PacktPub | Apache Spark 3 for Data Engineering and Analytics with Python [Video] [FCO]
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| [0] Introduction.mp4 | 47.1 MB | ||
| [10] MacOS - PySpark Installation.mp4 | 32 MB | ||
| [11] MacOS - Testing the Spark Installation.mp4 | 25.1 MB | ||
| [12] Install Jupyter Notebooks.mp4 | 31.5 MB | ||
| [13] The Spark Web UI.mp4 | 53.1 MB | ||
| [14] Section Summary.mp4 | 14.2 MB | ||
| [15] Section Introduction.mp4 | 12 MB | ||
| [16] Spark Application and Session.mp4 | 23 MB | ||
| [17] Spark Transformations and Actions Part 1.mp4 | 37 MB | ||
| [18] Spark Transformations and Actions Part 2.mp4 | 20.8 MB | ||
| [19] DAG Visualisation.mp4 | 28.6 MB | ||
| [1] The Spark Architecture.mp4 | 31.7 MB | ||
| [20] Introduction to RDDs.mp4 | 37.9 MB | ||
| [21] Data Preparation.mp4 | 17.4 MB | ||
| [22] Distinct and Filter Transformations.mp4 | 19.8 MB | ||
| [23] Map and Flat Map Transformations.mp4 | 22.1 MB | ||
| [24] SortByKey Transformations.mp4 | 22.3 MB | ||
| [25] RDD Actions.mp4 | 28.5 MB | ||
| [26] Challenge - Convert Fahrenheit to Centigrade.mp4 | 28.7 MB | ||
| [27] Challenge - XYZ Research.mp4 | 17.8 MB | ||
| [28] Challenge - XYZ Research Part 1.mp4 | 24.6 MB | ||
| [29] Challenge XYZ Research Part 2.mp4 | 14.9 MB | ||
| [2] The Spark Unified Stack.mp4 | 32.6 MB | ||
| [30] Structured APIs Introduction.mp4 | 46.6 MB | ||
| [31] Preparing the Project Folder.mp4 | 25 MB | ||
| [32] PySpark DataFrame, Schema, and DataTypes.mp4 | 26.9 MB | ||
| [33] DataFrame Reader and Writer.mp4 | 36.8 MB | ||
| [34] Challenge Part 1 – Brief.mp4 | 38.3 MB | ||
| [35] Challenge Part 1 - Data Preparation.mp4 | 26.3 MB | ||
| [36] Working with Structured Operations.mp4 | 45 MB | ||
| [37] Managing Performance Errors.mp4 | 25.7 MB | ||
| [38] Reading a JSON File.mp4 | 39 MB | ||
| [39] Columns and Expressions.mp4 | 36.2 MB | ||
| [3] Java Installation.mp4 | 23.6 MB | ||
| [40] Filter and Where Conditions.mp4 | 32.8 MB | ||
| [41] Distinct Drop Duplicates Order By.mp4 | 21.2 MB | ||
| [42] Rows and Union.mp4 | 27.2 MB | ||
| [43] Adding, Renaming, and Dropping Columns.mp4 | 28 MB | ||
| [44] Working with Missing or Bad Data.mp4 | 30 MB | ||
| [45] Working with User-Defined Functions.mp4 | 22.5 MB | ||
| [46] Challenge Part 2 – Brief.mp4 | 57.9 MB | ||
| [47] Challenge Part 2 - Remove Null Row and Bad Records.mp4 | 37.6 MB | ||
| [48] Challenge Part 2 - Get the City and State.mp4 | 38.8 MB | ||
| [49] Challenge Part 2 - Rearrange the Schema.mp4 | 47.2 MB | ||
| [4] Hadoop Installation.mp4 | 22.6 MB | ||
| [50] Challenge Part 2 - Write Partitioned DataFrame to Parquet.mp4 | 35.9 MB | ||
| [51] Aggregations.mp4 | 8.2 MB | ||
| [52] Aggregations - Setting Up Flight Summary Data.mp4 | 21.5 MB | ||
| [53] Aggregations - Count and Count Distinct.mp4 | 23.7 MB | ||
| [54] Aggregations - Min Max Sum SumDistinct AVG.mp4 | 21.6 MB | ||
| [55] Aggregations with Grouping.mp4 | 26.1 MB | ||
| [56] Challenge Part 3 – Brief.mp4 | 30.8 MB | ||
| [57] Challenge Part 3 - Prepare 2019 Data.mp4 | 21.9 MB | ||
| [58] Challenge Part 3 - Q1 Get the Best Sales Month.mp4 | 34.4 MB | ||
| [59] Challenge Part 3 - Q2 Get the City that Sold the Most Products.mp4 | 19.1 MB | ||
| [5] Python Installation.mp4 | 14.8 MB | ||
| [60] Challenge Part 3 - Q3 When to Advertise.mp4 | 39.2 MB | ||
| [61] Challenge Part 3 - Q4 Products Bought Together.mp4 | 43.2 MB | ||
