Our team of experts carefully broke down the problem into components. Solutions for each of these components were brainstormed, and a proper flow of work:
With every problem, there are numerous challenges that need to be taken care of. Listed below are some of the important aspects that we had to consider:
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Data Sourcing: The data was acquired from multiple sources and in different formats like flat files (Excel and JSON), legacy on-premise SQL, SharePoint files and Microsoft OneDrive files.
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Data Ingestion: The massive amounts of unstructured data was then stored in Azure Blob storage. This was then converted into a structured format so that it can be fed to the data warehousing layer.
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Data Storage: To analyze multiple data formats (.xls, .csv, .json, etc.) we leveraged the Azure Synapse Analytics which is used for intelligent workload management.
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Building the Data Model: Next, with the help of a very handy tool- Azure Analysis Services, we built the data model on the cloud. This highly compatible platform as a service was used to combine and analyze multiple data sources and metrics.
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Protection Against Cyber Attacks: Azure Active Directory, an enterprise identity service was implemented to authenticate users and add conditional access. This would ensure that there is no threat to our client’s data.
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Data Visualization: We created a customized dashboard to track all the KPIs that are needed for better insights. Power BI was used being the leading data visualization tool in the industry. It can be connected to multiple data sources and proves to be the ideal solution for the management to check the metrics and make data-driven decisions.
