Data for Leaders Awareness

Topics covered

• compare structured and unstructured data and describe how the ability to extract value from unstructured data is important when dealing with big data
• define concepts essential to data science like dataset, database, data analytics, data aggregation, and time series
• describe common sources of modern data and major data formats in use
• describe common use cases and basic principles of data warehousing
• describe extract, transform, and load (ETL) functionality and specify how the movement between transactional OLTP databases and a data warehouse is performed and how to organize and design your extraction, transformation, and loading capabilities to keep your data warehouse up-to-date
• describe how a data warehouse is different from a database and how data warehouses are used for business intelligence
• describe how to perform data migration and explain the functionality of common tools like extract, transform, and load (ETL)
• describe online transaction processing in the context of relational databases and data warehousing
• 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
• describe traditional data warehousing technologies such as virtual data warehousing and enterprise data warehousing
• 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
• name and describe common database types used in the industry
• name and describe the most commonly used ETL tools and software
• outline disaster recovery plans and list common data backup strategies and tools
• outline the evolution of data analytics, the changing perspectives with respect to it, and what's meant by descriptive, diagnostic, predictive, and prescriptive analytics