Cloud Service - Analytics

AWS cloud service for Analytics:

  1. Introduction to AWS Analytics Services: Overview of the AWS analytics services and how they fit into the data lifecycle (collection, storage, processing, analysis, visualization).

  2. Data Collection and Storage with AWS: Discussing services like Amazon S3, Amazon Kinesis, and AWS Glue for data collection and storage.

  3. Data Processing with AWS: Exploring how services like AWS Lambda and Amazon EMR can be used for data processing.

  4. Data Analysis with AWS: Deep dive into Amazon Redshift and Amazon Athena for data analysis.

  5. Data Visualization with AWS: Using Amazon QuickSight for creating interactive dashboards and visualizations.

  6. Real-time Analytics with AWS: Discussing how to perform real-time analytics with Amazon Kinesis.

  7. Big Data Solutions with AWS: Overview of AWS’s big data solutions like Amazon EMR, Amazon Redshift, and Amazon DynamoDB.

  8. Machine Learning with AWS: Introduction to Amazon SageMaker and how it integrates with AWS’s analytics services.

  9. Security and Compliance with AWS Analytics: Discussing AWS’s security features and how to ensure data privacy and compliance.

  10. Cost Optimization for AWS Analytics: Strategies for managing costs while using AWS analytics services.

Google Cloud Service for Analytics:

  1. Introduction to Google Cloud Analytics Services: Overview of the Google Cloud analytics services and how they fit into the data lifecycle (collection, storage, processing, analysis, visualization).

  2. Data Collection and Storage with Google Cloud: Discussing services like Google Cloud Storage, Google Cloud Pub/Sub, and Google Cloud Data Catalog for data collection and storage.

  3. Data Processing with Google Cloud: Exploring how services like Google Cloud Dataflow and Google Cloud Dataproc can be used for data processing.

  4. Data Analysis with Google Cloud: Deep dive into Google BigQuery for data analysis.

  5. Data Visualization with Google Cloud: Using Google Data Studio for creating interactive dashboards and visualizations.

  6. Real-time Analytics with Google Cloud: Discussing how to perform real-time analytics with Google Cloud Pub/Sub and Google Cloud Dataflow.

  7. Big Data Solutions with Google Cloud: Overview of Google Cloud’s big data solutions like Google Cloud Dataproc, Google BigQuery, and Google Cloud Bigtable.

  8. Machine Learning with Google Cloud: Introduction to Google Cloud AI Platform and how it integrates with Google Cloud’s analytics services.

  9. Security and Compliance with Google Cloud Analytics: Discussing Google Cloud’s security features and how to ensure data privacy and compliance.

  10. Cost Optimization for Google Cloud Analytics: Strategies for managing costs while using Google Cloud analytics services.

Just like with AWS, each of these topics is quite broad and can be broken down into more specific subtopics. Including practical examples and use cases to illustrate each concept is also a good idea. Happy studying! 😊

PreviousData WarehouseNextData Visualization

Last updated

Was this helpful?