| [62] Introduction to DataBricks.mp4 | 22.3 MB | ||
| [63] Spark SQL Introduction.mp4 | 22.8 MB | ||
| [64] Register Account on Databricks.mp4 | 11.4 MB | ||
| [65] Create a Databricks Cluster.mp4 | 11.5 MB | ||
| [66] Creating our First 2 Databricks Notebooks.mp4 | 12.8 MB | ||
| [67] Reading CSV Files into DataFrame.mp4 | 34 MB | ||
| [68] Creating a Database and Table.mp4 | 36.4 MB | ||
| [69] Inserting Records into a Table.mp4 | 33.5 MB | ||
| [6] PySpark Installation.mp4 | 42.5 MB | ||
| [70] Exposing Bad Records.mp4 | 18.8 MB | ||
| [71] Figuring out How to Remove Bad Records.mp4 | 15.4 MB | ||
| [72] Extract the City and State.mp4 | 30.4 MB | ||
| [73] Inserting Records to Final Sales Table.mp4 | 53 MB | ||
| [74] What was the Best Month in Sales_.mp4 | 26.2 MB | ||
| [75] Get the City that Sold the Most Products.mp4 | 7.6 MB | ||
| [76] Get the Right Time to Advertise.mp4 | 12.3 MB | ||
| [77] Get the Most Products Sold Together.mp4 | 24.9 MB | ||
| [78] Create a Dashboard.mp4 | 12.2 MB | ||
| [79] Summary.mp4 | 30.5 MB | ||
| [7] Install Microsoft Build Tools.mp4 | 11.3 MB | ||
| [8] MacOS - Java Installation.mp4 | 20.8 MB | ||
| [9] MacOS - Python Installation.mp4 | 23.1 MB | ||
| ▲ 84 total files | |||
Description
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By : David Mngadi
Released : August 2021
Course Source : https://www.packtpub.com/product/apache-spark-3-for-data-engineering-and-analytics-with-python-video/9781803244303
Video Details
ISBN 9781803244303
Course Length 8 hours 30 minutes
About
Apache Spark 3 is an open-source distributed engine for querying and processing data. This course will provide you with a detailed understanding of PySpark and its stack. This course is carefully developed and designed to guide you through the process of data analytics using Python Spark. The author uses an interactive approach in explaining keys concepts of PySpark such as the Spark architecture, Spark execution, transformations and actions using the structured API, and much more. You will be able to leverage the power of Python, Java, and SQL and put it to use in the Spark ecosystem.
You will start by getting a firm understanding of the Apache Spark architecture and how to set up a Python environment for Spark. Followed by the techniques for collecting, cleaning, and visualizing data by creating dashboards in Databricks. You will learn how to use SQL to interact with DataFrames. The author provides an in-depth review of RDDs and contrasts them with DataFrames.
There are multiple problem challenges provided at intervals in the course so that you get a firm grasp of the concepts taught in the course.
The code bundle for this course is available here: https://github.com/PacktPublishing/Apache-Spark-3-for-Data-Engineering-and-Analytics-with-Python-
Author
David Mngadi
David is a data management professional who is influenced by the power of data in our lives and has helped several companies become more data-driven to gain a competitive edge as well as meet the regulatory requirements. In the last 15 years, he has had the pleasure of designing and implementing data warehousing solutions in retail, telco, and banking industries, and recently in more big data lake-specific implementations. He is passionate about technology and teaching programming online.
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