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Microsoft Cortana Intelligence Suite
In April 2016, Microsoft launched a set of new offerings named the Cortana Intelligence Suite (previously called Cortana Analytics Suite). Although the suite by itself is also a product through Cortana Intelligence Solutions, the heart of the Suite lies in all the individual Azure products it combines such as Azure HDInsights, Azure Data Lake, Azure Blob Storage, Azure Machine Learning, Azure Stream Analytics, etc. (see full list below)
Figure 1: Strategic overview of Cortana Intelligence Suite
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The power of the Cortana Intelligence Suite lies in its ability to seamless connect all these individual products and aggregate them into a E2E solution designed to tackle your big data or advanced analytics use-case.
Example: Doing predictive maintenance in a supply-chain environment. For the use-case to work, we'll need to capture the data using Event Hubs, curate the date using Stream Analytics and save the output in Blob storage. From there, we'll apply our Machine Learning algorithm and finally save the output data in an SQL Server. Finally, the visual dashboard for the operators will be build on PowerBI. The added value of Cortana Suite is that all of above products are combined in the Cortana Suite Solution design and offered to the user at-once (incl. configured connections).
Multiple designed solutions are already outlined on the Cortana online website. From there, clients can get inspiration on use-cases to tackle, read how other clients tackled them and finally get started by either getting hands-on with the respective Azure products or contacting a partner such as element61.
In summary, the Suite thus aggregates a set of individual products. Given the importance of these products we want to stress the the key components of most suites.
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Short description on key Azure products:
- Azure Machine Learning
Serves as an accessible but comprehensive tool to build machine learning models. Read more on Azure Machine Learning in our recent insight.
- Azure HDInsight
Allows for fast computations written in Hadoop, Spark, R, HBase or Storm. It’s designed to handle any amount of data with the ability to scale up to tera- and petabytes on demand.
- Azure Stream Analytics
Designed to offer real-time stream processing to get real-time insights from device, sensor, infrastructure and applications data (Internet of Things)
- Azure Data Lake Analytics
- Azure Data Lake Store
Serves as a hyper-scale repository for data and thus as a big file-system (HDFS) without any limit on file or storage size.
- Azure SQL Data Warehouse
Offers the known strengths of a SQL Server solution as an integration cloud solution
- Azure Data Factory
Acts as an orchestrater of data pipelines or data load and transformation processes
- Azure Data Catalog
- Azure Event Hub
Acts as a receiving point for events or data streams such as real-time sensors or IoT-devices
- Power BI
Offers a dashboarding tool for simple but robust visualizations; even in real-time
- Cognitive Services
- Bot Framework
Although the list is already long, numerous other components of Microsoft Azure will play a crucial role in the end solution. The Azure network currently contains 600+ individual products and depending on the upcoming needs in data science and big data industry, Microsoft will add additional (integrated) products.