{"id":5837,"date":"2023-10-20T17:15:05","date_gmt":"2023-10-20T21:15:05","guid":{"rendered":"https:\/\/solutionsreview.com\/data-integration\/?p=5837"},"modified":"2023-10-24T14:26:02","modified_gmt":"2023-10-24T18:26:02","slug":"full-stack-streaming-data-applications-for-real-time-insights","status":"publish","type":"post","link":"https:\/\/solutionsreview.com\/data-integration\/full-stack-streaming-data-applications-for-real-time-insights\/","title":{"rendered":"Full-Stack Streaming Data Applications for Real-Time Insights"},"content":{"rendered":"

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Solutions Review\u2019s Contributed Content Series<\/u><\/a> is a collection of contributed articles written by thought leaders in enterprise tech<\/a>. In this feature, Nstream<\/a>‘s Aditya Chidurala offers commentary on full-stack streaming for data applications.<\/strong><\/i><\/span><\/p>\n

\"\"<\/a>Organizations must make efficient, timely decisions to gain a competitive edge in today’s fast-moving business landscape. <\/span>In recent years, streaming data has provided enterprises with continuous data that can be leveraged in collaboration with stored data. However, the typical data analysis approach requires multiple data systems, which adds to costs and latency. This limits organizations’ ability to capture the true, real-time value of data and make immediate automated decisions.<\/span><\/p>\n

This is where streaming data applications built on full-stack application development platforms provide more value through the data pipeline.<\/span> As data is generated, streaming data applications gather, model, assess, and provide insights about data in true real-time. This is significantly different from systems that store and process data at a later time \u2013 even if just for a few minutes. When it comes to informed, automated, real-time decision-making and resulting competitive edge, customer satisfaction revolves around the speed with which data is received, processed, and analyzed and insights are gleaned.<\/span>\u00a0<\/span><\/p>\n

Traditional data processing takes data from many sources (on-premises, at the edge, or in the cloud) and digests and stores the data in applications prior to its incorporation. The storage stage, often called \u201cdata at rest,\u201d renders the data static in a database or a drive. While acceptable for many processes, when real-time decision-making requires immediate access to information, data at rest hampers its effectiveness.\u00a0<\/span>\u00a0<\/span><\/p>\n

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Full-Stack Streaming Data<\/strong><\/h2>\n

Key Use Cases for Streaming Data Applications<\/span><\/b>\u00a0<\/span><\/h3>\n

Nearly all industries can benefit from real-time streaming data insights \u2013 from retailers tracking inventory to transportation companies reducing fuel costs to financial institutions providing personalized recommendations. Fraud detection is another critical and growing use case that is well-suited for real-time data analysis. It requires the fastest insights, the ability to prioritize them, and the generation of rapid responses.<\/span><\/p>\n

Batch-processed data is too slow, as the information about fraud detected can be outdated, and in some cases, it can take hours to identify and respond. S<\/span>treaming data applications are faster and more effective at reducing losses and preventing future financial strains.\u00a0<\/span>\u00a0<\/span><\/p>\n

Other common use cases include real-time customer 360, inventory management and tracking, and anomaly detection.\u00a0<\/span>\u00a0<\/span><\/p>\n