50 Hours of Big Data, PySpark, AWS, Scala, and Scraping

Part 1 is designed to reflect the most in-demand Scala skills. It provides an in-depth understanding of core Scala concepts. We will wrap up with a discussion on Map Reduce and ETL pipelines using Spark from AWS S3 to AWS RDS (includes six mini-projects and one Scala Spark project).

Part 2 covers PySpark to perform data analysis. You will explore Spark RDDs, Dataframes, a bit of Spark SQL queries, transformations, and actions that can be performed on the data using Spark RDDs and dataframes, the ecosystem of Spark and Hadoop, and their underlying architecture. You will also learn how we can leverage AWS storage, databases, computations, and how Spark can communicate with different AWS services.

Part 3 is all about data scraping and data mining. You will cover important concepts such as Internet Browser execution and communication with the server, synchronous and asynchronous, parsing data in response from the server, tools for data scraping, Python requests module, and more.

In Part 4, you will be using MongoDB to develop an understanding of the NoSQL databases. You will explore the basic operations and explore the MongoDB query, project and update operators. We will wind up this section with two projects: Developing a CRUD-based application using Django and MongoDB and implementing an ETL pipeline using PySpark to dump the data in MongoDB.

By the end of this course, you will be able to relate the concepts and practical aspects of learned technologies with real-world problems.

All the resources of this course are available at https://github.com/PacktPublishing/50-Hours-of-Big-Data-PySpark-AWS-Sca…

Type
video
Category
Data
publication date
2022-03-30
what you will learn

Build ETL pipeline from AWS S3 to AWS RDS using Spark
Explore Spark/Hadoop applications, ecosystem, and architecture
Learn collaborative filtering in PySpark
Recognize the distinction between synchronous and asynchronous requests
Understand MongoDB CRUD, query operators, projection operators, and update operators
Build APIs for CRUD operations in MongoDB through Django

duration
3272
key features
Data scraping and data mining for beginners to pro with Python * Clear unfolding of concepts with examples in Python, Scrapy, Scala, PySpark, and MongoDB * Master Big Data with PySpark and AWS
approach
This course is meant to represent the most in-demand talents that you will use in the industry right away. This course includes mini projects, which are an important part of the curriculum. These projects give you the chance to try things out for yourself through trial and error. You have the opportunity to learn from your mistakes. Importantly, the potential gaps that may exist between theory and practice are easily understood. We will offer you quizzes with solutions at the end of each lesson to help you improve your skills.
audience
This course is designed for absolute beginners who want to create intelligent solutions, study with actual data, and enjoy learning theory and then putting it into practice. Data scientists, machine learning experts, and drop shippers will all benefit from this training.

A basic understanding of programming, HTML tags, Python, SQL, and Node JS is required. However, no prior knowledge of data scraping, and Scala is needed.
meta description
Learn, build, and execute big data strategies with Scala and Spark, PySpark and AWS, data scraping and data mining with Python, and master MongoDB
short description
In this four-in-one course, we will cover data scraping and data mining for beginners to pros with Python; master Big Data with Scala and Spark; learn PySpark and AWS to master Big Data; and MongoDB for beginners.
subtitle
Big Data with Scala and Spark, PySpark and AWS, Data Scraping and Data Mining with Python, Mastering MongoDB for Beginners
keywords
Big Data, PySpark, AWS, Scala and Scraping, NoSQL, MongoDB, Django, CRUD, Data Mining, Data Scraping, SQL, RDDs, Hadoop
Product ISBN
9781803237039