rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: 50 hours of big data, PySpark, AWS, Scala, and Scraping
  title info:
    title: Fifty hours of big data, PySpark, AWS, Scala, and Scraping
  name:
    name part: Ahmad, Muhammad
    name part: 1982-
    role:
      role term: instructor.
  name:
    name part: Packt Publishing
    role:
      role term: publisher.
  name:
    name part: AI Science (Firm)
  type of resource: moving image
  genre: videorecording
  genre: Instructional films.
  genre: Internet videos.
  genre: Nonfiction films.
  origin info:
    place:
      place term: xx
    date issued: 2022
    edition: [First edition].
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (1 video file (54 hr., 36 min.)) : sound, color.
  abstract: Learn, build, and execute big data strategies with Scala and Spark, PySpark and AWS, data scraping and data mining with Python, and master MongoDB About This Video 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 In Detail 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. Audience This course is designed for absolute beginners who want to create intelligent solutions, study with actual data, and enjoy lea…
  note: "Updated in March 2022."
  note: "AI Sciences."
  note: Muhammad Ahmad, )instructor.
  subject:
    topic: Data mining
  subject:
    topic: Big data
  subject:
    topic: Big data
  subject:
    topic: Data mining
  classification: QA76.9.D343
  classification: 005.1
  identifier: 9781803237039
  identifier: 1803237031
  identifier: 9781803237039 O'Reilly Media
  identifier: https://proxy.library.tamu.edu/login
  record info:
    record content source: ORMDA
    record creation date: 220510
    record change date: 20260129210752.9
    record identifier: in00004671163
    language of cataloging:
      language term: eng