Big Data with Apache Spark 3 and Python: From Zero to Expert

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Data Bootcamp

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1. Spark Fundamentals

1. How to get the most out of this course.html

2.1 Big Data with Apache Spark 3 and Python From Zero to Expert.pdf

2.2 Entrega.rar

2. Course material.html

3. Spark Fundamentals.mp4

01:37

4. Apache Spark execution.mp4

01:57

5. Apache Spark ecosystem and documentation.mp4

04:59

6. PySpark operation, cluster administration and architecture.mp4

03:38

2. Installing Apache Spark locally

1. Download Spark, Java and Anaconda.mp4

03:19

2. Setting environment variables.mp4

03:32

3. Running Spark in Prompt and Jupyter Notebook.mp4

03:12

4. Fixing common problems.html

3. Basic Features and RDDs

1.1 PySpark RDDs.pdf

1. PySpark Cheat Sheet.html

2. RDD Fundamentals.mp4

03:19

3. Initialize PySpark with SparkSession and the SparkContext.mp4

04:06

4. Transformations in RDDs like map, filter, flatMap and distinct.mp4

02:25

5. Transformations in RDDs like reduceByKey, groupByKey or sortByKey.mp4

04:36

6. RDD actions such as count, first, collect or take.mp4

02:43

4. Spark DataFrames and Apache Spark SQL

1.1 PySpark Sql Basics Cheat Sheet.pdf

1. PySpark Cheatsheet SQL.html

2. Fundamentals and advantages of DataFrames.mp4

03:27

3. Characteristics of DataFrames and data sources.mp4

02:24

4. Creating DataFrames in PySpark.mp4

03:07

5. Operations with PySpark DataFrames.mp4

04:42

6. Different types of joins in DataFrames.mp4

02:43

7. SQL queries in PySpark.mp4

02:26

8. Advanced features for loading and exporting data in PySpark.mp4

03:35

5. Advanced features in Apache Spark

1. Funciones avanzadas y optimizacion del rendimiento.mp4

03:39

2. BroadCast Join and caching.mp4

04:49

3. User Defined Functions (UDF) and advanced SQL functions.mp4

04:36

4. Handling and imputation of missing values.mp4

02:51

5. Partitioning and catalog of APIs.mp4

04:32

6. Practical Exercise Advanced Analytics with Apache Spark.html

6. Advanced Analytics with Apache Spark

1. Introduction to advanced analytics with Spark.mp4

02:15

2. Data loading and data schema modification.mp4

04:03

3. Inspect data in PySpark.mp4

02:46

4. Column transformation in PySpark.mp4

01:58

5. Advanced missing data imputation in PySpark.mp4

05:32

6. Data selection with PySpark and PySpark SQL.mp4

06:23

7. Data visualization and graph generation in PySpark.mp4

05:17

8. Persist data with PySpark.mp4

01:50

7. Kolas The Apache Spark Pandas API

1. Spark Koalas Fundamentals.mp4

02:39

2. Feature Engineering with Koalas.mp4

03:51

3. Creating DataFrames with Koalas.mp4

04:10

4. Data manipulation and DataFrames with Koalas.mp4

02:08

5. Working with missing data in Koalas.mp4

02:24

6. Data visualization and graph generation with Koalas.mp4

03:07

7. Importing and exporting data with Koalas.mp4

01:51

8. Hands-on exercise with Koalas.html

8. Machine Learning with Apache Spark

1. Fundamentals of Machine Learning with Spark.mp4

03:04

2. Spark Machine Learning Components.mp4

02:08

3. Stages of developing a Machine Learning model.mp4

05:22

4. Import data and exploratory data analysis (EDA).mp4

08:58

5. Data preprocessing with PySpark.mp4

05:04

6. Training the machine learning model in PySpark.mp4

03:57

7. Evaluation of the Machine Learning model.mp4

02:28

9. Spark Streaming

1. Practical example of counting words with Spark Streaming.mp4

04:18

2. Spark Streaming Configurations Output Modes and Operation Types.mp4

03:05

3. Time Window Operations in Spark Streaming.mp4

03:54

4. Spark Streaming Capabilities.mp4

01:20

5. Use case Real-time bank fraud detection (Part I).mp4

03:56

6. Use case Real-time bank fraud detection (Part II).mp4

04:55

7. Spark Streaming Exercise.html

10. Introduction to Databricks

1. Introduction to Databricks.mp4

03:33

2. Databricks Terminology and Databricks Community.mp4

04:03

3. Delta Lake.mp4

01:55

4. Create a free Databricks account.mp4

01:57

11. Apache Spark on Databricks

1. Introduction to the Databricks environment.mp4

10:15

