Spark 3 on Google Cloud Platform-Beginner to Advanced Level

سرفصل های دوره

Build Scalable Batch and Real Time Data Processing Pipelines with PySpark and Dataproc

1. Introduction

1. Course Introduction and Overview

2. GitHub repository for the course.html

3. Setup a Trial GCP Account

4. Install and Setup the Gcloud SDK

2. Getting Started with Spark Fundamentals

1. Introduction to Dataproc on GCP

2. Overview of Sparks Architecture

3. Datalake vs Datawarehouse

4. Role of Spark in Big Data Ecosystem

5. Overview of Spark APIs

6. Whats new in Spark3

7. Should i be learning Spark in 2023

3. Getting started with Spark DataFrame API

1.1 DataframeAPI-Source-Code.zip

1. Section Introduction

2. Lab - Create a Dataproc Cluster

3. Lab - Walkthrough of Jupyter Notebook and different components

4. Lab- Basic Dataframe Operations in PySpark

5. Lab - Typecasting & timestamp column extraction

6. Labs - Dataframe Aggregations

7. Assignment on Dataframe Aggregations.html

8. Transformations and Actions in Spark

9. Lab - Advanced transformations using Window Functions

10. Lab - Rolling Window Operations

11. Lab - Write transformed data back to a sink GCS Bucket and BigQuery

12. Lab - Use Spark-Submit to submit jobs to dataproc clusters

4. Getting started with SparkSql in Spark3

1.1 Data-For-Joins.zip

1.2 SparkSql-Source-Code.zip

1. Introduction to SparkSql

2. Different Types of Tables in Spark

3. Lab - Create Tables for SparkSql

4. Lab - Analytical Window Functions and creating permanent tables

5.1 Data-For-Joins.zip

5. Lab - Perform Joins on Dataframes

6. What are Partitions in Spark Dataframes

7. Lab - Perform repartitioning of dataframes

8. Data Shuffling in Joins

9. Lab - User defined functions in Spark

5. Spark Concepts - Autoscaling , Optimization and Alerting

1. What is a catalyst optimizer in spark

2. Cache and Persist in Spark

3. What is Autoscaling in spark and dataproc

4. Lab - Apply Autoscaling Policies to Dataproc Clusters

5. Introduction to Dataproc Workflows

6. Lab - Execute GCP Workflows

7. Lab - Cloud Scheduler to automate Workflow Execution

8. What is Checkpointing in Spark

9. What are Broadcast Joins

10. Lab - Setup Alerting Policies for Spark Jobs

6. Project - End to End Batch processing pipeline using Spark

1.1 Project-Source-Code.zip

1. Project Introduction

2. Lab - Setup MySql Instance and Database on GCP

3. Lab - Ingest Data into MySql

4. Lab - Setup Dataproc with initialization actions

5. Assignment Lab - Setup Connectivity from PySpark to MySql Db

6. Assignment Lab - Perform transformations using PySpark

7. Lab - Setup Workflows to execute end-to-end pipeline

7. Real Time Analytics With Spark Structured Streaming

1.1 Section-Source-Code.zip

1. Section Introduction

2. Overview of PusSub Lite

3. What are Tumbling Windows

4. What is Watermarking

5. What are Sliding Windows

6. Lab - Create PubSub Lite Reservation

7. Lab - Publish Data to PubSub and Testing using PySpark

8. Lab - Implement Tumbling Windows

9. Lab -Implement Tumbling Window with Watermarking

10. Lab- Implement Sliding Windows

8. Joins on Streaming Data

1.1 Section-Source-Code.zip

1. Overview of Joining Streaming Dataframe

2. Lab -Join Streaming Dataframe with Static Dataframe

3. Lab - Join 2 Streaming Dataframes

4. Lab - Use Watermarking in Streaming Joins

9. Real Time Collaborative Filtering Project

1.1 Project-Source-Code.zip

1. Overview of the Use Case

2. Lab - Model Training using ML Library and Code Walkthrough

3. Lab - Code Walkthrough and Publish Data

4. Lab - Real Time Product Recommendation Model in Action

10. Prep Up for the Interview Questions on Spark

1. Introduction and Tips

2. Batch Data Processing Interview Questions - Part 1

3. Batch Data Processing Interview Questions - Part 2

4. Batch Processing Interview Questions - Part 3

5. Real Time Data Processing Interview Questions - Part 1

6. Real Time Data Processing Interview Questions - Part 2

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تولید کننده: Udemy-Training