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    You are at:Home»Development»Data Engineering Using AWS Analytics Services
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    Data Engineering Using AWS Analytics Services

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    Data Engineering Using AWS Analytics Services - Online Course Download
    Data Engineering Using AWS Analytics Services - Online Course Download
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    What you'll learn :

    Data Engineering leveraging AWS Analytics features
    Managing Tables using Glue Catalog
    Engineering Batch Data Pipelines using Glue Jobs
    Orchestrating Batch Data Pipelines using Glue Workflows
    Running Queries using Athena – Server less query engine service
    Using AWS Elastic Map Reduce (EMR) Clusters for building Data Pipelines
    Using AWS Elastic Map Reduce (EMR) Clusters for reports and dashboards
    Data Ingestion using Lambda Functions
    Scheduling using Events Bridge
    Engineering Streaming Pipelines using Kinesis
    Streaming Web Server logs using Kinesis Firehose

    Requirements :

    Programming experience using Python
    Data Engineering experience using Spark
    Ability to write and interpret SQL Queries
    This course is ideal for experienced data engineers to add AWS Analytics Services as key skills to their profile

    Description :

    Data Engineering is all about building Data Pipelines to get data from multiple sources into Data Lake or Data Warehouse and then from Data Lake or Data Warehouse to downstream systems. As part of this course, I will walk you through how to build Data Engineering Pipelines using AWS Analytics Stack. It includes services such as Glue, Elastic Map Reduce (EMR), Lambda Functions, Athena, QuickSight, and many more.

    Here are the high-level steps which you will follow as part of the course.

    Setup Development Environment
    Getting Started with AWS
    Development Life Cycle of Pyspark
    Overview of Glue Components
    Setup Spark History Server for Glue Jobs
    Deep Dive into Glue Catalog
    Exploring Glue Job APIs
    Glue Job Bookmarks
    Data Ingestion using Lambda Functions
    Streaming Pipeline using Kinesis
    Consuming Data from s3 using boto3
    Populating GitHub Data to Dynamodb

    Getting Started with AWS

    Introduction – AWS Getting Started
    Create s3 Bucket
    Create IAM Group and User
    Overview of Roles
    Create and Attach Custom Policy
    Configure and Validate AWS CLI

    Development Lifecycle for Pyspark

    Setup Virtual Environment and Install Pyspark
    Getting Started with Pycharm
    Passing Run Time Arguments
    Accessing OS Environment Variables
    Getting Started with Spark
    Create Function for Spark Session
    Setup Sample Data
    Read data from files
    Process data using Spark APIs
    Write data to files
    Validating Writing Data to Files
    Productionizing the Code

    Overview of Glue Components

    Introduction – Overview of Glue Components
    Create Crawler and Catalog Table
    Analyze Data using Athena
    Creating S3 Bucket and Role
    Create and Run the Glue Job
    Validate using Glue CatalogTable and Athena
    Create and Run Glue Trigger
    Create Glue Workflow
    Run Glue Workflow and Validate

    Who this course is for :

    Beginner or Intermediate Data Engineers who want to learn AWS Analytics Services for Data Engineering
    Intermediate Application Engineers who want to explore Data Engineering using AWS Analytics Services
    Data and Analytics Engineers who want to learn Data Engineering using AWS Analytics Services
    Testers who want to learn Databricks to test Data Engineering applications built using AWS Analytics Services

    Course Size Details :

    13 hours on-demand video
    59 articles
    Full lifetime access
    Access on mobile and TV
    Certificate of completion

    View Demo

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