Section: Describe how to work with non-relational data on Azure
Explanation:
Data as it appears in the real world is naturally connected. Traditional data modeling focuses on defining entities separately and computing their relationships at runtime. While this model has its advantages, highly connected data can be challenging to manage under its constraints.
A graph database approach relies on persisting relationships in the storage layer instead, which leads to highly efficient graph retrieval operations. Azure Cosmos DB's Gremlin API supports the property graph model.
Reference:
https://docs.microsoft.com/en-us/azure/cosmos-db/graph-introduction#introduction-to-graph-databases

Reference:
https://docs.microsoft.com/en-us/azure/azure-sql/database/features-comparison

Reference:
https://docs.microsoft.com/en-us/azure/data-lake-store/data-lake-store-get-started-portal
https://docs.microsoft.com/en-us/azure/storage/common/storage-account-overview
https://azure.microsoft.com/en-us/pricing/details/bandwidth/

Reference:
https://docs.microsoft.com/en-us/power-bi/connect-data/service-datasets-across-workspaces
https://docs.microsoft.com/en-us/power-bi/consumer/end-user-dashboardshttps://powerbi.microsoft.com/en-us/excel-and-power-bi/