Master AWS for Data Engineering
Learn to design and deploy scalable data pipelines using AWS services for modern data engineering.
This Course Includes
- 30 Hours of Hands-on Training
- Tools: AWS Glue, Redshift, Athena, Kinesis
- Online AWS Labs
- Learn ETL and Data Pipeline Skills
- Real-World Data Engineering Projects
- Serverless Data Processing with Lambda
Things You'll Learn
- Building scalable data pipelines
- ETL processes with AWS Glue
- Data warehousing with Redshift
- Real-time data streaming with Kinesis
- Querying data lakes with Athena
Course Content
Introduction to AWS Data Engineering
- Overview of data engineering on AWS.
- Key AWS services for data engineering: Glue, Redshift, S3.
- Hands-on exercise: Setting up an AWS data engineering environment.
- Understanding data lakes vs. data warehouses.
- Introduction to AWS Free Tier for data services.
- Real-world use case: Ingesting raw data into S3.
- Navigating AWS Data Pipeline and Lake Formation.
- Basic AWS CLI commands for data tasks.
Data Storage and Ingestion
- Using S3 for scalable data storage.
- Hands-on lab: Creating S3 buckets for raw and processed data.
- Ingesting data with AWS Kinesis Data Streams.
- Hands-on exercise: Streaming real-time data with Kinesis.
- Configuring AWS Data Pipeline for data ingestion.
- Real-world scenario: Ingesting IoT sensor data.
- Optimizing S3 storage with lifecycle policies.
- Best practices for data partitioning and compression.
ETL with AWS Glue
- Building ETL jobs with AWS Glue.
- Hands-on lab: Creating a Glue crawler and ETL job.
- Using Glue Data Catalog for metadata management.
- Hands-on exercise: Transforming CSV to Parquet with Glue.
- Integrating Glue with S3 and Redshift.
- Real-world case study: ETL for e-commerce analytics.
- Automating Glue jobs with triggers and schedules.
- Debugging and optimizing Glue performance.
Data Warehousing and Querying
- Setting up and managing Amazon Redshift clusters.
- Hands-on lab: Loading data into Redshift.
- Querying data lakes with Amazon Athena.
- Hands-on exercise: Running SQL queries on S3 data with Athena.
- Optimizing Redshift performance with distribution keys.
- Real-world example: Building a BI dashboard with Redshift.
- Using AWS QuickSight for data visualization.
- Best practices for data warehouse design.
Advanced Data Engineering
- Building serverless data pipelines with AWS Lambda.
- Hands-on lab: Triggering Lambda functions for data processing.
- Real-time analytics with Kinesis Data Analytics.
- Hands-on exercise: Analyzing streaming data with Kinesis.
- Orchestrating pipelines with AWS Step Functions.
- Real-world scenario: End-to-end data pipeline for marketing data.
- Monitoring pipelines with CloudWatch.
- Preparing for AWS Data Analytics certification.
Why Choose This Course?
- Led by AWS-certified data engineers
- Hands-on labs with real-world datasets
- Flexible online learning format
- Projects to showcase data engineering skills
- Prepares you for AWS Data Analytics certification