2. Getting started with Databricks.mp4

07:30

3. Creating and saving DataFrames in Databricks.mp4

04:40

4. Data transformation and visualization in Databricks.mp4

05:51

5. Use case Population data analytics.mp4

07:50

12. Machine Learning in Databricks

1. Import and exploratory analysis of the data.mp4

04:33

2. Variable preprocessing with PySpark and Databricks.mp4

05:21

3. Definition of the Machine Learning model and development of the Pipeline.mp4

04:09

4. Model evaluation with PySpark and Databricks.mp4

04:28

5. Hyperparameter tuning and registration in MLFlow.mp4

03:19

6. Predictions with new data and visualization of the results.mp4

03:29

13. Additional material

1.1 PySpark_Cheat_Sheet_Python.pdf

1. Additional Resources Complete Guide to Spark.html

Description

Complete bootcamp to learn PySpark, Databricks, Spark Machine Learning, Advanced Analytics, Koalas and Spark Streaming

What You'll Learn?

Introduction to Big Data and Apache Spark Fundamentals
Spark RDDs, Dataframes and Spark Koalas
Machine Learning with Spark
Advanced features with Apache Spark
Advanced analytics and data visualization toold
Spark in cloud with Azure and Databricks
Spark Streaming and GraphX
Databricks
Machine learning in Databricks

Who is this for?

Anyone who wants to learn advanced big data skills

Anyone who knows Python and wants to adquire Big Data processing skills

Anyone that want to make a career as a data engineer, data analyst or data scientist

Anyone interested in learning Apache Spark and Pyspark for Big Data analysis

Anyone that want to learn cutting-edge technology in Big Data

More details

Description
If you are looking for a hands-on, complete and advanced course to learn Big Data with Apache Spark and Python, you have come to the right place.

This course is designed to cover the complete skillset of Apache Spark, from RDDs, Spark SQL, Dataframes, and Spark Streaming, to Machine Learning with Spark ML, Advanced Analytics, data visualization, Spark Koalas, and Databricks.

With lessons, downloadable study guides, hands-on exercises, and real-world use cases, this is the only course you'll need to learn Apache Spark.
Apache Spark has become the reference tool for Big Data, surpassing Hadoop MapReduce. Spark works up to 100 times faster than Hadoop MapReduce and has a complete ecosystem of functionalities for machine learning and data analytics. This makes Apache Spark one of the most in-demand skills for data engineers, data scientists, etc. Big Data is one of the most valuable skills today. So this course will teach you everything you need to position yourself in the Big Data job market.
In this course we will teach you the complete skillset of Apache Spark and PySpark. Starting from the basics to the most advanced features. We will use visual presentations in Power Point, sharing clear explanations and useful professional advice.

This course has the following sections:
Introduction to big data and fundamentals of Apache Spark
Installation of Apache Spark and libraries such as Anaconda, Java, etc.
Spark RDDs
Spark Dataframes
Advanced features with Apache Spark
Advanced analytics and data visualization
Spark Koalas
Machine Learning with Spark
Spark Streaming
Spark GraphX
Databricks
Spark in the cloud (Azure)

If you're ready to sharpen your skills, increase your career opportunities, and become a Big Data expert, join today and get immediate and lifetime access to:
â€¢ Complete guide to Apache Spark (PDF e-book)
â€¢ Downloadable Spark project files and code
â€¢ Hands-on exercises and quizzes
â€¢ Spark resources like: Cheatsheets and Summaries
â€¢ 1 to 1 expert support
â€¢ Course question and answer forum
â€¢ 30 days money back guarantee

See you there!
Who this course is for:
Anyone who wants to learn advanced big data skills
Anyone who knows Python and wants to adquire Big Data processing skills
Anyone that want to make a career as a data engineer, data analyst or data scientist
Anyone interested in learning Apache Spark and Pyspark for Big Data analysis
Anyone that want to learn cutting-edge technology in Big Data

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