AWS Data Engineering
AWS Data Engineering involves building and managing data pipelines on Amazon Web Services to collect, process, and store data for analytics and machine learning.
- Collects and integrates data from multiple sources.
- Stores data using Amazon S3, Redshift, RDS, or DynamoDB.
- Processes and transforms data with AWS Glue, EMR, or Lambda.
- Implements data models for analytics and BI tools.
- Ensures data security and compliance using IAM and KMS.
- Automates workflows and monitors systems with CloudWatch.
- Basic programming knowledge (Python, SQL, or Java).
- Understanding of databases (SQL and NoSQL).
- Knowledge of ETL and data warehousing concepts.
- Familiarity with cloud computing and AWS fundamentals.
- Basic Linux or command-line skills.
- Understanding of networking and security basics.
- Optional: Experience with big data tools like Spark or Hadoop.
- Cloud and AWS Basics
- Understanding Data Engineering Concepts
- Data Storage with Amazon S3, RDS, and DynamoDB
- Data Ingestion using AWS Glue, Kinesis, and Data Pipeline
- Data Processing with AWS Glue, EMR (Spark, Hadoop), and Lambda
- Data Warehousing using Amazon Redshift
- Data Lake Architecture and Implementation
- Monitoring and Automation with CloudWatch and Step Functions
- Security, IAM, and Data Governance
- Project Work and Certification Preparation
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