AWS Data Analytics Course

Categories

AWS Specialty

AWS Data Analytics Course

Student Prerequisites

AWS Academy Data Analytics requires a strong foundation in IT concepts and skills, such as those that students gain through the AWS Academy Cloud Foundations course.Students may benefit from completing the freeAWS Data Analytics Fundamentals online training.Before taking this сntermediatecourse, students should be able to:

  • Describe the difference between an online transaction processing (OLTP) system and an online analytical processing (OLAP) system.
  • Describe the differences between a database and a data warehouse.
  • Design a set of data objects and table relations for a simple data set.
  • Write simple data retrieval and manipulation queries with SQL.
  • Describe the five V’s of big data (Velocity, Volume, Value, Variety, and Veracity).
  • List common use cases and domains for big data solutions.
  • Normalizedatabase design.Students are not expected to have programming experience.

Course Contents

Lab Exercises

  • Lab 1 Ingesting Data into Amazon S3
    • Accessing Amazon S3 in the console
    • Creating an Amazon S3 bucket
    • Securing an Amazon S3 bucket
    • Loading data into an Amazon S3 bucket
  • Lab 2 Querying Amazon S3 Data Using Amazon Athena
    • Creating an Amazon S3 bucket
    • Loading data into an Amazon S3 bucket
    • Querying data with Amazon Athena
  • Lab 3 Transforming Data Using Amazon S3AWS Glue, and Amazon Athena
    • Creating an Amazon S3 bucket
    • Uploading large data files
    • Inferring a schema from a data set with AWS Glue
    • Querying data with Amazon Athena
    • Uploading large data files partitioned by date
  • Lab 4 Loading the Amazon Redshift Cluster with Data and Querying
    • Creating an Amazon Redshift Cluster
    • Loading data into an Amazon S3 bucket
    • Creating a table in Amazon Redshift
    • Loading data into Amazon Redshift from Amazon S3
    • Querying data in Amazon Redshift
  • Lab 5 Analyze data with Amazon SageMaker, Jupyter notebooks, and Bokeh
    • Creatinga Jupyter notebook with Amazon Sage
    • Maker.Importing data into a Jupyter notebook.
    • Creating a presentation with a Jupyter notebook.
    • Visualizingdata with the open-source Bokeh Python package
  • Lab 6 Setting up and Executing a Data Pipeline Job to Load Data into Amazon S3
    • Creating an Amazon Redshift cluster
    • Creating a data pipeline to load data from Amazon S3 to Amazon Redshift using templates
    • Creating Amazon QuickSight visualizations
  • Lab 7 Streaming Data with AWS Kinesis Firehose, Amazon Elasticsearch Service, and Kibana
    • Creating an AWS Kinesis firehose delivery stream
    • Configuring an Amazon Elasticsearch Service cluster
    • Connecting AWS Kinesis firehose to deliver logs to Amazon Elasticsearch Service
    • Configuring Kibana indexes
    • Visualizing streaming data
  • Lab 8 Using AWS IoT Analytics for Data Ingestion and Analysis
    • Collecting data
    • Processing data
    • Storing data
    • Analyzing and visualizing data