New Age Data Infrastructures: Factors Driving Data Infrastructures
Data Infrastructure
| Beginner
- 12 videos | 37m 30s
- Includes Assessment
- Earns a Badge
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
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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
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1m 41s
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2m 31s
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3. Limitations of Traditional Data Architecture3m 28s
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4. Limitations of Traditional ETL Systems3m 30sIn 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 Systems2m 30sIn 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 Platforms4m 14s
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7. Multi-model Databases3m 50s
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8. Commonly Used Data Sources3m 16s
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9. Real-time Data Processing3m 53s
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10. Traditional and New Age Business Intelligence4m 20s
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11. The Evolution of Analytics3m 25s
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12. Course Summary52s
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