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Ultimate Guide to Deploying Flask to AWS

In this course you will learn how to deploy Python based Flask applications to the AWS Cloud using both traditional EC2 Virtual Private Servers (VPS) along with the ever increasingly popular Serverless method using Fully Managed AWS service offerings.  In the sections on traditional Server based EC2 deployments I present professional grade, open source, and battle tested technologies such as Nginx web server, uWSGI Python application server, and PostgreSQL database.  For server based deployment methods I cover a variety of architectures from monolithic to distributed N-tier approaches. For Serverless deployments I utilize the AWS Serverless Application Model (SAM) with AWS API Gateway, AWS Lambda and AWS DynamoDB managed services to build a microservices like REST API.  This course covers the most common, industry leading, methods of architecting and deploying Python based Flask applications utilizing the AWS Cloud in ways that are fault tolerant, cost effective, and scalable.

* Apply code AWSFLASK_TCI for over 80% off during August

Apache Flink Programming using Table API and SQL

In this course students learn to harness the power of Apache Flink, a modern distributed computing framework, providing a unified approach to both batch and streaming data processing workloads. This course focuses on the relational programming paradigm exposed through the Table API and SQL interface with Python offering intuitive yet powerful abstractions to process vast amounts of data from bounded (batch) or unbounded (streaming) sources.

  • Learn batch processing through examples of consuming, processing, and producing results from/to the filesystem in CSV format
  • Learn stream processing with examples consuming, processing and producing results from/to Apache Kafka running in a local Dockerized Kafka cluster

* Apply code PYFLINK_TCI for over 80% off during August

Ultimate Guide to Data Streaming with AWS Kinesis

In this course students learn to harness the power of Kinesis Data Streams (KDS) and Kinesis Data Firehose (KDF) to construct high-throughput, low latency, pipelines of data across a variety of architectural components leading to scalable and loosely coupled systems. Additional focus is placed on how these stream persistence technologies are used in conjunction with Kinesis Data Analytics to perform advanced, real-time, computations which drive informed business actions and insights.

  • Scalable real-time big data analytics using fully managed infrastructure
  • Dozens of code examples and walk throughs in both Java and Python

* Apply code KINESIS_TCI for over 80% off during August