AWS DE

Section 1: Introduction to AWS and Data Engineering

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

Duration:4 hours

Topics:

• Overview of data engineering: Roles, responsibilities, and tools
• Introduction to AWS: Regions, Availability Zones, and IAM
• AWS data engineering ecosystem: S3, Glue, Athena, Redshift, Databricks
• Setting up an AWS account and CLI/SDK for data engineering
• Best practices for security and cost management

Hands-On Lab:

• Create an AWS account, configure IAM roles, and set up CloudTrail for monitoring.
• Install and configure AWS CLI for programmatic access.

Section 2: Amazon S3 for Data Storage

Objective:Learn to use S3 as the foundation for data lakes and storage.

Duration:6 hours

Topics:

• S3 fundamentals: Buckets, objects, and storage classes (Standard, Glacier, Intelligent-Tiering)
• S3 security: IAM policies, bucket policies, and encryption (SSE, KMS)
• Data organization: Partitioning, prefixing, and lifecycle policies
• S3 event notifications and integration with other AWS services
• Performance optimization: Multipart uploads and transfer acceleration

Hands-On Lab:

• Create an S3 bucket, upload sample datasets (e.g., CSV, JSON), and configure lifecycle rules.
• Implement bucket policies to restrict access and enable server-side encryption.
• Set up S3 event notifications to trigger a Lambda function.

Project:

• Design an S3-based data lake structure for a retail dataset with partitioning by year/month.

Section 3: AWS Glue for ETL and Data Cataloging

Objective:Build ETL pipelines and manage metadata using AWS Glue.

Duration:8 hours

Topics:

• AWS Glue overview: Components (Crawlers, Jobs, Triggers)
• Glue Data Catalog: Metadata management for S3 and other sources
• Writing ETL jobs: PySpark, Python, and Glue Studio
• Glue crawlers: Auto-discovering schema and populating the Data Catalog
• Scheduling and monitoring Glue jobs
• Optimizing Glue jobs: Partitioning, pushdown predicates, and job bookmarks

Hands-On Lab:

• Create a Glue crawler to catalog an S3 dataset (e.g., sales data).
• Write a PySpark Glue job to transform CSV data into Parquet format.
• Schedule a Glue job using triggers and monitor execution in CloudWatch.

Project:

• Build an ETL pipeline to clean and transform a raw customer dataset in S3, storing results in a curated zone.

Section 4: Amazon Athena for Serverless Querying

Objective:Query data lakes using Athena’s serverless SQL engine.

Duration:6 hours

Topics:

• Athena overview: Use cases and architecture
• Querying S3 data with Athena: SQL syntax and best practices
• Integration with Glue Data Catalog for schema-on-read
• Partitioning and compression for cost and performance optimization
• Workgroups, query execution, and cost management
• Visualizing Athena results with QuickSight (optional integration)

Hands-On Lab:

• Query a partitioned S3 dataset using Athena and Glue Data Catalog.
• Optimize query performance by adding partitions and converting data to Parquet.
• Set up a workgroup and monitor query costs in the Athena console.

Project:

• Analyze a sales dataset in S3 using Athena to generate insights (e.g., top-selling products by region).

Section 5: Amazon Redshift for Data Warehousing

Objective:Design and manage data warehouses with Redshift for analytics.

Duration:8 hours

Topics:

• Redshift fundamentals: Clusters, nodes, and distribution styles (KEY, EVEN, ALL)
• Data loading: COPY command, S3 integration, and Redshift Spectrum
• Table design: Sort keys, distribution keys, and compression
• Query optimization: Workload Management (WLM) and query tuning
• Redshift Spectrum: Querying external S3 data without loading
• Backup, restore, and scaling Redshift clusters

Hands-On Lab:

• Create a Redshift cluster and load data from S3 using the COPY command.
• Design a star schema for a sales dataset and apply distribution/sort keys.
• Query external S3 data using Redshift Spectrum and compare performance.

Project:

• Build a data warehouse in Redshift for a retail dataset, creating dashboards with sample SQL queries.

Section 6: Databricks on AWS for Advanced Analytics

Objective:Build ETL pipelines and manage metadata using AWS Glue.

Duration:8 hours

Topics:

• Databricks overview: Architecture and integration with AWS (S3, Redshift)
• Setting up a Databricks workspace on AWS
• PySpark fundamentals: DataFrames, transformations, and actions
• Delta Lake: ACID transactions, versioning, and time travel
• Collaborative notebooks: Sharing and visualizing data
• Performance optimization: Caching, partitioning, and cluster sizing

Hands-On Lab:

• Configure a Databricks cluster and mount an S3 bucket.
• Create a Delta table from an S3 dataset and perform upserts.
• Build a notebook to process and visualize a dataset using PySpark and Databricks visualizations.

Project:

• Develop a Databricks pipeline to process streaming data from S3, storing results in Delta Lake.

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

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

Duration:6 hours

Topics:

• Medallion architecture: Bronze, Silver, Gold layers
• Orchestrating pipelines: AWS Step Functions, Lambda, and EventBridge
• Real-time vs. batch processing: Use cases and tools
• Monitoring and logging: CloudWatch, Glue job metrics, and Databricks alerts
• Cost optimization: Right-sizing clusters, spot instances, and storage tiers
• Best practices for production-ready pipelines

Hands-On Lab:

• Build a pipeline using Glue to ingest data, Databricks to transform it, and Redshift to store curated data.
• Query the final dataset with Athena and visualize results.
• Set up CloudWatch alarms for pipeline monitoring.

Project:

• Architect an end-to-end pipeline for a customer analytics use case, from raw S3 data to a Redshift 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 Athena and visualize insights.
  • Document the architecture and present findings.

Career Prep:

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

Additional Resources

• Tools Covered: AWS S3, AWS Glue, Amazon Athena, Amazon Redshift, Databricks, PySpark, Delta Lake, AWS CLI, CloudWatch
• Sample Datasets: Retail sales, customer data, and streaming logs
• Supplementary Materials: Cheat sheets for PySpark, SQL, and AWS 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