Big Data Literacy (Beginner Level)

Topics covered

• compare and contrast parallel and distributed computing systems
• define a non-relational database and describe how it does not use the traditional schema of rows and columns as found in traditional database systems
• define the role of the data processing layer and specify how information captured in the previous layer is processed
• define the role of the data storage layer using HDFS as an example of commonly used primary data storage
• describe batch processing, its use cases, and common reasons for using it
• describe how the NoSQL approach facilitates the horizontal distribution of large, structured, and unstructured data and specify when to use NoSQL and SQL databases
• describe how to add structure to raw data and name big data tools that aid this process
• describe in-memory storage systems and their use cases and advantages using examples
• describe the challenges in the current data analytics models and system designs, such as scalability, consistency, reliability, efficiency, and maintainability
• describe the concept of columnar databases, which store data in a column-wise format
• describe the Hadoop system and name its main features, benefits, and use cases
• describe the main advantages of big data analytics, including cost reduction and better decision-making
• describe the process of deciphering correlations, market trends, patterns, and customer behavior using big data
• describe the subcomponents of Hadoop, such as MapReduce and HDFS
• list and describe five main challenges when dealing with big data
• list top domains that are exploring and utilizing big data technologies, including process automation, security, and credit scoring
• name and describe the features of Hadoop HDFS and identify common in-memory storage systems including Kudu, Elasticsearch, and CockroachDB
• outline how HBase architecture works and compare column and row-wise storage of data
• outline how stream processing enables quick decision-making by creating actionable real-time insights
• outline the main pillars and components of big data architecture
• recognize the role of NoSQL databases in horizontal distribution of large, structured, and unstructured data
• specify why unstructured data comes from variable sources and describe how it moves from its origin to storage and gets further analyzed and visualized