Real world examples of Microsoft Azure Data Analytics

In order to help you understand what we're talking about, here are a few examples. You can use our Data Factory to extract data from a variety of sources and then load it into our SQL Data Warehouse, as we discussed in the previous module. Using Analysis Services on top of this is a fairly common pattern, and you can then create some models that make sense for your users. And, of course, Power BI can be plugged in anywhere in this room. After we've done the Analysis Services, we'll be able to pull that data into a model, and we'll be able to show it visually to the users. We can also combine Stream Analytics with Machine Learning, as an example. The Event Hubs that we discussed earlier can be used for ingesting large amounts of data. For example, you could use IoT Hubs or Kafka to collect data. We can then use our stream analytics to answer some questions about what data we should be looking for. Machine learning can be integrated into our stream analytics as part of that. So it can analyze the data, use what we've learned from training the model to get the results you need, and then output that data into a data store. In this example, the Azure Cosmos DB we might use is depicted as an Azure Cosmos DB. It could be stored in a data lake. For example, you could store it in a SQL Server or any other data store that makes sense for your application to consume the results of the stream analytics and machine learning models. We've looked at a variety of analytics tools, and the more data we collect, the more we need to understand it. A user-friendly interface is essential for managing and distributing content. It's also difficult to make sense of all the data because there's so much of it. We've got data warehouses and analytics tools built to handle and benefit from cloud scale in Azure. Provisioning, scaling, and reliability and resilience are all built-in. These big data workloads can be supported by the tooling we currently have, which allows us to analyze enormous amounts of data and distill it down to the most important bits of information for the people or systems that will be analyzing it in order to improve intelligence and provide better results for end users. Also, I'd like to point out that Microsoft has taken a strong interest in open source software development. Rather than trying to replace open source analytics tools, HDInsight and Databricks are working to build a cloud infrastructure that makes it easier to get started using them. Data storage and processing was covered in our previous module, as was data analysis in this one. The next module will focus on integration and how our systems are linked so that data and logic can be shared between them.

Видео Real world examples of Microsoft Azure Data Analytics канала EduFi