Data Engineering with Python and AWS Lambda 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.
Learn Python Programming - Everything You Need to Know
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Learn Python Programming - Full Course from Harvard University
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AWS Lambda vs. Azure Functions vs. Google Functions
☞ morioh.com/p/267c15fd2761
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 tutorial, 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
#Python #AWS #Morioh
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