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  • 1
    Online Resource
    Online Resource
    [Place of publication not identified] : Packt Publishing
    ISBN: 9781804610329 , 1804610321
    Language: English
    Pages: 1 online resource (1 video file (6 hr., 30 min.)) , sound, color.
    Edition: [First edition].
    DDC: 005.7
    Keywords: Big data Management ; Microsoft Azure (Computing platform) ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: Be part of a beginner's ready course on metadata-driven ingestion framework in Azure. Design, implement and be production-ready along with developing a new pipeline for a real project About This Video A beginner-friendly and comprehensive course on designing and implementing Azure Data pipeline ingestion Industry-based along with tips and tricks for production-ready data ingestion in an Azure project The highly practical course along with the theoretical concepts animated for better interactivity In Detail Building frameworks is now an industry norm and it has become an important skill to know how to visualize, design, plan, and implement data frameworks. The framework that we are going to build together is the Metadata-Driven Ingestion Framework. Metadata-driven frameworks allow a company to develop the system just once and it can be adopted and reused by various business clusters without the need for additional development, thus saving the business time and costs. Think of it as a plug-and-play system. The first objective of the course is to onboard you onto the Azure Data Factory platform to help you assemble your first Azure Data Factory pipeline. Once you get a good grip on the Azure Data Factory development pattern, then it becomes easier to adopt the same pattern to onboard other sources and data sinks. Once you are comfortable with building a basic Azure Data Factory pipeline, as a second objective, we then move on to building a fully-fledged and working metadata-driven framework to make the ingestion more dynamic; furthermore, we will build the framework in such a way that you can audit every batch orchestration and individual pipeline runs for business intelligence and operational monitoring. By the end of this course, you will be able to design, implement, and get production-ready for data ingestion in Azure. Audience This course is ideal for aspiring data engineers and developers that are curious about Azure Data Factory as an ETL alternative. You will need a basic PC/laptop; no prior knowledge of Microsoft Azure is required.
    Note: Online resource; title from title details screen (O'Reilly, viewed July 12, 2022)
    Library Location Call Number Volume/Issue/Year Availability
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  • 2
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : Packt Publishing | Boston, MA : Safari
    ISBN: 9781803244303
    Language: English
    Pages: 1 online resource (1 video file, approximately 8 hr., 31 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local ; Electronic videos
    Abstract: Master Python and PySpark 3.0.1 for Data Engineering / Analytics (Databricks) About This Video Apply PySpark and SQL concepts to analyze data Understand the Databricks interface and use Spark on Databricks Learn Spark transformations and actions using the RDD (Resilient Distributed Datasets) API In Detail 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. Who this book is for This course is designed for Python developers who wish to learn how to use the language for data engineering and analytics with PySpark. Any aspiring data engineering and analytics professionals. Data scientists/analysts who wish to learn an analytical processing strategy that can be deployed over a big data cluster. Data managers who want to gain a deeper understanding of managing data over a cluster.
    Note: Online resource; Title from title screen (viewed August 30, 2021) , Mode of access: World Wide Web.
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