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Azure HDInsight [30] is a big data-relevant service that deploys Hortonworks Hadoop on Microsoft Azure and supports the creation of Hadoop clusters using Linux with Ubuntu. Azure Stream Analytics is a Serverless scalable event-processing engine that enables users to develop and run real-time analytics on multiple streams of data from sources ...
Azure Data Explorer is a fully-managed [1] big data analytics cloud platform [2][3] and data-exploration service, [4] developed by Microsoft, [5][6] that ingests structured, semi-structured (like JSON) and unstructured data (like free-text). [7][8][9][10] The service then stores this data and answers analytic ad hoc queries on it with seconds ...
Website. databricks.com. Databricks, Inc. is a global data, analytics and artificial intelligence company founded by the original creators of Apache Spark. [3] The company provides a cloud-based platform to help enterprises build, scale, and govern data and AI, including generative AI and other machine learning models. [4]
Azure Data Lake service was released on November 16, 2016. It is based on COSMOS, [2] which is used to store and process data for applications such as Azure, AdCenter, Bing, MSN, Skype and Windows Live. COSMOS features a SQL -like query engine called SCOPE upon which U-SQL was built. [2]
Website. markrussinovich.com. Mark Eugene Russinovich (born December 22, 1966) is a Spanish-born American software engineer and author who serves as CTO of Microsoft Azure. He was a cofounder of software producers Winternals before Microsoft acquired it in 2006.
Microsoft Garage. The Microsoft Garage is a Microsoft program that encourages employees to work on projects about which they are passionate, despite having no relation to their primary function within the company. Employees from all divisions of Microsoft are free to take part in Microsoft Garage activities and small-scale innovation projects.
Data engineering. Data engineering refers to the building of systems to enable the collection and usage of data. This data is usually used to enable subsequent analysis and data science, which often involves machine learning. [1][2] Making the data usable usually involves substantial compute and storage, as well as data processing.
Site reliability engineering. Site reliability engineering (SRE) is a set of principles and practices that applies aspects of software engineering to IT infrastructure and operations. [1] SRE aims to create highly reliable and scalable IT systems. Although they are closely related, SRE is slightly different from DevOps. [2][3][4]
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