Apache Beam - A Hands-On course to build Big data Pipelines

Apache Beam | A Hands-On course to build Big data Pipelines, available at $84.99, has an average rating of 4.56, with 62 lectures, 3 quizzes, based on 1818 reviews, and has 11408 subscribers.

You will learn about Learn Apache Beam – A portable programming model whose pipelines can be deployed on Spark, Flink, GCP (Google Cloud Dataflow) etc. Understand the working of each and every component of Apache Beam with HANDS-ON examples. Learn Apache Beam fundamentals including its Architecture, Programming model, Pcollections, Pipelines etc. Multiple PTransforms to Read, Transform and Write the processed data. Advance concepts of Windowing, Triggers, Watermarks, Late elements, Type Hints and many more. Load data to Google BigQuery Tables from Apache Beam pipeline. Build Real-Time businesss Big data processing pipelines using Apache Beam. Data-sets and Beam codes used in lectures are available in resources tab. This course is ideal for individuals who are Students who want to learn Apache Beam from scratch to its Live Project Implementation. or Data engineers who want to build Unified & Portable Big data processing pipelines. or Developers who want to learn a futuristic programming model for Big data processing. It is particularly useful for Students who want to learn Apache Beam from scratch to its Live Project Implementation. or Data engineers who want to build Unified & Portable Big data processing pipelines. or Developers who want to learn a futuristic programming model for Big data processing.

Enroll now: Apache Beam | A Hands-On course to build Big data Pipelines

Summary

Title: Apache Beam | A Hands-On course to build Big data Pipelines

Price: $84.99

Average Rating: 4.56

Number of Lectures: 62

Number of Quizzes: 3

Number of Published Lectures: 62

Number of Published Quizzes: 3

Number of Curriculum Items: 66

Number of Published Curriculum Objects: 66

Original Price: $39.99

Quality Status: approved

Status: Live

What You Will Learn

Learn Apache Beam – A portable programming model whose pipelines can be deployed on Spark, Flink, GCP (Google Cloud Dataflow) etc.

Understand the working of each and every component of Apache Beam with HANDS-ON examples.

Learn Apache Beam fundamentals including its Architecture, Programming model, Pcollections, Pipelines etc.

Multiple PTransforms to Read, Transform and Write the processed data.

Advance concepts of Windowing, Triggers, Watermarks, Late elements, Type Hints and many more.

Load data to Google BigQuery Tables from Apache Beam pipeline.

Build Real-Time businesss Big data processing pipelines using Apache Beam.

Data-sets and Beam codes used in lectures are available in resources tab.

Who Should Attend

Students who want to learn Apache Beam from scratch to its Live Project Implementation.

Data engineers who want to build Unified & Portable Big data processing pipelines.

Developers who want to learn a futuristic programming model for Big data processing.

Target Audiences

Students who want to learn Apache Beam from scratch to its Live Project Implementation.

Data engineers who want to build Unified & Portable Big data processing pipelines.

Developers who want to learn a futuristic programming model for Big data processing.

Apache Beam is a unified and portable programming model for both Batch and Streaming data use cases.

Earlier we could run Spark, Flink & Cloud Dataflow Jobs only on their respective clusters. But now Apache Beam has come up with a portable programming model where we can build language agnostic Big data pipelines and run it using any Big data engine (Apache Spark, Flink or in Google Cloud Platform using its Cloud Dataflow and many more Big data engines).

Apache Beam is the future of building Big data processing pipelines and is going to be accepted by mass companies due to its portability. Many big companies have even started deploying Beam pipelines in their production servers.

What’s included in the course ?

Complete Apache Beam concepts explained from Scratch to Real-Time implementation.

Each and every Apache Beam concept is explained with proper HANDS-ONexamples of it.

Include even those concepts, the explanation to which is not very clear anywhere online.

Type Hints, Encoding & Decoding, Watermarks, Windows, Triggers and many more.

Build 2 Real-time Big data case studies using Apache Beam programming model.

Load processed data to Google Cloud BigQuery Tables from Apache Beam pipeline via Dataflow.

