Data for Leaders Competency (Intermediate Level)

Topics covered

• compare and contrast various data warehousing schemas, such as Star, Snowflake, etc.
• 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
• define key characteristics and requirements for a reliable data collection pipeline
• describe situations when normalization or denormalization is needed and name the key steps of each process
• describe the architecture of a modern data lake
• describe the concept of data mart and how it can be used for business decision-making through data mining
• describe the differences between a data lake and a data warehouse
• describe the process of deciphering correlations, market trends, patterns, and customer behavior using big data
• identify how document databases are designed to store and query data as JSON-like documents and outline their benefits and use cases
• list and describe five main challenges when dealing with big data
• list the processes essential to preparing data and specify the goal of each process
• list widely used data management architectures
• name and describe the features of Hadoop HDFS and identify common in-memory storage systems including Kudu, Elasticsearch, and CockroachDB
• name and describe the most commonly used ETL tools and software
• name the components utilized in a cloud computing architecture
• name the major issues involved and strategies used when trying to achieve data compliance
• name the steps and processes essential to any data science project
• outline how stream processing enables quick decision-making by creating actionable real-time insights
• recognize how Spark offers an open-source, scalable, massively parallel, in-memory solution for analytics applications
• specify how multi-model databases combine different types of database models into one integrated database engine