Big Data Awareness (Entry Level)

Topics covered

• briefly describe traditional data and data warehousing architecture
• compare and contrast parallel and distributed computing systems
• 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
• compare structured and unstructured data and describe how the ability to extract value from unstructured data is important when dealing with big data
• define the big 7 characteristics that define big data: volume, velocity, variety, variability, veracity, visualization, and value
• describe how business intelligence analytics has developed from traditional to modern approaches
• describe the concept of big data and the history behind it
• describe the difference between data warehousing and big data and specify the impact that big data has had on data warehousing
• describe the difference between horizontal and vertical scaling and specify why horizontal scaling is the best choice with respect to big data
• distinguish between raw data, information, applicable knowledge, and general wisdom
• identify the sources that are capable of generating big data
• 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
• list the most commonly used data sources and formats
• specify why real-time processing is advantageous when dealing with large amount of data