Data Engineering on AWS - The complete training

Data Engineering is a rapidly growing field with high demand and limited skilled professionals. This comprehensive training empowers you to build the confidence and credibility needed to excel as a Data Engineer, Data Architect, and in other related data roles. You'll gain practical skills to implement data pipelines, and master techniques for monitoring, troubleshooting, and optimizing cost and performance – all while adhering to best practices.

This course significantly enhances your ability to handle critical data engineering tasks, including:

• Data Ingestion & Transformation: Ingest data from various sources, transform and process it effectively, and orchestrate robust data pipelines using programming concepts.
• Data Store Management: Select the optimal data store for your needs, design efficient data models, catalog data schemas, and effectively manage data lifecycles.
• Data Operations & Support: Operationalize, maintain, and monitor data pipelines for optimal performance. Analyze data to ensure high data quality and automate data processing using AWS services.
• Data Security & Governance: Implement robust authentication and authorization mechanisms, ensure data encryption and masking, prepare logs for audit, and understand crucial data privacy and governance principles.

While specifically designed to prepare you for the AWS Certified Data Engineer – Associate (DEA-C01) certification, this training provides invaluable knowledge for anyone seeking to deepen their understanding of Data Engineering in AWS Cloud.

You will gain proficiency in these key areas:

Data Ingestion and Transformation:

• Data Ingestion: Learn to bring data into your pipelines from diverse sources.
• Data Transformation: Master techniques to transform and process raw data into usable formats.
• Pipeline Orchestration: Effectively orchestrate complex data pipelines for seamless data flow.
• Programming Concepts: Apply programming skills to enhance data processing capabilities.

Data Store Management:

• Data Store Selection: Make informed decisions about choosing the right data store for specific use cases.
• Data Cataloging: Understand and utilize data cataloging systems for efficient data discovery.
• Data Lifecycle Management: Effectively manage the entire lifecycle of data, from creation to archival.
• Data Modeling: Design robust data models and manage schema evolution for scalability.

Data Operations and Support:

• Automated Data Processing: Automate data processing tasks using powerful AWS services.
• Data Analysis: Leverage AWS services for efficient data analysis and insights.
• Pipeline Monitoring: Maintain and monitor data pipelines to ensure reliability and performance.
• Data Quality Assurance: Implement measures to ensure consistently high data quality.

Data Security and Governance:

• Authentication & Authorization: Implement secure authentication and authorization protocols.
• Data Encryption: Apply data encryption and masking techniques to protect sensitive information.
• Audit Logging: Prepare comprehensive logs for auditing and compliance.
• Data Privacy & Governance: Understand and implement best practices for data privacy and governance.