Real-time Analytics 101

Real-time or Streaming analytics is the ability to calculate analytics in real time while moving within the stream of data. It involves monitoring and acting upon events at any given moment. Organizations must act on the data quickly before the data loses its value. Typically, the data originates from the clickstream, e-commerce transactions, Internet of Things (IoT), and devices such as mobile phones and sensors.

The classic example is a recommender engine used in e-commerce sites such as Amazon or Flipkart which constantly updates its recommendations based on the user views and selection of products. Real-time Analytics offers insights into customer behavior.  Via recommendations, it can create a personal shopping experience for each customer.  Organizations can gain visibility into a customer like and dislike and finally the customer buying behavior. Based on this data, these orgs can increase revenues through up-sell and cross-sell of products.  This gives companies the ability to generate additional profit, retain existing customers, rapidly respond to customer needs and remain competitive.

Popular Tools

Most popular open source tools used in real-time analytics is Apaches Spark. Apache Spark is a distributed data analysis platform for large-scale data processing. it is easy to build scalable and fault-tolerant applications. It supports tools such as MapReduce for in-memory processing and stream processing.

The ELK stack (ElasticSearch, Logstash, Kibana) which is now known as Elastic Stack is also gaining popularity. One can centralize any desired log and machine data with Logstash, store it in Elasticsearch, and then display it with real-time Kibana visualizations. Elastic has created an end-to-end stack that delivers actionable insights in real time from almost any type of structured and unstructured data source. The Elastic Stack makes searching and analyzing data easier than before.

As more and more data is generated from a variety of connected devices and sensors, transforming this data into actionable insights and predictions in near real-time is now an operational necessity. Azure Stream Analytics seamlessly integrates with Azure IoT Hub and Azure IoT Suite to enable powerful real-time analytics on data from your IoT devices and applications.

What is the advantage of real-time?

Because of in-memory processing, one can analyze data even before it lands in the database. Due to this, the biggest and most important advantage of real-time analytics is faster decision making than traditional data analytics technologies!!! The ability to analyze data as soon as it becomes available opens many choices. Businesses can identify new revenue streams, analyze risks and improve customer services which eventually results in an increase in profits.

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