OReilly – Data Engineering with Python and AWS Lambda

OReilly – Data Engineering with Python and AWS Lambda
English | Tutorial | Size: 1.62 GB

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 Pragmatic AI: An Introduction to Cloud-Based Machine Learning
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

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
Download the project files: GitHub Repo

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

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