Top Trends For Stream Processing Expected To Affect Enterprises In 2019
Data serves as the life source for many organizations many of whose customers rely on live stream data. Take for example applications that provide live updates such as Uber or Google Map. Stream processing has set a new standard of customer experience in terms of how users relate to data. By definition, stream processing is a technology that let users query continuous data streams and detect conditions quickly within a small time period from the time of receiving the data. The detection time period varies from few milliseconds to minutes. As opposed to the traditional data processing which follows a store and process procedure, stream processing allows live and incoming data to be processed simultaneously and continuously. Stream processing simplifies the offering of personalized services to customers and instantaneous response to issues. According to a report, the world data will grow from 33 zettabytes in 2018 to a massive 175 zettabytes by 2025 of which nearly 30% will be live stream data. The report also says that the number of consumers who interact with data every day will rise from 5 billion in 2018 to 6 billion in 2025 which would make up 75% of the world population. With data only expected to grow, organisations have to come up with efficient ways to process live data. Here are the top trends for stream processing expected to hit the enterprises in 2019 Need For Distributed Stream Processing Machine Learning models heavily emphasize on data and with more and more AI applications and the use for live data going up, distributed stream processing will become a necessity. Distributed and high-performance stream processing frameworks will be required to handle complex real-time data efficiently. Greater Bandwidth For IoT, More Data To Process With Fifth generation, cellular communication already on its way, and more IoT devices hitting the market, more real-time streaming data will be created and thus more use cases that need instant reaction to events. Edge computing will also increase the need for stream processing. Better Compliance With GDPR With both consumers and organizations growing concern over the privacy of sensitive data, stream processing will lead a new path which is more GDPR compliant than traditional “Store and process later” architectures as Stream processing does not require long term storage of data and sensitive information can be kept isolated in the application state for a limited time. Stream Processing Helps Cyber Security Data breaches and other cybersecurity threats are on the rise and stream processing is expected to provide a great deal of help. Stream processing will be emphasized in Cyber Security as it brings in real-time gathering and aggregating of events, tracking complex patterns, evaluating and adjusting ML models over the real-time data among other features into the plate. Complex Data Calls For Stream Processing Stream processors will have an edge over relational databases as Stream processors can process ACID transactions directly across streams. Stream processing will add more flexibility to the processing of data as multiple and overlapping streams can be resolved simultaneously. Outlook Stream processing, by all means, proves to be better than traditional data processing architectures and has high hopes for future with data growing enormously every second. Stream processing promises a better and more consumer-friendly approach to the management of data and its privacy and hence we can expect this trend to go up in the future.
Data serves as the life source for many organizations many of whose customers rely on live stream data. Take for example applications that provide live updates such as Uber or Google Map. Stream processing has set a new standard of customer experience in terms of how users relate to data. By definition, stream processing is […]
Data serves as the life source for many organisations many of whose customers rely on live stream data.Here we list the top 5 trends in stream processing.
["iot enterprise architecture", "iot friendly database"]
["Go", "stream processing", "machine learning", "programming_languages:R", "AI", "iot enterprise architecture", "ML", "RAG", "GAN", "iot friendly database", "edge computing", "R"]
["AI", "machine learning", "ML", "RAG", "edge computing", "R", "Go", "stream processing", "GAN", "programming_languages:R"]
A Computer Science Engineer turned Data Scientist who is passionate about AI and all related technologies. 
Contact: amal.nair@analyticsindiamag.com