Mastering Big Data Analytics with PySpark [Video]

Mastering Big Data Analytics with PySpark [Video]

English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 8h 07m | 1.64 GB
eLearning | Skill level: All Levels

Mastering Big Data Analytics with PySpark [Video]: Effectively apply Advanced Analytics to large datasets using the power of PySpark

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.

• Gain a solid knowledge of vital Data Analytics concepts via practical use cases
• Create elegant data visualizations using Jupyter
• Run, process, and analyze large chunks of datasets using PySpark
• Utilize Spark SQL to easily load big data into DataFrames
• Create fast and scalable Machine Learning applications using MLlib with Spark
• Perform exploratory Data Analysis in a scalable way
• Achieve scalable, high-throughput and fault-tolerant processing of data streams using Spark Streaming

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.