Modern life demands – and thrives on – immediacy. It’s no wonder that businesses in virtually every industry are not only automating front-end services and offerings, but they’re also adopting processes, workflows, and tools to keep backend processes in constant flow. And so it makes sense that data streaming technologies, too, join the mix.
What is Data Streaming?
Data is king in the digital-first era, and how you process and leverage your data can make or break your organization. Data streaming enables the continuous inflow and processing of data (known as streams). This data is fed into a data streaming application, where they are processed, stored, parsed, and transformed into actionable insights.
Real-time data streaming enables businesses to automate the receipt and treatment of data, either through continuous streaming or batch processing. This data may include browser content logs, order processing or financial transaction logs, or sensor data.
How Does it Work?
No matter what data you have or what you plan to do with it, a data streaming architecture is a necessity to handle your data effectively. Consider three key aspects:
- Data stream processing platforms are built to capture data from a defined device or application.
- Analytics tools supplement the data processors by parsing or querying the incoming data.
- Storage space is required to direct your streamed data for use later.
Real-time (streaming) or Batch Processing
Understanding how you would like to handle big data is fundamental to your approach. When it comes to data streaming, you can opt for real-time or batch processing according to your organization’s needs.
Batch processing collects data across a specified amount of time. The data is collected and held, then sent for analysis manually or in automated intervals.
Organizations most commonly use this approach with large amounts of data or legacy tools in their environment that cannot deliver streamed data. Batch processing is practical when you don’t need to parse data immediately, and real-time results would be overkill, such as payroll or billing information.
Streaming or real-time is exactly as it sounds: as data comes in, it is dispatched to your analytics tools piece by piece.
In cases where you need real-time analytics and information generated from your data, streaming is key. Building data streams yields near-immediate insights with the help of data stream processing tools. Streaming is beneficial for fraud detection, log monitoring, and customer behavior analysis.
Benefits of Data Streaming
Underlying all wise business decisions is data. Data can inform your direction, help you change course if things aren’t working out, detect fraud to protect your business, customers, or end users, and more. Data streaming enables you to make these decisions swiftly.
Real-time data analysis offers continuous insights into your business functions. With instantaneous views of dynamic data from multiple streams, you can view an accurate picture of your business operations and respond accordingly.
Streaming also saves storage space. Rather than aggregating large amounts of data and processing it later, streaming allows you to save storage space by parsing and reviewing data insights rapidly.
With these real-time insights, you can mitigate risk before it becomes detrimental to your business. Identify threats, flag risks, and respond to events quickly.
Data Streaming Use Cases
The use cases for data streaming are endless. Here are a couple of instances when data streaming would be beneficial for business operations:
Video streaming and hosting sites
YouTube processes end-user browsing data and generates recommended content as a result, and it all happens in real time. According to Statista, more than 500 hours of video are uploaded to YouTube every minute. YouTube needs to be able to analyze the real-time streaming data for these videos, keep track of view and subscriber counts, publish comments, and suggest further watching.
Ride-sharing and delivery apps
In order to provide the best service to their customers and partners, ride-sharing and delivery apps need to analyze and consider a wide range of data. This includes location, distance, traffic conditions, price fluctuations, demand, and opening hours (in the case of delivery). Streaming data facilitates on-demand services like these.
Logistics companies
To track a vast network of vehicles by land, sea, and air, as well as a large number of moving parcels, logistics companies use streaming data. Real-time movement data enables these companies to provide accurate data for tracking numbers, identify any bottlenecks, report on weather conditions through integrated systems, and more.
