Data underpins every decision in a modern-day business. From discovering new opportunities to measuring the progress of a campaign, data impacts absolutely every process we see on a day-to-day basis. Part of the evolution of data over the past decade due to its necessity has been a certain movement to on-demand analytics.
Real-time data is now everywhere we look, with live dashboards and embedded analytical tools providing a time advantage that translates to a revenue uplift. In fact, 80% of companies that incorporate real-time data analytics into their processes report a revenue increase due to the agility of these analytics.
Yet, despite their centrality to effective modern business practices, many organizations commonly confuse real-time data and streaming data. While both of these sources evoke a sense of live or near-live data, they are not the same thing.
In this article, we’ll live into the core differences that set real-time data and streaming data apart, demonstrating how each works and why they’re important in analytics environments.
Let’s dive right in.
What is real-time data?
For most organizations, the main point of contact we have with real-time data is digital dashboards. These could be CRM dashboards, customer analytics pages, or website traffic monitors. Behind each of these is a system that delivers real-time data, rapidly updating with incredibly low latency to give us a snapshot of the data in that very second.
The core thing to remember when it comes to real-time data is that what ‘real-time’ means will vary from company to company. In this analytics system, real-time refers to a time threshold for data to be delivered to a company. For one business, this threshold to qualify as real-time could be in milliseconds, while in others, it could be in minutes.
Real-time data always refers to how quickly you can process data. If a business has a certain defined threshold for what qualifies as real-time data and the processing time does not meet that requirement, it can no longer be called real-time.
With that in mind, real-time data could actually have a vastly different meaning in different organizations. But, most of the time, it refers to the processing and delivery of data analytics in time segments of under a second.
Businesses that need access to real-time data, such as financial or healthcare institutions, will invest in cloud data warehouse architecture that supports real-time capabilities.
What is streaming data?
Streaming data is a form of data generation and processing that is defined by its ability to continually produce. While real-time data suggests a start and end process, often in under a second, streaming data is simply the continual movement of data into a business, through data pipelines, and into an analytics product.
Streaming data will continuously generate data from different sources, processing it as quickly as possible and then outputting it for a business. This method of interacting with and processing data is the direct opposite of batch data processing. Batch processing is where companies ingest data in smaller batches, typically occurring at regular intervals.
For example, if a business processes new data every 10 minutes, then this would count as batch data processing. However, if they were constantly processing new data without a start and end, this would qualify as streaming data. That considered, streaming data often contributes to real-time data architectural capabilities. However, as streaming data doesn’t need to process data under a certain latency, it wouldn’t technically count as a form of real-time data.
What are the core differences between real-time data and streaming data?
Part of the difficulty of telling these two forms of interacting with data apart for many businesses is that they both offer a range of similarities. Both deal with the delivery of data to organizations and result in a high degree of agility when it comes to analytics environments.
Yet, there are a number of differences that clearly mark these two as different. Here are the core distinctions you should look out for.
Method of generation
Real-time data processing makes data available as quickly as possible. However, even though the latency requirement is extremely low, this doesn’t mean that a business has to continuously process data. Real-time data could actually use a very fast latency of batch processing to meet the requirements defined by the system. Due to this, streaming data isn’t always used or needed in real-time sources of data processing.
Streaming data, by nature, is a continuous process. Businesses collect and generate data from several connected sources concurrently, evoking a continual flow of new information that the business can use. This tendency to always want new information has made many businesses jump directly onto the streaming bandwagon.
However, unless a business has a direct use case that needs real-time data, then constructing streaming pipelines to enable the rapid ingestion of data may be an expensive cost for little return.
Use Cases
Real-time data is useful when you want to provide a seamless user experience that relies on updating information. For example, when a user calls an Uber, the application instantly begins to connect them with a driver, with any updates to who is driving them, their route, or available cars in the area instantly logging to the user’s phone.
Alternatively, real-time data is useful in predictive analytics platforms, allowing businesses to update risk factors and keep their workforce safe. When combined with risk analysis platforms, real-time data can prevent accidents, notify managers when changes are needed, and keep things running smoothly. Real-time data is vital here as the lowest possible latency will ensure that businesses have as much time as possible to respond to errors, mistakes, or critical situations.
Streaming data, on the other hand, is used when businesses will continue to need an updating form of data long into the future. It’s less about having access to real-time data and more about always having a source of new data to inform decision-making. A wonderful example of this is in stock trading, where multiple sources converge to give traders a more holistic picture of the present price of stocks and shares.
Final Thoughts
While it is true that real-time data and streaming data are more similar than different, the core differences in how they operate, deliver information, and function over time are important to remember. However, by understanding the differences between real-time data and streaming data, we can use more precise language when discussing data infrastructure.
Over the next few years, we’re likely going to see even more developments in the realm of live-data analytics, with these two methods of interacting with data leading the way.
