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AWS With Data Analytics

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AWS With Data Analytics

AWS

Duration
45 Hours

Course Description

       An AWS with Data Analytics course typically covers using Amazon Web Services (AWS) to analyze and manage large datasets. It focuses on building skills in data storage, processing, and visualization using AWS services, often preparing learners for the AWS Certified Data Analytics - Specialty certification. The curriculum often includes practical, hands-on experience with tools like Amazon Kinesis, Amazon Athena, Amazon Redshift, and Amazon QuickSight.

Course Outline For AWS with Data Analytics

1. Introduction to data analytics and AWS

  • Understanding the importance of data analytics and its types (descriptive, diagnostic, predictive, prescriptive).
  • Introduction to Big Data concepts and challenges.
  • Overview of the AWS platform and its advantages for data analytics.
  • Comparison of batch processing with real-time streaming data processing. 

2. Data collection and storage

  • Data Collection Services: Amazon Kinesis for real-time data ingestion and processing, including Kinesis Data Streams and Kinesis Firehose.
  • Data Migration Services: AWS Database Migration Service (DMS), AWS Snowball, and AWS Snowmobile for transferring data to AWS.
  • Data Storage: Amazon S3 (Simple Storage Service) as a data lake, including storage classes, lifecycle management, and security.
  • Data Warehousing: Amazon Redshift for large-scale data warehousing and analytics.
  • NoSQL Databases: Amazon DynamoDB for NoSQL data storage and management.
  • Data Lake Governance: AWS Lake Formation for setting up and managing secure data lakes. 

3. Data processing and transformation

  • ETL (Extract, Transform, Load): AWS Glue as a serverless ETL service for data preparation.
  • Big Data Processing: Amazon EMR for running big data frameworks like Apache Hadoop and Apache Spark.
  • Serverless Data Processing: AWS Lambda for event-driven processing and data transformation.
  • Orchestrating Workflows: AWS Step Functions and AWS Data Pipeline for building and managing data workflows. 

 

 

4. Data analysis and visualization

  • Interactive Query Service: Amazon Athena for querying data in S3 using SQL.
  • Business Intelligence: Amazon QuickSight for creating dashboards and visualizations.
  • Log and Search Analytics: Amazon OpenSearch Service for real-time log analysis and search capabilities.
  • Machine Learning (ML): Leveraging services like Amazon SageMaker for building, training, and deploying ML models. 

5. Security, governance, and compliance

  • Access Management: AWS IAM (Identity and Access Management) for securing access to AWS resources.
  • Data Encryption: Implementing encryption at rest and in transit using services like AWS Key Management Service (KMS).
  • Monitoring and Auditing: AWS CloudTrail and AWS Config for logging and auditing data access and changes.
  • Data Governance: Understanding best practices for data governance, including data classification and access control. 
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