Final Exam: Big Data Infrastructures

Big Data 2021    |    Beginner
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Final Exam: Big Data Infrastructures will test your knowledge and application of the topics presented throughout the Big Data Infrastructures track of the Skillsoft Aspire Data for Leaders and Decision Makers Journey.

WHAT YOU WILL LEARN

  • recognize the need for Big Data
    name and describe the role of the main layers of Big data analytics from the bottom to the top
    define the role of the data processing layer and specify how information captured in the previous layer is processed
    describe Spark and how it offers open-source scalable massively parallel in-memory solutions for analytics applications
    list the main characteristics of Spark such as loading behavior, file formats, parallelism, cache, data skews
    name most important performance optimization techniques such as file format selection, level of parallelism and API selection
    describe the concept of Big Data and the history behind it
    identify the sources that are capable of generating Big Data
    define the big 7 characteristics that define Big Data
    describe the subcomponents of Hadoop such as MapReduce and HDFS
  • describe the difference between horizontal and vertical scaling
    name and describe the features of Storage systems such as HDFS, S3 and Object stores, Elastic Search and Apache Solr, Kudu, CockroachDB
    describe the rewarding role of NoSQL databases in horizontal distribution of large, structured and unstructured data
    specify when to use NoSQL and when to use SQL database
    specify use cases, benefits and challenges of popular key-value data stores
    describe graph database use cases and specify why the relationship between data is as important as the data itself in a graph database
    specify the shortcoming of distributed systems and why these shortcomings make Big Data even more important
    describe what horizontal scaling is and specify how it eliminates the need for adding more memory to existing machines by using clusters (AKA, Sharding )
    name and describe the four types of Big Data Analytics (i.e. Prescriptive, Predictive, Diagnostic, Descriptive)
    describe the challenges in the current data analytics models and system designs such as scalability, consistency, reliability, efficiency, and maintainability

IN THIS COURSE

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