Azure Stream Analytics is a fully managed PaaS offering that enables real-time analytics and complex event processing on fast moving data streams. Thanks to zero-code integration with over 15 Azure services, developers and data engineers can easily build complex pipelines for hot-path analytics within a few minutes.
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. This adds complexity in terms of having to orchestrate the pipeline such that data gets processed in a timely fashion.
Learn how to monitor performance and resource utilization on Azure HDInsight by keeping tabs on metrics, such as CPU, memory, and network usage, to better understand how your cluster is handling your workloads and whether you have enough resources to complete the task at hand.
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.
Build more accurate forecasts with the release of capabilities in automated machine learning. Have scenarios that require have gaps in training data or need to apply contextual data to improve your forecast or need to apply lags to your features? Learn more about the new capabilities that can assist you.
For businesses today, data is indispensable. Innovative ideas in manufacturing, health care, transportation, and financial industries are often the result of capturing and correlating data from multiple sources.
Apache Kafka is one of the most popular open source streaming platforms today. However, deploying and running Kafka remains a challenge for most.
We are excited to announce the General Availability of the autoscale feature for Azure HDInsight. This feature enables enterprises to become more productive and cost-efficient by automatically scaling clusters up or down based on the load or a customized schedule.
This post is part of a 2-part series about how organizations are using Azure Cosmos DB to meet real world needs, and the difference it’s making to them. In this post, we’ll examine additional implementation details and the outcomes resulting from the team’s efforts.
This post is part of a 2-part series about how organizations are using Azure Cosmos DB to meet real world needs, and the difference it’s making to them.