Perish the thought of ‘Data’ not being perishable. What has perished is the pairing of ‘perishable’ just with food, goods and products, for data can now turn perishable if it is not acted upon in quick time and close to the source where it gets generated. Fast data, fast action on data and faster the time to intelligence apparently refer to action being taken on data within milliseconds for making instant decisions.

As sensors, IoT devices, smart signals, social networks and other assets emit real-time data, streaming architecture augmented by Storm, Flink, Kinesis, Kafka, HDFS, Spark, HBase and Hive gives enterprises the much needed streaming analytics environment for ingesting, storing and processing streaming data to extract the much required real-time intelligence.

Setting broader context for streaming analytics

For stream processing to spill value, it is imperative to think beyond on-the-fly analytics on streaming data. There are more patterns and scenarios worth considering to seek value from real-time analytics. Take ‘Alerts’ for instance.

Alert scenario in Manufacturing

A manufacturer is keen to minimize downtime of one of the instruments. The manufacturer relies on the ‘alert’ mechanism to respond quickly to specific events and be proactive in preventing instrument failures – this in turn is made possible by streaming analytics.

Stream processing for sentiment analysis in Retail

When a retail chain sets out to make the most of sentiment analysis, leveraging real-time stream processing, it calls for preprocessing to facilitate message transformation in the data pipeline – transformations by way of removing attributes from a message or adding attributes to a message or getting a single data pipeline out of many pipelines. Stream processing also accommodates other patterns where multiple streams can be integrated for creating a new one or when correlation of data within a stream matters most to acquire intelligence.

Financial institution and stream processing

Consider a bank that wants to keep a check on fraudulent activities that can tarnish the image of the bank. What steps would the bank take to identify a fraudulent transaction?

With streaming data propelling detection of fraudulent activities, banks bolster detection by combining historical data and streaming data – systems for gleaning customer details, spending patterns combined with streaming transaction data –to identify fraudulent transactions.

Stream processing + ML

Stream processing also works with machine learning. For instance anomaly detection is a case in point. In an industrial setup, unearthing false alarms triggered by sensors can be of great value, wherein big data technologies like Spark, Kafka and Flink along with machine learning algorithms like DNN and Random Forest come together for classifying alarms and improving accuracy.

Laying the data pipeline

As we lay the real-time data pipeline, what rises into an absolute imperative is the need to adopt Massively Parallel Processing (MPP). Let’s take the case of fraud detection in financial institutions which reiterates the significance of streaming data, wherein stream processing pipeline is laid.

Stream processing significance in retail

A retail chain wants to know the sale of a particular SKU across stores on a week basis, where batch processing can help satiate the need. When a competitor announces an ad-hoc promotion, sudden and luring in nature, there can be no delay in critical data acquisition related to that promotion. The retail chain has to act immediately to collate real-time details and use prescriptive analytics to roll out the next best move to beat competition. In this case, stream processing comes to the fore to help the retailer in tackling competitor measures with near real-time responses.

Take the case of promoting personalized offers to customers walking into a store. This is made possible when stream processing is complemented by batch processing. In such a scenario, Lambda architecture built using Spark, so to speak, can help the retailer achieve this objective. If big data architecture aims at maximizing value from batch and stream processing systems, data pipeline can be designed in a way where the data processing layer accommodates batch, real-time and hybrid processing.

Media & streaming analytics

A leading media company realized the significance of video streaming to read customer behavior and adopt novel ways to engage customers. When data-in-motion mattered most, the media company laid the streaming analytics pipeline laying emphasis on collecting maximum event data. This was achieved using Kinesis, AWS Lambda functions facilitating transformations and Kinesis Firehos and MemSQL.

Streaming data pipeline and technologies

In building streaming analytics pipeline, Kafka has grown into a preferred technology for creating real-time data pipelines. Along with Kafka, Spark, HBase and Storm, for instance, team up to facilitate streaming analytics and real-time insights.

Whether the business lens has its focus trained on preventing revenue losses and forecasting price fluctuations or promoting custom offers and offering personalized care, streaming analytics has empowered enterprises to gain value from data when it matters the most and accomplish set objectives.

‘Exhibit two adjacent parking slots that are unoccupied now in this multi-level car parking complex’ – That’s one query that captures the relevance and significance of real-time stream processing in this digital world.

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