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Microsoft Azure Blog

Latest posts

Published • 7 min read

Making AI real for every developer and every organization 

AI is fueling the next wave of transformative innovations that will change the world. With Azure AI, our goal is to empower organizations to apply AI across the spectrum of their business to engage customers, empower employees, optimize operations and transform products. To make this a reality, we have three guiding investment principles.

Published • 4 min read

Digitizing trust: Azure Blockchain Service simplifies blockchain development 

In a rapidly globalizing digital world, business processes touch multiple organizations and great sums are spent managing workflows that cross trust boundaries. As digital transformation expands beyond the walls of one company and into processes shared with suppliers, partners, and customers, the importance of trust grows with it.

Published • 7 min read

Migrating big data workloads to Azure HDInsight 

Migrating big data workloads to the cloud remains a key priority for our customers and Azure HDInsight is committed to making that journey simple and cost effective. HDInsight partners with Unravel whose mission is to reduce the complexity of delivering reliable application performance when migrating data from on-premises or a different cloud platform onto HDInsight.

Published • 6 min read

Quest powers Spotlight Cloud with Azure 

Spotlight Cloud is the first built on Azure database performance monitoring solution focused on SQL Server customers. Leveraging the scalability, performance, global distribution, high-availability, and built-in security of Microsoft Azure Cosmos DB, Spotlight Cloud combines the best of the cloud with Quest Software’s engineering insights from years of building database performance management tools.

Published • 2 min read

Understanding HDInsight Spark jobs and data through visualizations in the Jupyter Notebook 

The Jupyter Notebook on HDInsight Spark clusters is useful when you need to quickly explore data sets, perform trend analysis, or try different machine learning models. Not being able to track the status of Spark jobs and intermediate data can make it difficult for data scientists to monitor and optimize what they are doing inside the Jupyter Notebook.