Prepare for your AWS Data Engineer interview with this comprehensive course, covering 500+ most asked interview questions and answers. This course is designed for candidates who want to strengthen their skills in AWS core services, data ingestion, processing, storage, analytics, security, and best practices. Each topic is carefully curated to help you master AWS services and understand their real-world applications. The course is structured in a way that covers all critical areas, from fundamental concepts to advanced implementations.

Course Topics Covered:

1. AWS Core Services for Data Engineering

• Amazon S3 (Simple Storage Service)

  • Object storage fundamentals and versioning

  • Data encryption, IAM roles, and bucket policies

  • S3 Event Notifications and performance optimization

• Amazon EC2 (Elastic Compute Cloud)

  • EC2 instance types, pricing models, and autoscaling

  • Load balancing, network configurations, and security groups

• AWS IAM (Identity and Access Management)

  • Roles, policies, federated access, and MFA

  • Fine-grained data access control

• Amazon VPC (Virtual Private Cloud)

  • Subnets, route tables, NACLs, and security groups

  • VPN, Direct Connect, and VPC Peering

2. Data Ingestion and Streaming

• AWS Glue

  • Data Cataloging, Crawler configuration, and ETL Jobs

  • Integration with S3, RDS, and Redshift

• Amazon Kinesis

  • Kinesis Streams vs. Kinesis Firehose

  • Real-time processing with Kinesis Data Analytics

  • Integrations with AWS Lambda and S3

• Amazon MSK (Managed Streaming for Apache Kafka)

  • Kafka vs Kinesis: Understanding use cases

  • Kafka partitioning, replication, and MSK scaling

3. Data Processing

• AWS Lambda

  • Event-driven serverless execution and integrations with AWS services

  • Monitoring and scaling Lambda functions

• Amazon EMR (Elastic MapReduce)

  • Apache Hadoop, Spark, HBase, and Presto on EMR

  • Cluster setup, auto-scaling, and Spot Instances

• AWS Glue

  • Data transformations, Glue Data Catalog, and querying with Athena

• Amazon Athena

  • Serverless SQL queries on S3 data

  • Schema on read and partitioning techniques for optimization

4. Data Storage

• Amazon Redshift

  • Redshift architecture, columnar storage, and compression

  • Performance tuning and querying data with Redshift Spectrum

• Amazon RDS (Relational Database Service)

  • Backup, scaling, read replicas, and IAM authentication

  • Supported engines: MySQL, PostgreSQL, Oracle, SQL Server

• Amazon DynamoDB

  • NoSQL concepts, indexing, and auto-scaling

5. Data Analytics and Visualization

• Amazon Redshift

  • Data warehousing, performance optimization, and Spectrum for querying S3

• Amazon QuickSight

  • BI tool for data visualization, dashboard creation, and ML insights

• Amazon Elasticsearch Service

  • Full-text search and integration with Logstash and Kibana

6. Data Security and Compliance

• AWS KMS (Key Management Service)

  • Data encryption, key rotation, and policies

• AWS CloudTrail

  • Logging, auditing, and integrating with S3 and CloudWatch

• AWS Secrets Manager

  • Secure storage and rotation of credentials and API keys

• Amazon Macie

  • Data security and privacy in S3, identifying Personally Identifiable Information (PII)

7. Monitoring and Optimization

• Amazon CloudWatch

  • Monitoring AWS resources, custom metrics, alarms, and logs

• AWS Cost Explorer

  • Cost optimization for services like S3, Redshift, Glue, and EMR

• AWS Trusted Advisor

  • Recommendations for performance, cost optimization, and security

8. Machine Learning & Data Pipelines

• Amazon SageMaker

  • Building and deploying ML models, integration with S3 and Redshift

• Amazon Glue for ML

  • Applying ML transformations and anomaly detection in Glue jobs

• Kinesis Data Analytics for Machine Learning

  • Real-time data analytics and inference

9. ETL (Extract, Transform, Load)

• AWS Data Pipeline

  • Data workflow orchestration and monitoring

• AWS Step Functions

  • Serverless orchestration with Lambda, Glue, and Batch

• AWS Batch

  • Running batch jobs, job queues, and dependencies

10. Architecting and Best Practices

• Data Lake Architecture on AWS

  • Best practices for creating data lakes with S3, Glue, and Athena

• Event-Driven Architecture

  • Real-time event processing with Lambda, S3, and Kinesis

• AWS Well-Architected Framework

  • Principles for cost optimization, performance, security, and reliability

• Serverless vs Server-based Data Pipelines

  • Comparing Lambda, Glue, Batch vs EMR, EC2 for data pipelines

11. Big Data Tools and Integrations

• AWS Glue with Apache Spark

  • Writing and optimizing Spark jobs in Glue

• Amazon Redshift with Apache Hudi, Delta Lake

  • Efficient updates to Redshift tables using Hudi and Delta Lake

• AWS Glue and Kafka/MSK Integration

  • Building near real-time data pipelines with Kafka/MSK

This course is ideal for professionals seeking to master AWS Data Engineering services and confidently prepare for interviews. With over 500 practice questions, you’ll cover each key service in-depth and gain a solid understanding of how to integrate them for building scalable, efficient data pipelines and architecture

Who this course is for:

• AWS Data Engineer Interview Aspirants
• Anyone who wants to test, Revise and Practice their knowledge in AWS Data Engineering domainwise