Comprehensive pathway to master AWS data engineering skills covering data ingestion, transformation, storage management, operations, security, and governance using core AWS services.
Upon completion of this pathway, learners will be able to design, build, and maintain scalable data pipelines on AWS, implement data ingestion and transformation solutions, manage data stores and lifecycle policies, ensure data security and governance, and demonstrate proficiency in core AWS data engineering services required for the AWS Certified Data Engineer Associate certification.
Serverless data processing using AWS Lambda for event-driven data pipelines, transformations, and integrations.
Big data processing using Amazon EMR with Apache Spark, Hadoop, and other big data frameworks for large-scale data analytics.
Serverless analytics with Amazon Athena and data lake governance using AWS Lake Formation for metadata management and access control.
Real-time data streaming and processing using Amazon Kinesis services including Data Streams, Data Firehose, and Data Analytics.
Essential terminology and concepts specific to AWS data engineering and cloud data processing.
Comprehensive understanding of Amazon S3 as the foundation for data lakes, including storage classes, lifecycle management, security, and optimization...
Comprehensive data security, governance, and compliance using AWS services including IAM, KMS, CloudTrail, and governance frameworks.
Comprehensive data warehousing using Amazon Redshift including architecture, optimization, security, and integration with analytics tools.
Comprehensive understanding of AWS database services including RDS, DynamoDB, and specialized databases for data engineering workflows.
Core AWS concepts including global infrastructure, security model, pricing, and basic services essential for data engineering.
Comprehensive preparation for the AWS Certified Data Engineer Associate exam including format, domains, strategies, and practice resources.
Essential resources, communities, and continuing education opportunities for AWS data engineering professionals.
Comprehensive understanding of AWS Glue for serverless ETL operations, including data catalog, crawlers, jobs, and workflows.
Data pipeline orchestration using AWS Step Functions, Apache Airflow (MWAA), and other workflow management tools for complex data processing workflows...
Comprehensive monitoring, logging, and operational practices for data engineering pipelines using CloudWatch, CloudTrail, and other AWS monitoring ser...
Essential programming skills including Python, SQL, and scripting languages required for data engineering tasks.
Foundation concepts of data engineering including data types, storage formats, processing patterns, and cloud computing principles.