AWS Data Engineer


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Course Overview
Intended Audience
Pre-requisite
Learning Objective
Extended Outcome
about this course
An AWS Data Engineer is an IT professional who specializes in designing, building, and managing data pipelines, storage solutions, and analytics platforms on Amazon Web Services (AWS). They focus on handling large-scale data processing, ensuring data quality, and enabling organizations to make data-driven decisions.

Key Responsibilities:
Data Pipeline Development:

Design and implement scalable data pipelines for collecting, transforming, and loading (ETL/ELT) data using AWS services like AWS Glue, AWS Data Pipeline, or Apache Spark on Amazon EMR.
Automate workflows and ensure efficient data movement across systems.
Data Storage and Management:

Design and optimize data storage solutions, including data lakes (Amazon S3), data warehouses (Amazon Redshift), and databases (Amazon RDS, DynamoDB).
Manage schema design, indexing, and partitioning to improve query performance.
Data Processing and Transformation:

Process and transform large datasets using services like AWS Lambda, Amazon Athena, or AWS Batch.
Use tools like AWS Glue, Apache Spark, or Python-based libraries for data wrangling and transformation.
Big Data and Analytics:

Enable advanced analytics and machine learning workloads by integrating data with AWS AI/ML services such as Amazon SageMaker.
Use services like Amazon Kinesis for real-time data processing and analytics.
Monitoring and Optimization:

Monitor data pipelines and systems using AWS CloudWatch and other observability tools.
Optimize costs, storage, and processing for efficient data management.
Security and Compliance:

Implement security best practices, including encryption (KMS), IAM policies, and network configurations.
Ensure compliance with data governance and industry regulations (e.g., GDPR, HIPAA).
Skills Required:
Data Engineering Expertise:

Proficiency in ETL/ELT processes and data pipeline architectures.
Familiarity with distributed systems and large-scale data processing.
AWS Expertise:

Deep knowledge of AWS data services like S3, Redshift, DynamoDB, Glue, Athena, and Kinesis.
Programming and Scripting:

Strong skills in Python, Java, or Scala for data manipulation and processing.
Database Management:

Hands-on experience with SQL and NoSQL databases.
Big Data Tools:

Knowledge of Apache Hadoop, Spark, Kafka, or Flink for big data processing.
Analytical Thinking:

Ability to design systems that support data analysis and visualization.
who needs this course?
This course is suitable for:
• Sales Businesses
• Strategic Marketing individuals
• Data-Driven Business Analysts
• Project managers
• AWS Academy students
• Other IT-related professionals
Getting started
No prerequisites are there to pursue this course, therefore, those candidates will surely benefit from already having a basic understanding of IT services. So, a little bit of exploration of the AWS platform and its services are recommended by us.
At the end of this course, you will be able to KNOW;
• AWS Cloud technology certification
• Benefits of having an AWS certification
• What steps do I need to take to get AWS certified
• How long does it take to become AWS certified
• What are the multiple AWS certification levels
• Certified Solution Architect Associate
What you stand to gain
By the end of this AWS CLF-C01 course, you will be learning about Cloud concepts, Security, Compliance, Technology, Billing & Pricing mechanism in AWS.
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