Why Do I Enroll?
🔰AWS Core Services for Data Engineering, Data Ingestion and Streaming
(105 questions)
🔰Data Processing, Data Storage
(104 questions)
🔰Data Analytics and Visualization, Data Security and Compliance
(104 questions)
🔰Monitoring and Optimization, Machine Learning & Data Pipelines
(88 questions)
🔰ETL (Extract, Transform, Load), Architecting and Best Practices
(104 questions)
🔰Big Data Tools and Integrations
(44 questions)
About Course
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 auto scaling
✅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
Course Features
📍AWS Data Engineer Interview Aspirants
Anyone who wants to test, Revise and Practice their knowledge in AWS Data Engineering domainwise
