AWS Data Engineering
What is AWS Data Engineering?
AWS Data Engineering refers to the practice of designing, building, and managing data pipelines and architectures using Amazon Web Services (AWS) tools and services. It involves leveraging AWS’s cloud platform to process, store, and analyze large volumes of data for data-driven insights.
AWS Data Engineering involves using Amazon Web Services (AWS) tools to design, build, and manage data pipelines and architectures. Key tasks include:
- Data Ingestion: Using services like Amazon Kinesis and S3 to collect data.
- Data Transformation: Cleaning and preparing data with AWS Glue or Lambda.
- Data Storage: Storing data in S3, Redshift, or RDS.
- Data Processing: Processing large datasets with Amazon EMR and Glue.
- Real-Time Processing: Handling real-time data with Kinesis.
- Security: Ensuring data security using IAM and KMS.
- Analytics: Analyzing and visualizing data with Athena and QuickSight.
AWS Data Engineering enables efficient data management, processing, and analysis on the cloud.
AWS Data Engineer Training (6 Weeks)
Learn how to design and implement scalable, reliable, and secure data pipelines in the cloud. Key topics include:
- AWS Core Services: S3, EC2, Lambda, Glue, and Redshift.
- ETL Pipelines: Designing and automating Extract, Transform, and Load processes.
- Data Lake Architecture: Setting up data lakes with AWS Lake Formation.
- Stream Processing: Using Kinesis for real-time data streaming.
- Data Security and Compliance: IAM roles, policies, and encryption.
- Hands-On Labs: Building end-to-end data workflows.