AWS Data Engineer Training | AWS Data Engineer Pipeline setup | AWS Data Engineer Course

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AWS Data Engineer – Overview

An AWS Data Engineer is responsible for designing, building, and managing scalable data pipelines and storage solutions on Amazon Web Services (AWS). Their main focus is to enable organizations to collect, process, store, and analyze data efficiently for analytics, AI/ML, and business intelligence.

Key Responsibilities

Data Ingestion & Integration

Build ETL/ELT pipelines to move data from multiple sources into AWS.

Use AWS Glue, AWS Data Pipeline, Kinesis, Lambda, DMS (Database Migration Service).

Data Storage & Management

Store structured, semi-structured, and unstructured data.

Use Amazon S3, Redshift, DynamoDB, RDS, Lake Formation.

Data Processing

Transform and clean data for downstream analytics.

Use AWS Glue, EMR (Hadoop/Spark), Athena, Lambda.

Data Analytics & BI Support

Enable analytics by preparing data for Amazon Redshift, Athena, or QuickSight.

Support data scientists with curated datasets for ML.

Security & Governance

Ensure compliance with IAM roles, encryption, and data governance policies.

Monitoring & Optimization

Monitor pipelines with CloudWatch.

Optimize performance & cost of data workflows.

Core AWS Services for Data Engineering

Storage: Amazon S3, Redshift, DynamoDB, RDS

ETL/Data Processing: AWS Glue, EMR, Lambda, Kinesis, Data Pipeline

Analytics: Athena, Redshift Spectrum, QuickSight

Orchestration: Step Functions, MWAA (Managed Airflow)

Governance/Security: IAM, Lake Formation, KMS

Skills Required

Strong SQL & Python (PySpark, Pandas)

Big Data frameworks (Apache Spark, Hadoop)

Data Warehousing concepts

ETL design & optimization

Knowledge of streaming (Kafka, Kinesis)