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Data Engineering with Python and AWS Lambda LiveLessons (Video Training)

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  • Copyright 2020
  • Edition: 1st
  • Online Video
  • ISBN-10: 0-13-596429-6
  • ISBN-13: 978-0-13-596429-3

7 Hours of Video Instruction

Data Engineering with Python and AWS Lambda LiveLessons shows users how to build complete and powerful data engineering pipelines in the same language that Data Scientists use to build Machine Learning models. By embracing serverless data engineering in Python, you can build highly scalable distributed systems on the back of the AWS backplane. Users learn to think in the new paradigm of serverless, which means to embrace events and event-driven programs that replace expensive and complicated servers.

Some of the many benefits of programming with AWS Lambda in Python include no servers to manage, continuous scaling, and subsecond metering. Several use cases include data processing, stream processing, IoT backends, mobile, and web applications. Learn to take advantage of a new paradigm in software architecture that will make your code easier to write, maintain, and deploy.
AWS Lambda functions are the building blocks for creating sophisticated applications and services on AWS. In this LiveLesson, you learn to use Python to develop Lambda functions that communicate with key AWS services: API Gateway, SQS, and CloudWatch functions. You also learn how a new cloud-based development environment, Cloud9, can streamline writing, debugging, and deploying AWS Lambda functions.
What You Will Learn

  • Performing Data Engineering tasks on AWS
  • Developing with Cloud9
  • Writing AWS Lambda functions in Python
  • Implementing cloud-native Data Engineering patterns, i.e. serverless
  • Architecting event-driven architectures on the AWS platform using SQS, Python Lambda, and other AWS technologies

Who Should Take This Course
  • You are an aspiring data engineer using Python
  • You work with data and want to learn cloud-native data engineering patterns
  • You are new to the AWS Cloud and want to write functions in Python that do not require servers
  • You are a data scientist who needs a simpler way to get data engineering results
  • You want to learn about serverless technology and how to accomplish it in Python

Course Requirements
  • Can write functions in Python and execute statements
  • Have a basic understanding of AWS

About Pearson Video Training

Pearson publishes expert-led video tutorials covering a wide selection of technology topics designed to teach you the skills you need to succeed. These professional and personal technology videos feature world-leading author instructors published by your trusted technology brands: Addison-Wesley, Cisco Press, Pearson IT Certification, Prentice Hall, Sams, and Que. Topics include IT Certification, Network Security, Cisco Technology, Programming, Web Development, Mobile Development, and more.  Learn more about Pearson Video training at  http://www.informit.com/video.

Video Lessons are available for download for offline viewing within the streaming format. Look for the green arrow in each lesson.



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Table of Contents


Lesson 1: Get Started with AWS Lambda
1.1 Create a Hello World AWS Lambda function in the console
1.2 Learn basic Lambda patterns
1.3 Learn Lambda Management console
1.4 Upload external code to AWS Lambda

Lesson 2: Use Cloud9 to Develop Python Lambda Functions
2.1 Set up Cloud9
2.2 Develop with Cloud9
2.3 Launch Cloud9 and workspace configuration
2.4. Import Lambda functions
2.5 Invoke Lambda functions
2.6. Invoke Lambda functions inside API Gateway
2.7 Deploy a Lambda function

Lesson 3: Create Timed Lambda Functions
3.1 Use AWS Lambda with CloudWatch events
3.2 Use AWS Lambda to populate AWS SQS
3.3 Use AWS CloudWatch logging with AWS Lambda

Lesson 4: Create Event-Driven Lambdas
4.1 Create a Producer Lambda function
4.2 Enable SQS Trigger
4.3 Serverless data engineering architecture

Lesson 5: Learn SAM Local
5.1 Install SAM
5.2 Use SAM application files and structure
5.3 Use SAM CLI to invoke functions locally
5.4 Use SAM to test and debug a Lambda function locally
5.5 Use SAM to package and deploy Lambda

Lesson 6: Learn AWS Glue
6.1 What is AWS Glue?
6.2 Use AWS Glue

Lesson 7: Create State Machines with Step Functions
7.1 Learn step functions
7.2 Use Amazon States Language
7.3 Step functions demo

Lesson 8: Use Step Functions with AWS Services
8.1 Learn integration with other AWS products
8.2 Use DynamoDB with step functions
8.3 Use AWS ECS/Fargate with step functions
8.4 Use AWS Callback Pattern

Lesson 9: Serverless Relational Databases
9.1 Serverless relational databases
9.2 Use Aurora Serverless
9.3 Use Data API for Aurora Serverless
9.4 Use stored procedures to invoke Lambda

Lesson 10: Build APIs with API Gateway
10.1 Use API Gateway
10.2 Integrate Lambda and API Gateway best practices

Lesson 11: Authenticate APIs with AWS Cognito
11.1 Begin Cognito authentication
11.2 Use Cognito User Pools
11.3 Use Cognito authentication with API Gateway
11.4 Use Federated Identity

Lesson 12: Use Serverless Datastores
12.1 Use DynamoDB for data engineering
12.2 Use Amazon Athena for data engineering
12.3 Use Amazon EMR for data engineering
12.4 Use Amazon EFS for data engineering

Lesson 13: Create Serverless Business Intelligence and AutoML
13.1 Integrate Amazon QuickSite
13.2 Integrate Lambda with AI APIs
13.3 Integrate Lambda with SageMaker

Lesson 14: Create Serverless Data Streaming
14.1 Use Kinesis Streams
14.2 Use Computer Vision Streams

Lesson 15: Case Studies
15.1 Compare AWS Lambda with Google Cloud Functions
15.2 Use GCP Cloud Functions with Pub Sub + Cloud Scheduler
15.3 Use Chalice framework
15.4 Push Versus Pull Architecture
15.5 Principles of DevOps
15.6 Principles of cloud computing
15.7 Summary of serverless computing
15.8 Managing Packages in AWS Lambda
15.9 Multi-cloud solutions

Lesson 16: Course Summary
16.1 Course summary



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