Mastering Big Data Analytics with PySpark

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Danny Meijer

8:07:06

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1-Python and Spark - A Match Made in Heaven

01-Course Overview.mp4

06:50

02-Python versus Spark.mp4

10:35

03-Preparing for the Course.mp4

06:23

04-Connecting Jupyter to Spark.mp4

14:58

2-Working with PySpark

05-Getting to Know Spark.mp4

07:27

06-The Power of Spark.mp4

06:38

07-The Power of Spark MLlib.mp4

06:35

08-Spark DataFrames.mp4

10:45

09-Spark Data Operations.mp4

11:33

3-Preparing Data Using Spark SQL

10-Loading Data from CSV Files.mp4

11:12

11-Fixing Issues in Our Data Part One.mp4

10:51

12-Fixing Issues in Our Data Part Two.mp4

10:44

13-Grouping, Joining, and Aggregating Part One.mp4

16:08

14-Grouping, Joining, and Aggregating Part Two.mp4

09:02

4-Machine Learning with Spark MLlib

15-Machine Learning with Spark.mp4

09:51

16-Building a Recommendation System with Spark MLlib Part One.mp4

11:11

17-Building a Recommendation System with Spark MLlib Part Two.mp4

11:22

18-Building a Recommendation System with Spark MLlib Part Three.mp4

16:18

19-Finalizing our Recommendation System.mp4

15:37

20-What We Have Learned So Far.mp4

10:13

5-Classification and Regression

21-Machine Learning with Spark.mp4

21:15

22-Machine Learning Pipelines.mp4

11:25

23-Running a Logistic Regression Pipeline.mp4

11:42

24-Parameters, Features, and Persistence.mp4

15:28

25-Frequent Pattern Mining and Statistics.mp4

21:59

6-Analyzing Big Data

26-Natural Language Processing with Spark.mp4

12:17

27-Identifying Our Data.mp4

11:38

28-Data Preparation and Exploration.mp4

11:38

29-Creating Our Raw Training Data.mp4

10:13

7-Processing Natural Language in Spark

30-Data Preparation and Regular Expressions.mp4

15:28

31-Data Cleaning and Transformation.mp4

19:01

32-Training a Sentiment Analysis Model Part One.mp4

15:33

33-Training a Sentiment Analysis Model Part Two.mp4

09:32

8-Machine Learning in Real-Time

34-Fetching Data from Twitter.mp4

06:24

35-Spark Structured Streaming.mp4

11:23

36-Managing and Converting Streams.mp4

12:49

37-Assembling Our Streaming ML Solution.mp4

17:05

38-A Structured Approach to ML Streaming.mp4

02:19

9-The Power of PySpark

39-Running Spark in Production.mp4

10:43

40-Running Spark at Scale.mp4

10:02

41-Tips, Tricks, and Take-Aways.mp4

14:59

Description

PySpark helps you perform data analysis at-scale; it enables you to build more scalable analyses and pipelines. This course starts by introducing you to PySpark's potential for performing effective analyses of large datasets. You'll learn how to interact with Spark from Python and connect Jupyter to Spark to provide rich data visualizations. After that, you'll delve into various Spark components and its architecture. You'll learn to work with Apache Spark and perform ML tasks more smoothly than before. Gathering and querying data using Spark SQL, to overcome challenges involved in reading it. You'll use the DataFrame API to operate with Spark MLlib and learn about the Pipeline API. Finally, we provide tips and tricks for deploying your code and performance tuning. By the end of this course, you will not only be able to perform efficient data analytics but will have also learned to use PySpark to easily analyze large datasets at-scale in your organization. All related code files are placed on a GitHub repository at: https://github.com/PacktPublishing/Mastering-Big-Data-Analytics-with-PySpark

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Big Data

PySpark

Danny Meijer

Instructor's Courses

Danny Meijer works as the Lead Data Engineer in the Netherlands for the Data and Analytics department of a leading sporting goods retailer. He is a Business Process Expert, big data scientist and additionally a data engineer, which gives him a unique mix of skills—the foremost of which is his business-first approach to data science and data engineering. He has over 13-years' IT experience across various domains and skills ranging from (big) data modeling, architecture, design, and development as well as project and process management; he also has extensive experience with process mining, data engineering on big data, and process improvement. As a certified data scientist and big data professional, he knows his way around data and analytics, and is proficient in various types of programming language. He has extensive experience with various big data technologies and is fluent in everything: NoSQL, Hadoop, Python, and of course Spark. Danny is a driven person, motivated by everything data and big-data. He loves math and machine learning and tackling difficult problems.

PacktPub

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Packt is a publishing company founded in 2003 headquartered in Birmingham, UK, with offices in Mumbai, India. Packt primarily publishes print and electronic books and videos relating to information technology, including programming, web design, data analysis and hardware.