New Age Data Infrastructures: Factors Driving Data Infrastructures

Data Infrastructure    |    Beginner
  • 12 videos | 37m 30s
  • Includes Assessment
  • Earns a Badge
Rating 4.5 of 186 users Rating 4.5 of 186 users (186)
As technology advances, new ways to store, process, and analyze data emerge. For example, large database systems, which require a lot of storage space, have been moved to the cloud and made remotely accessible to many users. These kinds of data infrastructures require business leaders to understand modern data systems and their working principles fully. Use this course to get to grips with the key differences between legacy data systems and modern infrastructures and explore crucial concepts related to modern data infrastructures. By the end of the course, you'll be able to argue why new age data infrastructures are necessary and traditional data systems are limited.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Briefly describe traditional data and data warehousing architecture
    List and describe the limitations of traditional data architecture, including limitations on speed, scalability, compatibility, and consumption
    List and describe the limitations of using etl systems when working with data, including limitations on performance, scalability, and structure
    Compare key differences in etl (extract, transform, load) and elt (extract, load, transform) systems and describe how etl is used with traditional data architectures and elt with modern ones
    Specify the advantages and importance of utilizing multi-model data platforms
  • Describe the system and principles of work for a multi-model database
    List the most commonly used data sources and formats
    Specify why real-time processing is advantageous when dealing with large amount of data
    Describe how business intelligence analytics has developed from traditional to modern approaches
    Outline the evolution of data analytics, the changing perspectives with respect to it, and what's meant by descriptive, diagnostic, predictive, and prescriptive analytics
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 1m 41s
  • 2m 31s
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    3.  Limitations of Traditional Data Architecture
    3m 28s
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    4.  Limitations of Traditional ETL Systems
    3m 30s
    In this video, you'll learn how ETL based data warehousing solutions often get plagued by cost overruns, due to high effort volumes and their complexity. Moreover, ETL based projects are known for their appallingly high failure rates. This is because great care is needed to conceptualize the database and totally define requirements to avoid having to rework complicated and frail connections. FREE ACCESS
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    5.  Compare ETL and ELT Systems
    2m 30s
    In this video, you'll learn the differences between ETL and ELT. ETL and ELT are two different methods for loading data into a data warehouse. As discussed in our previous course on data warehousing and ETL, ETL stands for extract, transform, and load. The primary purpose of an ETL process is to extract data from various sources, transform it, and load it into the database. FREE ACCESS
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    6.  Demand for Multi-model Data Platforms
    4m 14s
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    7.  Multi-model Databases
    3m 50s
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    8.  Commonly Used Data Sources
    3m 16s
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    9.  Real-time Data Processing
    3m 53s
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    10.  Traditional and New Age Business Intelligence
    4m 20s
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    11.  The Evolution of Analytics
    3m 25s
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    12.  Course Summary
    52s

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