Oreilly – Data Engineering with Python and AWS Lambda LiveLessons 2023-9 – Download

Description

Data Engineering Using Python and AWS Lambda LiveLessons course. This course will teach you how to build rich, powerful data science pipelines using Python, the same language data scientists use to build machine learning models. By adopting serverless data processing in Python, you can build highly scalable distributed systems on the AWS cloud infrastructure.

In this course, you will learn about the new serverless paradigm, which means using events and event-driven applications instead of expensive and complex servers. Some of the benefits of programming with AWS Lambda in Python include: no server management required, continuous scalability, and sub-second scaling. There are many use cases including data processing, stream processing, IoT infrastructure, mobile and web applications. In this course, you’ll learn to use a new pattern in software architecture that makes it easier to write, maintain, and deploy your code.

AWS Lambda functions are the building blocks for building advanced applications and services on AWS. In this course, you’ll learn to use Python to develop Lambda functions that interact with key AWS services such as API Gateway, SQS, and CloudWatch functions. You’ll also learn how a new cloud development environment called Cloud9 can make it easier to write, debug, and deploy AWS Lambda functions.

What will you learn

  • Running Data Processing Tasks on AWS
  • Development with Cloud9
  • Writing AWS Lambda Functions Using Python
  • Implementation of cloud data processing patterns, i.e. serverless.
  • Design event-driven architectures on the AWS platform using SQS, Python Lambda and other AWS technologies.

This course is suitable for people who:

  • Are you an aspiring data engineer using Python?
  • You work with data and want to learn cloud data design patterns.
  • You’re new to the AWS cloud and want to write serverless functions in Python.
  • Are you a data scientist looking for an easier way to get your data results?
  • Want to learn about serverless technology and how to implement it in Python?

Course details

Course headings

  • Introduction
    1. Data Engineering with Python and AWS Lambda LiveLessons: Introduction
  • Lesson 1: Getting Started with AWS Lambda
    1. 1.1 Create a Hello World AWS Lambda function in the console
    2. 1.2 Learn Basic Lambda Patterns
    3. 1.3 Explore the Lambda Management Console
    4. 1.4 Load external code into AWS Lambda
  • Lesson 2: Using Cloud9 to Develop Python Lambda Functions
    1. 2.1 Setting up Cloud9
    2. 2.2 Development with Cloud9
    3. 2.3 Launching Cloud9 and setting up workspace
    4. 2.4 Importing lambda functions
    5. 2.5 Calling lambda functions
    6. 2.6 Calling Lambda functions inside an API gateway
    7. 2.7. Deploying a lambda function
  • Lesson 3: Creating temporary lambda functions
    1. 3.1. Using AWS Lambda with Cloudwatch Events
    2. 3.2. Using AWS Lambda to Populate AWS SQS
    3. 3.3. Using AWS Cloudwatch Logging with AWS Lambda
  • Lesson 4: Creating Event-Driven Lambdas
    1. 4.1 Creating a producer lambda function
    2. 4.2 Enabling the SQS trigger
    3. 4.3 Serverless data processing architecture
  • Lesson 5: Learn SAM Local
    1. 5.1 Installation of SAM Local
    2. 5.2. Use SAM Local to call functions locally.
    3. 5.3. Using SAM to package and deploy Lambda
    4. 5.4. Using SAM with IAM
    5. 5.5 Using SAM Lambda Environment Variables
  • Lesson 6: Learn AWS Glue
    1. 6.1 What is AWS glue?
    2. 6.2. Using AWS Glue
  • Lesson 7: Creating State Machines Using Step Functions
    1. 7.1 Learn step by step functions
    2. 7.2. Use Amazon State Language
    3. 7.3 Demonstration of step-by-step functions
  • Lesson 8: Using Step Functions with AWS Services
    1. 8.1 Explore integration with other AWS products
    2. 8.2 Using DynamoDB with step functions
    3. 8.3 Using AWS ECS/Fargate with Stepping Functions
    4. 8.4. Using the AWS Callback Pattern
  • Lesson 9: Serverless Relational Databases
    1. 9.1 Serverless relational databases
    2. 9.2 Using serverless version of Aurora
    3. 9.3 Using the Data API for Aurora Serverless
    4. 9.4. Using Stored Procedures to Call Lambda
  • Lesson 10: Creating an API using API Gateway
    1. 10.1 Using the API Gateway
    2. 10.2 Lambda and API Gateway integration best practices
  • Lesson 11: API Authentication with AWS Cognito
    1. 11.1 Start authentication
    2. 11.2 Using Cognito user pools
    3. 11.3. Using Cognito Authentication with API Gateway
    4. 11.4. Using Federated Identity
  • Lesson 12: Using Serverless Data Warehousing
    1. 12.1 Using DynamoDB for data processing
    2. 12.2 Using Amazon Athena to Process Data
    3. 12.3. Using Amazon EMR to Process Data
    4. 12.4. Using Amazon EFS to Process Data
  • Lesson 13: Building Serverless Business Intelligence and AutoML
    1. 13.1 Amazon Fast Site Integration
    2. 13.2 Lambda integration with AI API
    3. 13.3 Lambda integration with Sagemaker
  • Lesson 14: Building Serverless Data Streaming
    1. 14.1 Using Kinesis Streams
    2. 14.2 Using computer vision streams
  • Lesson 15: Case Studies
    1. 15.1 Compare AWS Lambda to Google Cloud Functions
    2. 15.2 Using GCP Cloud Functions with Pub Sub + Cloud Scheduler
    3. 15.3 Use of the Chalice Platform
    4. 15.4 Push and pull architecture
    5. 15.5 DevOps principles
    6. 15.6 Principles of cloud computing
    7. 15.7 Brief introduction to serverless computing
    8. 15.8 Managing Packages in AWS Lambda
    9. 15.9 Multi-cloud solutions
  • Lesson 16: Course Summary
    1. 16.1 Course summary

Course Prerequisites

  • Can write Python functions and execute instructions.
  • Have a basic understanding of AWS

Data Engineering Images with Python and the AWS Lambda LiveLessons Course

Data Engineering with Python and AWS Lambda LiveLessons

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