MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 124 lectures (9h 35m) | Size: 3.61 GB
Data Eeering 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.
Build Data Eeering Pipelines using AWS Analytics Services such as Glue, EMR, Athena, Kinesis, Quick Sight, etc
Data Eeering leveraging AWS Analytics features
Managing Tables using Glue Catalog
Eeering Batch Data Pipelines using Glue Jobs
Orchestrating Batch Data Pipelines using Glue Workflows
Running Queries using Athena - Server less query ee 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
Eeering Streaming Pipelines using Kinesis
Streaming Web Server logs using Kinesis Firehose
Programming experience using Python
Data Eeering experience using Spark
Ability to write and interpret SQL Queries
This course is ideal for experienced data eeers to add AWS Analytics Services as key skills to their profile
As part of this course, I will walk you through how to build Data Eeering 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 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
Bner or Intermediate Data Eeers who want to learn AWS Analytics Services for Data Eeering
Intermediate Application Eeers who want to explore Data Eeering using AWS Analytics Services
Data and Analytics Eeers who want to learn Data Eeering using AWS Analytics Services
Testers who want to learn Databricks to test Data Eeering applications built using AWS Analytics Services
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