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 …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.Description
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.
About the Instructors
Noah Gift is a lecturer and consultant at both the UC Davis Graduate School of Management MSBA program and the Graduate Data Science program, MSDS, at Northwestern. He is teaching and designing graduate Machine Learning, AI, and Data Science courses, and consulting on Machine Learning and Cloud Architecture for students and faculty, including leading a multi-cloud certification initiative for students. Noah is a Python Software Foundation Fellow, AWS Subject Matter Expert (SME) on Machine Learning, AWS Certified Solutions Architect and AWS Academy Accredited Instructor, Google Certified Professional Cloud Architect, and Microsoft MTA on Python. Noah has published close to 100 technical publications, including two books on subjects ranging from Cloud Machine Learning to DevOps. Gift received an MBA from UC Davis, an M.S. in Computer Information Systems from Cal State Los Angeles, and a B.S. in Nutritional Science from Cal Poly San Luis Obispo. Currently, he is consulting startups and other companies on Machine Learning, Cloud Architecture, and CTO level consulting as the founder of Pragmatic AI Labs. His most recent book is Pragmatic AI: An Introduction to Cloud-Based Machine Learning (Pearson, 2018).Robert Jordan is a visionary architect with more than 20 years of experience designing, implementing, and deploying production applications for some of the world’s largest media and scientific customers. He has successfully led projects on all major cloud platforms and is currently certified on both AWS and GCP platforms.
Kennedy Behrman is a veteran consultant specializing in architecting and implementing cloud solutions for early-stage startups. He is experienced in data engineering, data science, AWS solutions, and engineering management, and has acted as a technical editor on a number of Python and data science-related publications. He has experience developing a training curriculum used in international economic development and more than a decade of hands-on Python experience. Kennedy has recently acted as both a content specialist for AWS Machine Learning certification development and as a technical editor for the book Pragmatic AI: An introduction to Cloud-Based Machine Learning (Pearson, 2018). He is also a founder of Pragmatic AI Labs.
What You Will Learn
Download the project files: GitHub Repo
Who Should Take This Course
Course Requirements
About Pearson Video Training
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Introduction
Data Engineering with Python and AWS Lambda LiveLessons: Introduction
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 Local
5.2 Use SAM Local to invoke functions locally
5.3 Use SAM to package and deploy Lambda
5.4 Use SAM with IAM
5.5 Use SAM Lambda environment variables
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