Description
Data Engineering Master Class using AWS Analytics Services is the name of the Data Engineering Master Class using AWS Analytics Services published by Udemy Academy.
Data engineering is all about creating data pipelines to get data from multiple sources into data lakes or data warehouses and then from the data lakes or data warehouses to downstream systems. As part of this course, I will walk you through how to build Data Engineering Pipelines using the AWS Data Analytics Stack. It includes services like Glue, Elastic Map Reduce (EMR), Lambda Functions, Athena, EMR, Kinesis and many others.
- Setting up the development environment
- Getting Started with AWS
- Storage – All About AWS s3 (Simple Storage Service)
- User-level security – Manage users, roles, and policies using IAM
- Infrastructure – AWS EC2 (Elastic Cloud Compute)
- Ingest data using AWS Lambda functions
- Overview of AWS adhesive components
- Start the Spark History server for AWS Glue Jobs
- Deep dive into the AWS glue catalog
- Explore AWS Glue Job APIs
- AWS sticky bookmarks
- Pyspark development life cycle
- Getting Started with AWS EMR
- Deploying Spark applications using
- AWS EMR flow pipeline using AWS Kinesis
- Consuming data from AWS s3 using boto3 Consumed using AWS Kinesis
- Populate GitHub data to AWS Dynamodb
- Overview of Amazon AWS Athena
- Amazon AWS Athena using Amazon’s AWS CLI
- AWS Athena using Python boto3
- Getting Started with Amazon AWS Redshift
- Copy data from AWS s3 to AWS Redshift tables
- Develop applications using AWS Redshift Cluster
- AWS Redshift tables with AWS Distkey and Sortkey
- AWS Redshift Federated Queries and Spectrum
Who is this course suitable 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
What you will learn in the Data Engineering Master Class using AWS Analytics Services course:
- Data engineering using AWS Analytics AWS Essentials features such as s3, IAM, EC2, etc
- Understanding AWS s3 for cloud-based storage
- Understanding the details of virtual machines in AWS known as EC2
- Manage AWS IAM users, groups, roles, and policies for RBAC (Role-Based Access Control)
- Manage tables using the AWS Glue
- Engineering batch data pipelines using AWS Glue Jobs
- Setting up batch data pipelines using AWS Glue Workflows
- Running queries using AWS Athena – Server Search Engine Service
- Using AWS Elastic Map Reduce (EMR) clusters to build data pipelines
- Using AWS Elastic Map Reduce (EMR) clusters for reports and dashboards
- Ingest data using AWS Lambda functions
- And….
Course details
Publisher: Udemy
Instructor: Durga Viswanatha Raju Gadiraju, Asasri Manthena , Perraju Vegiraju
Language: English
Education level: Advanced
Number of lessons: 434
Duration of education: 26 hours 15 minutes
Headlines of Data Engineering Master Class using AWS Analytics Services:
Data Engineering Master Class Course prerequisites:
- 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
Data Engineering Master Class Course pictures:
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