Microsoft named a Leader in The Forrester Wave™: Streaming Analytics, Q2 2021
Today, we are announcing that Microsoft has been named a Leader in The Forrester Wave™: Streaming Analytics, Q2 2021. We believe this report truly reflects the market momentum of Azure Stream Analytics, satisfied customers, a growing partner ecosystem, and the overall strength of our Azure cloud platform. Take a look at the Forrester Wave™: Streaming Analytics, Q2 2021 report.
The Forrester Wave™: Streaming Analytics, Q2 2021 report evaluated streaming analytics offerings from 14 different solution providers, and we are honored to share that Forrester has recognized Microsoft as a Leader in this category. Azure Stream Analytics received the highest score in twelve different categories including performance, ability to execute, solution roadmap, customer adoption, and more.
The report states, “Microsoft makes world-class streaming analytics easy to use on cloud and edge. Azure Streaming Analytics lets developers use SQL to define a rich set of streaming analytics queries. Behind the scenes of these streaming analytics queries, the Azure Streaming Analytics service optimizes the underlying resources to maximize throughput and latency. Notable and unique for a cloud service provider is that streaming analytics queries can run on both the cloud and the edge using Azure IoT Edge. Azure Streaming Analytics’ sweet spot is for enterprises that need a cloud-scale streaming analytics capability and or a combination of cloud-scale streaming analytics and edge streaming analytics.”
Our key differentiators for streaming analytics
Best in class integration
Azure Stream Analytics is seamlessly integrated with the rest of the Azure Ecosystem: in few clicks, you can build powerful pipelines processing data from IoT Hub, or Event Hub (including Event Hubs from Apache Kafka applications) and generate real-time insight for dynamic dashboarding, real-time applications, large scale analytics, or long-term storage. Examples of outputs include Azure SQL Databases, Azure Cosmos DB, Azure Synapse Analytics, Power BI, and more. Additionally, by leveraging Logic Apps rich set of connectors, users can also connect their streaming pipelines to hundreds of third-party apps.
Ease of use
One of the advantages of Azure Stream Analytics is its simple SQL query language with powerful temporal constraints to analyze data in motion. Familiarity with SQL language is enough to author powerful queries.
As part of our commitment to simplify the end-to-end analytics journey, we are working to further integrate our unique low-latency streaming technology into Azure Synapse Analytics.
Additionally, we are now offering a no-code experience for streaming analytics with Power BI Streaming dataflows, enabling organizations to easily create end-to-end pipelines and productize them for real-time dashboards.
Azure Stream Analytics is designed for mission-critical scenarios. With the guarantee of no data loss, exactly-once processing, and repeatability—it is used for some of the most important streaming pipelines within Microsoft and is used to run critical workloads for thousands of customers.
The design of Azure Stream Analytics enables organizations to instantly scale out the processing power from one to hundreds of streaming units for any job to get additional processing power when needed.
With 99.9 percent availability guaranteed at the minute level, Azure Stream Analytics offers industry-leading service-level agreement (SLA) characteristics.
As part of our commitment to security, Azure Stream Analytics provides enhanced security for your data, supporting VNET, encryption of private assets with Customer Managed Keys. Additionally, with connections to other resources with Managed Identities, we provide high security while removing the need to define and rotate connection strings, simplifying platform maintenance.
With its powerful SQL language, Azure Stream Analytics provides a wide array of analytic capabilities such as native support for geospatial functions, integration with machine learning for real-time scoring, built-in machine learning models for Anomaly Detection, pattern matching, and more to help developers quickly build low-latency streaming analytics.
As latency and bandwidth are often key requirements of streaming analytics, we allow organizations to process data as close as possible to where it is generated. To support this scenario, our product is available in the cloud worldwide for cloud-scale analytics (currently in 30 plus Azure regions, more to come), and can also run on IoT Edge or Azure Stack.
Try it today
There is a strong and growing developer community that supports Azure Stream Analytics. Learn how to get started and build your first streaming pipeline for free.
Source: Azure Blog Feed