AWS Certified Data Analytics

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Module 1 – Data Collection
  • Overview of Data Ingestion in AWS Selecting services for ingestion: Amazon Kinesis Data Streams & Firehose AWS IoT Core AWS Snowball / Snowmobile Data ingestion patterns: Batch ingestion Real-time ingestion Micro-batch Data transfer strategies: AWS Direct Connect AWS DataSync AWS Storage Gateway Lab: Stream real-time data with Kinesis & deliver to S3
Module 2 – Storage and Data Management
  • Choosing storage solutions: Amazon S3 (Standard, Intelligent-Tiering, Glacier) Amazon Redshift Amazon RDS DynamoDB Building Data Lakes with AWS Lake Formation Data partitioning, compression, lifecycle policies Data cataloging with AWS Glue Data Catalog Metadata management & access control with Lake Formation Lab: Create a secure Data Lake with S3 + Lake Formation
Module 3 – Processing
  • ETL with AWS Glue (jobs, crawlers, transformations) Processing Big Data on AWS: Amazon EMR (Spark, Hive, Presto) AWS Lambda for lightweight transformations Kinesis Data Analytics for streaming analytics Ensuring data quality & transformation logic Pipeline orchestration using: AWS Step Functions Amazon Managed Workflows for Apache Airflow (MWAA) Lab: Build ETL workflow using Glue + Step Functions
Module 4 – Analysis and Visualization
  • Querying and analyzing data with: Amazon Athena Amazon Redshift Spectrum Data Warehousing with Amazon Redshift Interactive dashboards with Amazon QuickSight Analyzing structured & semi-structured data with SQL/BI tools Performance optimization & caching strategies (Redshift RA3, Spectrum) Lab: Build QuickSight dashboards with Athena & Redshift data
Module 5 – Security
  • Data encryption: Encryption at rest with KMS Encryption in transit (SSL/TLS) IAM – role-based access control S3 bucket policies & access logging Security in Redshift & Glue (VPC, resource policies) Auditing & Monitoring: AWS CloudTrail Amazon CloudWatch AWS Config Lab: Apply KMS encryption & audit access logs for S3/Redshift
Module 6 – Key AWS Services to Master
  • Amazon S3, Lake Formation, Glue Amazon Kinesis (Streams, Firehose, Analytics) Amazon Redshift, Athena, EMR Amazon QuickSight IAM, KMS, CloudTrail, CloudWatch Final Exam Prep: Domain weightage review, Mock tests
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Course Detail

AWS Certified Data Analytics – Specialty – Training Curriculum

 

  • Module 1 – Data Collection 
    • Overview of Data Ingestion in AWS
    • Selecting services for ingestion:
      • Amazon Kinesis Data Streams & Firehose
      • AWS IoT Core
      • AWS Snowball / Snowmobile
    • Data ingestion patterns:
      • Batch ingestion
      • Real-time ingestion
      • Micro-batch
    • Data transfer strategies:
      • AWS Direct Connect
      • AWS DataSync
      • AWS Storage Gateway
    • Lab: Stream real-time data with Kinesis & deliver to S3
  • Module 2 – Storage and Data Management 
    • Choosing storage solutions:
      • Amazon S3 (Standard, Intelligent-Tiering, Glacier)
      • Amazon Redshift
      • Amazon RDS
      • DynamoDB
    • Building Data Lakes with AWS Lake Formation
    • Data partitioning, compression, lifecycle policies
    • Data cataloging with AWS Glue Data Catalog
    • Metadata management & access control with Lake Formation
    • Lab: Create a secure Data Lake with S3 + Lake Formation
  • Module 3 – Processing 
    • ETL with AWS Glue (jobs, crawlers, transformations)
    • Processing Big Data on AWS:
      • Amazon EMR (Spark, Hive, Presto)
      • AWS Lambda for lightweight transformations
      • Kinesis Data Analytics for streaming analytics
    • Ensuring data quality & transformation logic
    • Pipeline orchestration using:
      • AWS Step Functions
      • Amazon Managed Workflows for Apache Airflow (MWAA)
    • Lab: Build ETL workflow using Glue + Step Functions
  • Module 4 – Analysis and Visualization 
    • Querying and analyzing data with:
      • Amazon Athena
      • Amazon Redshift Spectrum
    • Data Warehousing with Amazon Redshift
    • Interactive dashboards with Amazon QuickSight
    • Analyzing structured & semi-structured data with SQL/BI tools
    • Performance optimization & caching strategies (Redshift RA3, Spectrum)
    • Lab: Build QuickSight dashboards with Athena & Redshift data
  • Module 5 – Security 
    • Data encryption:
      • Encryption at rest with KMS
      • Encryption in transit (SSL/TLS)
    • IAM – role-based access control
    • S3 bucket policies & access logging
    • Security in Redshift & Glue (VPC, resource policies)
    • Auditing & Monitoring:
      • AWS CloudTrail
      • Amazon CloudWatch
      • AWS Config
    • Lab: Apply KMS encryption & audit access logs for S3/Redshift
  • Module 6 – Key AWS Services to Master
    • Amazon S3, Lake Formation, Glue
    • Amazon Kinesis (Streams, Firehose, Analytics)
    • Amazon Redshift, Athena, EMR
    • Amazon QuickSight
    • IAM, KMS, CloudTrail, CloudWatch
    • Final Exam Prep: Domain weightage review, Mock tests
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