Assessed on pipeline functionality, optimization, and presentation
• Certificate: Awarded upon successful completion of the course and capstone project