Data Engineering using AWS Data Analytics

Data Engineering with AWS: Build Robust Data Pipelines

Data Engineering Fundamentals & AWS Data Analytics Stack

This comprehensive course guides you through the world of data engineering, focusing on building efficient data pipelines to move data from various sources into centralized repositories like Data Lakes and Data Warehouses. We'll leverage the powerful AWS Data Analytics Stack to achieve this, covering essential services like AWS Glue, Elastic MapReduce (EMR), Lambda Functions, Athena, Kinesis, and more.

Here's what you'll learn:

1. Setting Up Your AWS Environment:

• Development Environment Setup: Get your workspace ready for hands-on learning.
• AWS Storage: Master AWS S3 (Simple Storage Service), a cornerstone of cloud storage.
  • Creating and managing S3 buckets and objects.
  • Understanding S3 storage classes and versioning.
  • Securing your S3 data with IAM (Identity and Access Management).
• User Level Security: Learn to manage users, roles, and policies using AWS IAM.
• Infrastructure: Understand the basics of AWS EC2 (Elastic Cloud Compute), virtual servers in the cloud.

2. Data Ingestion with AWS Lambda & Kinesis:

• Data Ingestion using AWS Lambda Functions: Develop and deploy serverless functions for data processing.
  • Utilizing Python for Lambda development.
  • Managing bookmarks for incremental data loads.
• Streaming Data Ingestion using AWS Kinesis: Build real-time data pipelines.
  • Understanding Kinesis Firehose and Kinesis Data Streams.
  • Ingesting data from log files and other sources.

3. Data Catalog & ETL with AWS Glue:

• Overview of AWS Glue Components: Explore key Glue services like Glue Crawlers, Glue Databases, and Glue Tables.
• AWS Glue Catalog: Learn to manage your data assets with the Glue Catalog.
  • Creating and managing Glue Databases and Tables.
  • Analyzing data using AWS Athena.
• AWS Glue Job APIs: Manage and orchestrate your data processing workflows.
• AWS Glue Job Bookmarks: Implement incremental data loading strategies.
• Development Lifecycle for PySpark: Build and manage Spark applications.

4. Big Data Processing with AWS EMR:

• Getting Started with AWS EMR: Deploy and manage scalable big data processing clusters.
  • Understanding EMR cluster configurations and scaling policies.
  • Utilizing the EMR console and CLI.
• Deploying Spark Applications using AWS EMR: Run distributed data processing jobs.
• Spark Application Deployment: Deploy applications using EMR clusters.
• Running Spark Applications: Execute Spark jobs in client and cluster modes.

5. Data Warehousing with Amazon AWS Redshift:

• Getting Started with Amazon AWS Redshift: Learn the basics of Redshift data warehousing.
  • Creating and managing Redshift clusters.
  • Utilizing the Redshift console and SQL editor.
• Redshift Data Modeling: Design and manage your data warehouse schema.
  • Creating and managing tables, indexes, and partitions.
• Copying Data into AWS Redshift: Efficiently load data from S3 into Redshift.
• Developing Applications with AWS Redshift: Interact with your data warehouse using SQL and Python.
• AWS Redshift Distribution Keys & Sort Keys: Optimize query performance.
• AWS Redshift Federated Queries & Spectrum: Query data directly from S3 using Redshift.

6. Advanced Redshift & Federated Queries:

• AWS Redshift Federated Queries: Query data residing outside of Redshift.
• AWS Redshift Spectrum: Query data directly from S3 using SQL.
• AWS Glue & Redshift Integration: Leverage Glue for data preparation and loading into Redshift.

7. Data Manipulation & Analysis:

• Populating AWS DynamoDB with GitHub Data: Store and manage data in a NoSQL database.
• Consuming Data from AWS S3 using Python (boto3): Access and process data stored in S3.
• Developing Applications with AWS Redshift: Build applications leveraging Redshift data.

Key Technologies Covered:

• AWS S3
• AWS IAM
• AWS EC2
• AWS Lambda
• AWS Glue
• AWS EMR
• AWS Kinesis
• AWS Athena
• Amazon AWS Redshift
• Amazon DynamoDB
• Python
• Spark
• SQL

**This course provides a practical, hands-on