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Govindarajan Devarajan

Working with Data Activator in Microsoft Fabric

Microsoft Fabric has multiple experiences such as Data Engineering and Data science that provides options to analyze data and build insights. The Data Activator experience is one that majorly differs from others, as it allows us to automate actions based on the insights derived from the data. Data activator continuously monitors data from sources such […]

Optimizing performance and cost in Microsoft Fabric Lakehouse

Optimizing performance and cost in Microsoft Fabric Lakehouse

Microsoft Fabric’s Lakehouse platform use Delta Lake for its tables, an open-source format that enables versioning and ACID capabilities to Lakehouse. Since storage forms the most important part of a data architecture, it is essential to understand the practices that could improve performance and reduce costs. This blog aims to explain the concept of Delta […]

Navigating Data Storage in Microsoft Fabric: Lakehouse and Warehouse

Microsoft Fabric features two storage-compute platforms for batch data processing, namely Lakehouse and Warehouse, which offer the capability to store and query data for use in other components. These elements are seamlessly integrated with other parts of the Fabric, serving as central hubs for data storage. These storage systems are aimed for different use cases […]

The curious case of Column names in Microsoft Fabric Lakehouse'

The curious case of Column names in Microsoft Fabric Lakehouse

In self-service BI tools, having column names that are user-friendly is crucial for creating an environment that is easy for business users to navigate. The use of whitespace is significant in these instances, as it helps maintain the simplicity and user-friendliness of reports and dashboards. Power BI semantic models and dataflows support column names with […]

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