Faust: Stream Processing Using Models, Agents, & Channels

IN THIS COURSE

• 1.  Course Overview
  2m 21s
  In this video, you’ll learn more about the course and your instructor. In this course, you’ll learn how to represent the individual elements of a stream using Faust models. Models allow you to specify fields and their data types, and then access your streaming data using this data structure. Next, you will learn how to work with agents. FREE ACCESS
• 2.  Using Faust Models for Stream Elements
  12m 25s
  In this video, you’ll learn more about Faust Stream Processing. In this demo, you’ll take a look at your code. In the folder you’re working in, you have your current working directory. There’s a subfolder called Models, and under Models, there’s a Python script that contains your Faust application. We’ll take a look at this code. Notice the import Faust on line 1, and on line 4 you instantiate your Faust application. FREE ACCESS
• 3.  Specifying Multiple Model Fields in Faust
  5m 48s
  In this video, you’ll watch a demo. In this demo, you’ll continue working with models to represent the data that you’ve read from a Kafka topic. The automatic deserialization of Kafka messages provided when you use these model objects is useful. You’ll learn that when you're working with stream processing using Faust, it's common to represent your input data using models. Next, you’ll take a look at the changes you've introduced in this code. FREE ACCESS
• 4.  Programmatically Publishing Messages to Kafka
  8m 14s
  In this video, you’ll watch a demo. You’ll take records from the input stream and print the details to the Console Window. The main change you’ll make is you’ll publish messages to your Kafka topic using Python code. Instead of manually publishing messages using a Console producer, you'll write Python code to publish messages you read in from a CSV file. To publish messages to a Kafka topic, you'll use the pykafka client. FREE ACCESS
• 5.  Invoking the ask() Method on Faust Agents
  12m 13s
  In this video, you’ll watch a demo. You'll learn about agents in Faust in more detail, and learn how these agents work. First, you’ll look at the code onscreen. You’ll use the working directory under the Skillsoft folder is Agents. Under the Agent subfolder, you’ll find two files you'll be working with. There is loans.py, which contains the code for your stream processing application, and send_loan_records.py. Next, you’ll explore the stream processing code you’ve written. FREE ACCESS
• 6.  Invoking the cast() Method on Faust Agents
  4m 32s
  In this video, you’ll watch a demo. You'll explore another method available on our agents. You’ll once again work with loans.py. You’ll import Faust, set up the LoanRecord model, and you’ll see the fields present in every LoanRecord. This LoanRecord inherits from the faust.Record base class. Onscreen you’ll see the Faust application has been instantiated using faust.App. The topic our agent will listen to is the loan-details topic. The app you’ll use is app.topic. FREE ACCESS
• 7.  Using Multiple Faust Agents for Chained Processing
  12m 59s
  In this video, you’ll watch a demo. You’ll define multiple agents. You’ll learn you can have multiple agent definitions within the same Python script. You’ll see you can have as many agents as needed to process your code across your Faust applications. You’ll continue using the class LoanRecord. You’ve instantiated the Faust application, and you've set up the loan details topic. This time, there are two app agent specifications and two decorators decorating two functions.  FREE ACCESS
• 8.  Using Multiple Faust Agents for Multiple Topics
  4m 56s
  In this video, you’ll watch a demo. In this demo, you’ll see space characters are removed from this gender field. Following along onscreen, you’ll see the applicant for this loan has the gender female. Next, you’ll print the details of the loan. You’ll print the loan type. Then, you’ll look at the second agent where we process loan records by income and credit score. You’ll run an async for loop to iterate every record. FREE ACCESS
• 9.  Using Faust Channels to Send and Receive Messages
  9m 45s
  In this video, you’ll watch a demo. In this demo, you’ll learn the channel construct in Faust Streaming. You’ll access the top-level folder Skillsoft and the Channels subfolder. Within Channels you’ll find the data_loan.csv file and you’ll continue working with the same dataset. This demo will focus on channels. You’ll learn that a channel in Faust is a special construct that Faust uses to send and receive messages. FREE ACCESS
• 10.  Manually Creating Streams in Faust
  8m 1s
  In this video, you’ll watch a demo. You’ll explore streams. You’ll learn a stream can be thought of as an infinite async iterable consuming messages from a channel. The channel you’ve used so far has been a Kafka topic, because Faust streaming has built-in support for the Kafka topic. You’ll learn streams can read from any kind of channel. A stream is essentially an infinite source of streaming events processed with code. FREE ACCESS
• 11.  Defining Stream Processors in Faust
  8m 6s
  In this video, you’ll watch a demo. You’ll learn that when you're working with events or messages from an input stream, you’ll want to pre-process those streams before you send them along to agents for actual processing. You’ll need to format the fields that already exist in the records that you process. In this demo, you'll see how to specify pre-processors for the records in our input stream when you manually create a stream. FREE ACCESS
• 12.  Course Summary
  2m 24s
  In this video, you’ll summarize what you’ve learned in the course. You explored the features available in Faust to enable stream processing. You learned that models describe the fields of the data structures that can be used to represent streaming elements, in the form of key-value pairs. You learned Faust deserializes JSON strings in the input stream to represent elements using the models we’ve defined. You installed the PyKafka library on your local machine. FREE ACCESS