analytics
786 TopicsAnnouncing Python in Excel: Combining the power of Python and the flexibility of Excel.
Since its inception, Microsoft Excel has changed how people organize, analyze, and visualize their data, providing a basis for decision-making for the millions of people who use it each day. Today we’re announcing a significant evolution in the analytical capabilities available within Excel by releasing a Public Preview of Python in Excel. Python in Excel makes it possible to natively combine Python and Excel analytics within the same workbook - with no setup required. With Python in Excel, you can type Python directly into a cell, the Python calculations run in the Microsoft Cloud, and your results are returned to the worksheet, including plots and visualizations.1.4MViews25likes160CommentsDecision Guide for Selecting an Analytical Data Store in Microsoft Fabric
Learn how to select an analytical data store in Microsoft Fabric based on your workload's data volumes, data type requirements, compute engine preferences, data ingestion patterns, data transformation needs, query patterns, and other factors.9.8KViews14likes5CommentsSecure Medallion Architecture Pattern on Azure Databricks (Part I)
This article presents a security-first pattern for Azure Databricks: a Medallion Architecture where Bronze, Silver and Gold each run as their Lakeflow Job and cluster, orchestrated by a parent job. Run-as identities are Microsoft Entra service principals; storage access is governed via Unity Catalog External Locations backed by the Access Connector’s managed identity. Least-privilege is enforced with cluster policies and UC grants. Prefer managed tables to unlock Predictive Optimisation, Automatic liquid clustering and Automatic statistics. Secrets live in Azure Key Vault and are read at runtime. Monitor reliability and cost with system tables and Jobs UI. Part II covers more low-level concepts and CI/CD.781Views11likes0CommentsMultivariate Anomaly Detection in Azure Data Explorer
ADX contains native support for detecting anomalies over multiple time series by using the function series_decompose_anomalies() that can analyze thousands of time series in seconds, enabling near real time monitoring solutions and workflows based on ADX. This function analyzes each metric independently for anomalies, however there are some anomalies that can only be detected by looking on multiple metrics at the same time. In this blog we present new ADX functions for multivariate anomaly detection, that jointly analyze time series of multiple metrics, and present example of these anomalies when analyzing prices of MSFT and SPY pair.9.7KViews10likes0Comments