Taking the Big Data Leap With Microsoft Azure
Taking the Big Data Leap With Microsoft Azure
As Microsoft grows its Azure big data portfolio, it can understandably get difficult for users to keep up. Here are some frequently used Azure terms that you should know.
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Big data is essentially changing the way data warehousing has been working up until now. Data volumes are getting increasingly large and complicated. There are countless sources that stream data in real time. The sources include everything from conventional point-of-sale systems and e-commerce sites to more recent customer sentiment based platforms like Twitter. Data warehousing has reached a very vital tipping point since its inception. The process now calls for systems that offer enhanced flexibility as far as adding new information and detection of change are concerned.
What matters to today's folks is holding onto their most prized asset, namely big data. Every organization wants to keep each bit of data and store it safely indefinitely. Now, Microsoft Azure gives them a chance to do exactly that. With Azure, businesses do not need to make cost tradeoffs, and they do not need to choose which data to keep and which one to let go of. At budget-friendly rates, they can hold on to their data without having to compromise their regulatory or company standards.
Microsoft Azure's big data services have surely gained traction and garnered attention. The company's mission now is to build a supremely intelligent and adaptable cloud platform for comprehensive data storage, cataloging, and sharing. From information management to machine learning, data storage to analytics and cognitive services, Microsoft Azure has it all covered. Moreover, with Azure Marketplace, it has now become possible for organizations to access various applications and systems from big data and have great analytics partners.
Now that Microsoft is focusing on growing its Azure big data portfolio, it can understandably get difficult for users to keep up. While opting for a complete Azure training, it's essential to know all about the solutions that the platform has to offer and how these can benefit your business. There are some frequently used Azure terms that you can learn to make your big data leap smooth.
Azure Data Lake Store
This service works on Apache Hadoop and presents users with hyper-scale storage of big data sets. Both unstructured and structured data can be stored in the Data Lake Store — in their native format.
Azure Data Lake Analytics
Data Lake Analytics refers to a query service for big data based on Microsoft’s public cloud. The service helps users with data analysis and provides them with insights. It automates resource scaling, too. This service can help organizations with tools for management, identity, warehousing and security tools. Data Lake Analytics is part of the Cortana Analytics Suite and can access SQL Data Warehouse, Data Factory, and Power BI.
Azure Stream Analytics
When it comes to real-time analytics, nothing performs as well as Azure Stream Analytics. It's mainly used for IoT. It gains as well as provides insights based on streams of data. It also scales insights with comparatively low latency. Azure Event Hubs can compare a number of streams of data simultaneously. The service not only displays insights in real-time on a user-friendly dashboard but also enables the use of customized alarms.
Azure Power BI Embedded
This is a service that helps users to create interactive reports for a better visualization of data. Those reports can then be embedded in a variety of applications, even in-house apps of organizations. There is no need to alter the apps’ designs for this embedding to work. The data can be viewed from multiple sources like Azure SQL Data and Azure SQL Database.
Azure Data Factory
Data needs to move smoothly from the onsite data center to the cloud so that it can be prepared for consumption and this is exactly what Azure Data Factory does. It creates, schedules, initiates, and conducts the flow of data. You can keep an eye on the movement of the data and automate complex processes like data pipelines with Data Factory solutions. It is often used along with other big data solutions of Microsoft Azure, like machine learning, Stream Analytics, and HDInsight.
Azure Data Catalog
Streamlining data discovery is a mammoth of a task. However, it can be simplified extensively by Azure Data Catalog. With the help of this tool, the user can register and find data sources and also share insights. You can also catalog metadata sets and control access to data sets with Azure Data Catalog.
HDInsight is based on Hadoop and manages Apache Hadoop, R clusters, and Spark. Besides scaling on demand and storing large chunks of data, with HDInsights solutions, users can view and even analyze the data on Excel. Integrated with Hortonworks Data Platform, HDInsights ensures that data can directly go from an onsite data store to Azure. HDInsights also encompasses other services like Apache HBase, Apache Spark, Apache Storm, and R server for Hadoop.
The benefits of Azure’s big data-related solutions are multiple. It is no secret that different types of clients want different things from a business. Azure's services enable brands to deliver personalized experiences to customers with preferences that can be changed. For example, your system can recommend shopping items to customers based on their shopping history.
Azure can also make it easier for businesses to extract the insights that are buried deep in big data. Since getting a clear idea of the factors that affect operational efficiency in various ways is important, gaining these insights can help optimize business processes like documenting and forecasting customer and staff needs, managing supply chains, and organizing human resources. In fact, getting your employees trained in Azure can help you take effective measures manage your inventories better, meet customer expectations, identify backlog issues, and get them solved in less time, improving your ROI.
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