When modernizing data warehouses, businesses should consider their current, ideal, and future data infrastructure, including a centralized location for data storage, analysis, and processing. Investing in data infrastructure early is crucial for data maturity, ensuring compliance with data protection regulations and implementing security measures. A hybrid approach to modernizing data warehouses offers cost efficiency, ease of maintenance, and future extensibility, allowing businesses to make informed decisions about their data and analytical infrastructure based on their specific needs and goals.

Here's a proposed workflow for the solution,
🔄 Data is ingested from various sources like legacy data warehouses, SQL servers, and other cloud data warehouses, and migrated or partially refactored legacy databases and SSIS packages are orchestrated into Azure SQL Database through Azure Synapse Analytics pipelines.

🏪 Unstructured, semi-structured, and structured data is passed into Azure Data Lake Storage for centralized storage and analysis with other sources.

🌊 Real-time data from streaming sources enters the system via Azure Event Hubs and is analyzed immediately using Azure Stream Analytics.

🌐 Data is also passed into the centralized Data Lake for further analysis, storage, and reporting.

🔬 Serverless analysis tools are used in the Azure Synapse Analytics workspace for ad hoc data science explorations, early prototyping for data warehouse entities, and defining views for Power BI.

🔗 Azure Synapse Analytics is tightly integrated with potential consumers of fused datasets, such as Azure Machine Learning, Power Apps, Azure Logic Apps, Azure Functions apps, Azure App Service web apps, or other web applications.

🛡️ Microsoft Entra Managed Identities ensure security and role management within the entire pipeline.

𝘕𝘰𝘸, 𝘐'𝘷𝘦 𝘤𝘳𝘦𝘢𝘵𝘦𝘥 𝘢 𝘴𝘦𝘳𝘪𝘦𝘴 𝘰𝘧 𝘣𝘭𝘰𝘨 𝘱𝘰𝘴𝘵𝘴 𝘸𝘩𝘦𝘳𝘦 𝘐 𝘵𝘳𝘪𝘦𝘥 𝘵𝘰 𝘴𝘪𝘮𝘶𝘭𝘢𝘵𝘦 𝘵𝘩𝘦 𝘣𝘶𝘪𝘭𝘥𝘪𝘯𝘨 𝘢𝘯𝘥 𝘢𝘯𝘢𝘭𝘺𝘵𝘪𝘤𝘢𝘭 𝘱𝘳𝘰𝘤𝘦𝘴𝘴𝘦𝘴. 𝘛𝘩𝘦 4-𝘱𝘢𝘳𝘵 𝘣𝘭𝘰𝘨 𝘴𝘦𝘳𝘪𝘦𝘴 𝘸𝘰𝘶𝘭𝘥 𝘨𝘪𝘷𝘦 𝘢 𝘸𝘢𝘭𝘬𝘵𝘩𝘳𝘰𝘶𝘨𝘩 𝘰𝘧 𝘵𝘩𝘦 𝘤𝘳𝘦𝘢𝘵𝘪𝘯𝘨 𝘢𝘯𝘥 𝘴𝘤𝘢𝘭𝘪𝘯𝘨 𝘱𝘳𝘰𝘤𝘦𝘴𝘴𝘦𝘴 𝘧𝘰𝘳 𝘵𝘩𝘦 𝘸𝘢𝘳𝘦𝘩𝘰𝘶𝘴𝘦 𝘪𝘯𝘤𝘭𝘶𝘥𝘪𝘯𝘨 𝘲𝘶𝘦𝘳𝘺𝘪𝘯𝘨 𝘧𝘰𝘳 𝘨𝘦𝘯𝘦𝘳𝘢𝘵𝘪𝘯𝘨 𝘪𝘯𝘴𝘪𝘨𝘩𝘵𝘴. 𝗧𝗵𝗲 𝗹𝗶𝗻𝗸 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗮𝗿𝘁𝗶𝗰𝗹𝗲 𝗶𝘀 𝗶𝗻 𝘁𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝗰𝗼𝗺𝗺𝗲𝗻𝘁.

I will try to post them on LinkedIn separately along with 𝘁𝗵𝗲 𝘄𝗼𝗿𝗸𝗶𝗻𝗴 𝗚𝗶𝘁𝗛𝘂𝗯 𝗿𝗲𝗽𝗼𝘀𝗶𝘁𝗼𝗿𝘆 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗽𝗿𝗼𝗷𝗲𝗰𝘁.


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