If you're looking to master the art of Big Data ETL with Pyspark, then you're in the right place. In this video, we're going to talk about how, with the help of Kaggle, you can process 175 million Uber_NY records efficiently.
Big data engineering has become an indispensable part of modern-day data processing, analysis, and visualization. And mastering the art of Big Data ETL with Pyspark is an excellent way to tap into the full potential of data processing power. In this video, we're going to show you how to process 175 million Uber_NY records using Pyspark and Kaggle.
The data used in these tutorial can be found at
1) www.kaggle.com/code/kamaljp/h...)
2) pyrite-ethereal-soccer.glitch...
3) hugovk.github.io/top-pypi-pac...
4) github.com/Kamalabot/s3-wareh...
5) www.kaggle.com/kamaljp/pyspar... for this video)
I willl start by giving you an overview of ETL (Extract, Transform, and Load) and how it applies to big data processing. We'll explain how ETL works and why it's essential for data processing, analysis, and visualization. We'll also give an introduction to Pyspark and Kaggle, which are popular Big Data tools used for ETL.
After the introduction, we'll take a deep dive into the Uber_NY dataset. We'll give you a breakdown of what the dataset contains, where you can find it, and how to load it into Pyspark. We'll also give you an overview of the pre-processing steps you need to take to optimize data processing performance.We'll then proceed to show you how to use Pyspark to perform ETL on the Uber_NY dataset. We'll walk you through the basics of using Pyspark to extract data, transform it, and load it into a database. We'll also give you practical tips on optimizing processing time and ensuring data accuracy.
Once we've shown you the basic Pyspark ETL operations, we'll introduce you to Kaggle. Kaggle is a platform that provides data scientists with access to large datasets and a community to share their data analysis and machine learning models. We'll show you how to use Kaggle to download the Uber_NY dataset and use it in conjunction with Pyspark to perform ETL.
We'll then dive into a practical exercise where we'll take the Uber_NY dataset through the ETL process. We'll show you how to use Pyspark and Kaggle to extract and transform Uber_NY data, load it into a database, and create visualizations to report the data. We'll also give you tips on how to optimize data processing performance and ensure data accuracy.
By the end of this video, you'll be confident in your ability to use Pyspark and Kaggle to process large datasets. You'll be able to extract, transform, and load Big Data efficiently by applying ETL techniques to your data processing workflows. You'll also have gained practical skills in using Pyspark and Kaggle, two of the most popular tools for Big Data ETL.
So if you're ready to take your Big Data ETL skills to the next level, join us in this video and learn how to process 175 million Uber_NY records with Pyspark and Kaggle.
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8 мар 2023
