Build Data Engineering Pipelines using AWS Analytics Services such as Glue, EMR, Athena, Kinesis, Quick Sight, etc
What you’ll learn
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Data Engineering leveraging AWS Analytics features
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Managing Tables using Glue Catalog
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Engineering Batch Data Pipelines using Glue Jobs
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Orchestrating Batch Data Pipelines using Glue Workflows
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Running Queries using Athena – Server less query engine service
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Using AWS Elastic Map Reduce (EMR) Clusters for building Data Pipelines
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Using AWS Elastic Map Reduce (EMR) Clusters for reports and dashboards
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Data Ingestion using Lambda Functions
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Scheduling using Events Bridge
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Engineering Streaming Pipelines using Kinesis
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Streaming Web Server logs using Kinesis Firehose
Requirements
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Programming experience using Python
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Data Engineering experience using Spark
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Ability to write and interpret SQL Queries
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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
Created by Durga Viswanatha Raju Gadiraju
Last updated 8/2021
English
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Size: 3.99 GB