Codes and Datasets used in lectures are attached in the course for your convenience.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction to Apache Beam

Lecture 2: Evolution of Big data Frameworks

Lecture 3: Architecture of Apache Beam

Lecture 4: Flow of Beams Programming Model

Lecture 5: Basic Terminologies in Beam

Lecture 6: Installation

Chapter 2: Transformations in Beam

Lecture 1: Structure of a Beam Pipeline

Lecture 2: Various Read Transforms in Beam

Lecture 3: Create Transform

Lecture 4: Various Write Transforms in Beam

Lecture 5: Map, FlatMap & Filter – Part 1

Lecture 6: Map, FlatMap & Filter – Part 2

Lecture 7: Branching Pipelines

Lecture 8: ParDo Transform

Lecture 9: Advanced Combiner of Beam

Lecture 10: Create Composite Transforms

Lecture 11: CoGroupBy for Joins

Lecture 12: How to access files from Google Drive

Chapter 3: Side Inputs and Outputs

Lecture 1: Side Inputs

Lecture 2: Additional Outputs in Pipeline

Chapter 4: Case Study – Identify Banks Defaulter Customers

Lecture 1: Introduction to Case Study

Lecture 2: Requirements and Data walk-through for Card skippers

Lecture 3: Identifying Credit card payment skippers

Lecture 4: Requirements and Data walk-through for Loan Defaulters

Lecture 5: Identifying Loan Defaulters – Part 1

Lecture 6: Identifying Loan Defaulters – Part 2

Chapter 5: Data encoding & decoding

Lecture 1: Data encoding in Beam

Lecture 2: Coder class in Beam

Chapter 6: Type Hints in Beam

Lecture 1: What is Type Safety and How Beam ensures it

Lecture 2: Using Type Hints

Chapter 7: Build Streaming data Pipelines

Lecture 1: Introduction to Streaming

Lecture 2: PubSub Streaming Architecture

Lecture 3: Beam connection with Google Cloud

Lecture 4: Setting up GCPs PubSub Project

Lecture 5: Run a Demo streaming pipeline on GCP

Chapter 8: Implement Windows in Apache Beam

Lecture 1: Introduction to Windows in Beam

Lecture 2: Mobile Game Example

Lecture 3: Time Notions in Streaming Frameworks

Lecture 4: What are Tumbling & Sliding Windows

Lecture 5: Implementing Tumbling Windows

Lecture 6: Recommendation for Windowing

Lecture 7: Implementing Sliding Windows

Lecture 8: Session Windows & its implementation

Lecture 9: Global Windows & its implementation

Chapter 9: Watermarks in Streaming environment

Lecture 1: What is Watermark

Chapter 10: Triggers and its Implementation

Lecture 1: How Beam handles late elements using Triggers

Lecture 2: Types of Triggers & their implementation

Lecture 3: Composite Triggers

Chapter 11: Case Study – Mobile Game Analysis

Lecture 1: Requirements & Code walk-through

Lecture 2: Pipeline for incrementing scores

Lecture 3: Pipeline to identify Players skilled weapon

Chapter 12: Deploy Beam pipeline on Google Cloud Dataflow

Lecture 1: Create Pipeline with Options

Lecture 2: Deploy it on GCP (Dataflow)

Chapter 13: Write to BigQuery Tables

Lecture 1: Introduction

Lecture 2: Create BigQuery Dataset

Lecture 3: Create and Load BigQuery Table

Lecture 4: View loaded Table in BigQuery

Chapter 14: Additional Learnings

Lecture 1: Batch Vs Stream Processing

Lecture 2: Flink Vs Spark

Lecture 3: GCP Big data ecosystem

Lecture 4: ThankYou

Chapter 15: Bonus

Lecture 1: Bonus

Instructors

J Garg – Real Time Learning
Data Engineering, Analytics and Cloud Trainer

Rating Distribution

1 stars: 23 votes

2 stars: 28 votes

3 stars: 152 votes

4 stars: 669 votes

5 stars: 945 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!