Issue link: https://insights.oneneck.com/i/1519582
11 oneneck.com 11 oneneck.com 04 Early Adoption Azure Data Storage: Organizations typically start their Azure journey by migrating data to Azure Blob Storage or Azure Data Lake Storage for scalable, secure cloud storage. Azure SQL Database: Often one of the first steps in cloud adoption. A typical entry point for cloud migration. It transitions on-prem relational databases to a managed cloud service with familiar SQL capabilities, simplifying cloud migration. Azure Data Factory: Used for initial data integration and orchestration efforts. It helps move and refine data from multiple sources for Azure-based processing and analytics. Azure Data Pipelines: Refer to the data processing workflows in Azure Data Factory (ADF). These pipelines orchestrate and automate the movement and transformation of data from various sources to different targets. Mid Adoption Azure Synapse Analytics: As orgs look to scale their analytics capabilities, Azure Synapse Analytics enables complex queries and extensive data processing for scalable analytics. Azure Databricks: Ideal for businesses expanding their use of analytics and AI. Azure Databricks provides a unified workspace optimal for enhancing analytics and AI initiatives, using Apache Spark for rapid data science and machine learning development. Advanced Adoption Azure Cosmos DB: Azure Cosmos DB fits into the more advanced stages of adoption. Its ability to scale massively, offer low latency and support various data models makes it suitable for complex, high- performance applications. Power BI: Power BI can be introduced at various stages. It is used to visualize and share insights derived from complex analytics processes supported by other Azure services. It tends to align more with advanced adoption, enabling deep insights and data-driven decision- making across the organization.