Analytics

Azure reservations now available for Databricks and App Service. Auto-renew reservations and scope to resource group.

MATCH_RECOGNIZE in Azure Stream Analytics significantly reduces the complexity and cost associated with building, modifying, and maintaining queries that match sequence of events for alerts or further data computation.

Azure Databricks is a fast, easy, and collaborative Apache Spark based analytics platform that simplifies the process of building big data and artificial intelligence (AI) solutions.

Cloud data lakes solve a foundational problem for big data analytics—providing secure, scalable storage for data that traditionally lives in separate data silos. Data lakes were designed from the start to break down data barriers and jump start big data analytics efforts.

Azure Stream Analytics is a fully managed PaaS offering that enables real-time analytics and complex event processing on fast moving data streams.

Collaborating on data across organizations and integrating it into business decision making is foundational to digital transformation initiatives in organizations. To enable rich data collaboration, a new capability is needed to make sharing data of any size and shape, simple and governed.

Recent announcements in the open source ecosystem have led customers of prominent open source analytics technology companies to explore options with Microsoft Azure and to this end, we now have new and compelling offers aimed at helping on-premises open source analytics workloads to migrate to

News and updates from Azure premium files, Azure Blockchain, Azure Machine Learning, and more.

Most modern-day businesses employ analytics pipelines for real-time and batch processing. A common characteristic of these pipelines is that data arrives at irregular intervals from diverse sources.

Throughout our Internet of Things (IoT) journey we’ve seen solutions evolve from device-centric models, to spatially-aware solutions that provide real-world context.

When data scientists work on building a machine learning model, their experimentation often produces lots of metadata: metrics of models you tested, actual model files, as well as artifacts such as plots or log files.

There is a lot more data in the world than can possibly be captured with even the most robust, cutting-edge technology. Edge computing and the Internet of Things (IoT) are just two examples of technologies increasing the volume of useful data.