Streaming data is a new buzzword in the big data world. It is not exactly an old concept, but it has been recently introduced due to certain technological developments and enhancements. The key idea behind streaming data is that it allows querying of data as soon as it enters the database and before its contents expire or get updated with newer records.

When we talk about the great value of streaming data the first thing that comes to mind is real-time analytics. Real-time analytics means analyzing huge amounts of unstructured streams of events occurring at high velocity and extracting meaningful intelligence from these free form sequences as they happen (or as close to real-time as possible). Streaming Data Analytics also helps us correlate seemingly unrelated events like cross-selling an advertisement about a new software upgrade to customers visiting hardware stores.

The ability to collect, transform and analyze streaming data (real-time or near real-time) allows us to fill in the gaps that structured databases leave behind. This is particularly useful for operational intelligence where it's crucial to have up-to-the-minute information on events as they are being processed at high velocity, without having to wait on periodic batch updates. For example, monitoring equipment can send alerts based on pre-configured events so you know before your equipment fails.

Benefits of Streaming Data

Data is only one element of the jigsaw. Today's corporate enterprises can't afford to wait for data to be processed in bulk. Real-time data streams are used by everything from fraud detection and stock market platforms to rideshare apps and internet shopping sites.

Streaming data directly into databases can extend the capabilities of traditional databases to create systems that are more responsive to data changes. For example, in financial trading applications, streaming transactions data is stored in a database and used to update stock quotes in real-time. Companies like Amazon use thousands of these updated prices every second to set dynamic pricing algorithms across their product line.

Use Cases

Real-time data and analytics are possible thanks to distributed stream processing systems like Apache Kafka and Confluent. While there are applications for real-time data integration, analysis, diagnosis, and/or prediction in every sector, the ability to integrate, analyze, diagnose, and/or forecast real-time data at a massive scale opens up new possibilities. Not only can businesses utilize previously-stored or batch data in storage; but also they may gain important insights into in-motion data.

A few prominent use cases of streaming data include: 


Fraud Detection – With the evolution of financial technology and electronic banking, fraudsters have also evolved their methods. This has increased the importance of real-time fraud detection and risk management systems. Streaming data can support different brands covering different lines of business. For example, a bank may collect order and trade information from securities exchanges in one system and information about credit cards and customer accounts from another source. By correlating events across all channels, banks can detect signs of fraud sooner while also ensuring that customers are protected with accurate positive alerts against possible identity theft.
Machine Auto-Discovery – Efficient service discovery is an important element in building scalable distributed applications for microservices architecture or any other Service Oriented Architecture. Typically, applications use central configuration databases to discover available services in a cluster. However, this approach is not well-suited for environments where the number of services is constantly changing because it requires constant updates to the service list stored in the database.,

Auto-scaling – As businesses expand their client base and data volume increases, they find that manual scaling of hardware resources just doesn’t scale. Automating application deployments based on streaming data can optimize costs by using only what's needed when it's needed without over provisioning or standing up physical nodes that are never used. Consumer IoT applications are proliferated across industries from healthcare to automotive, logistics to manufacturing.
Machine Data Diagnosis – A key benefit of big data is the ability to monitor user behavior to detect issues before they become problematic. For example, when fleets of vehicles are equipped with sensors gathering data on temperature, fuel levels, speed, location, and other parameters, that information can be used for predictive analytics to identify potential maintenance needs or operational efficiency requirements.