Register for AWS for Data Engineering Course
Master data engineering with our 30-hour expert-led course. Limited spots available!
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Complete the form below to reserve your place. An invoice will be sent to your email from registration.confirmation@braintechblueprint.co.uk with payment details.
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Why Join the AWS for Data Engineering Course?
This 30-hour training program is designed for data engineers aiming to build scalable data pipelines using AWS services like Glue, Redshift, Athena, and Kinesis. With hands-on labs and real-world projects, you’ll gain practical skills to excel in data engineering.
- Duration: 30 hours of expert-led training
- Level: Intermediate to Advanced
- Format: Online, self-paced with live Q&A sessions and instructor support
- Tools: AWS Glue, Redshift, Athena, Kinesis, S3, Lambda
- Certification: Earn a Certificate of Completion recognized by top employers
- Support: Access to a private community and 1:1 mentorship options
Investment: £750
An invoice will be sent to your email from registration.confirmation@braintechblueprint.co.uk with payment instructions.
What You’ll Master
Introduction to AWS Data Engineering
- Overview of data engineering on AWS
- Key concepts: Data lakes, data warehouses, ETL
- Hands-on exercise: Setting up an AWS data environment
- Exploring AWS services: S3, Glue, Redshift
- Real-world use case: Ingesting raw data into S3
- Navigating AWS Free Tier for data services
Data Storage and Ingestion
- Using S3 for scalable data storage
- Hands-on lab: Creating S3 buckets for raw data
- Ingesting data with AWS Kinesis Data Streams
- Real-world scenario: Streaming IoT sensor data
- Configuring AWS Data Pipeline for ingestion
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
- Transforming data formats (e.g., CSV to Parquet)
- Real-world case study: ETL for e-commerce analytics
Data Warehousing and Querying
- Setting up Amazon Redshift clusters
- Hands-on lab: Loading data into Redshift
- Querying data lakes with Amazon Athena
- Real-world example: Building a BI dashboard
- Using AWS QuickSight for data visualization
Advanced Data Engineering
- Building serverless pipelines with AWS Lambda
- Hands-on lab: Triggering Lambda for data processing
- Real-time analytics with Kinesis Data Analytics
- Orchestrating pipelines with AWS Step Functions
- Monitoring pipelines with CloudWatch
Why Choose This Course?
- Led by AWS-certified data engineers
- Hands-on labs with real-world datasets
- Flexible online format tailored to your schedule
- Build a portfolio with 3+ practical projects
- Prepares you for AWS Data Analytics certification
- Lifetime access to course updates
Ready to Master AWS for Data Engineering?
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