Azure Synapse Link for Dataverse provides a continuous pipeline of data from Dataverse to Azure Synapse Analytics or Azure Data Lake Storage Gen2. Once the link is established, the service will export all the initial data as well as sync any incremental changes, enabling customers to glean near real-time insights from data sourced from the Microsoft Dataverse service.
Read MoreA data architecture pattern that brings elements of the data warehouse to the data lake, enabling a broader set of analytical workloads to coexist on a unified platform. The data lakehouse is not a new product or service, but rather an evolution of the data lake pattern, made possible due to recent technology breakthroughs that have brought warehouse-like capabilities such as ACID transaction support, schema evolution, and time travel to the data lake.
Read MoreAzure Data Share is a fully managed service that enables organisations to share data across tenants (B2B), simply and securely. Data can be consumed as needed by triggering a full copy or incremental update. Alternatively, data can be received automatically at a regular interval, as defined by the data provider.
Read MoreA collection of resources, study notes, and learning material that helped me, and can hopefully help others, prepare for and pass exam DP-201: Designing an Azure Data Solution. Note: Passing DP-201 is one out of two steps required to become a Microsoft Certified: Azure Data Engineer, you must pass both DP-200 and DP-201.
Read MoreA collection of resources, study notes, and learning material that helped me, and can hopefully help others, prepare for and pass exam DP-200: Implementing an Azure Data Solution. Note: Passing DP-200 is one out of two steps required to become a Microsoft Certified: Azure Data Engineer, you must pass both DP-200 and DP-201.
Read MoreIn part 3, we ratchet up the complexity once more to see how we handle multiple JSON files with schema structures that require extraction at multiple levels (i.e. highly hierarchical). Using U-SQL via Azure Data Lake Analytics we will transform semi-structured data into flattened CSV files to glean insights on the top restaurants across London.
Read MoreIn part 2, we ratchet up the complexity to see how we handle JSON schema structures more commonly encountered in the wild (i.e. an array of objects, dictionaries, nested fields, etc). Using U-SQL via Azure Data Lake Analytics we will transform semi-structured data into flattened CSV files.
Read MoreIn part 1, we begin by learning how to set up our environment in order to query JSON documents using U-SQL via Azure Data Lake Analytics, then storing the flattened results into a CSV file back into Azure Data Lake Store.
Read MoreLearn how to upload a file from a local machine to an Azure Data Lake Store using the .NET SDK.
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