If you say the word "streaming" to anyone today, many of them will think you're referring to content from streaming services—Netflix, TV on Hulu, music on Spotify, and so on. Less recognized, but equally important, is streaming data.
The steady increase in the volume of on-demand media, the continued proliferation of personal mobile devices, and the rise of "always-on" culture in work and life are major reasons why streaming data is fast becoming one of the key data trends of the early 21st century.
By harnessing data from the constant flow of information within the data stream, enterprises can tap into actionable, on-demand insights that help them provide real-time services, improve the customer experience, and boost operational efficiency. Using hybrid cloud or multi-cloud platforms and leading-edge analytics tools can help businesses derive even greater value from their data streaming processes.
What is streaming data?
The term "streaming data" refers to data that is generated continuously in real time. It's also sometimes called "continuous real-time analytics." In an enterprise context, data could be streaming in from hundreds or thousands of different sources, all at the same time. These points of origin include everything from applications and websites to devices and sensors connected to the internet of things (IoT).
Common information-generating events that eventually make their way into a data stream include e-commerce purchases, social media activity, server transactions, and tracked locations, among many others. Because of this, streaming data is characterized as event-driven, rather than query- or request-driven, and the processing techniques used to aggregate, cleanse, and analyze this data fall under the category of event data processing. A single stream often contains thousands or even millions of records. While the data doesn't have to be downloaded for enterprises to make use of it, storage—and processing—still need to be taken into consideration.
Why data streaming matters: Key benefits and use cases
Streaming data pipelines and the various techniques used to collate, filter, and examine data from each stream are, of course, not the only form of enterprise analytics. But they have grown immensely important in recent years as demand for real-time services has skyrocketed.
Consider an application like fraud detection. Customers don't want to lose their money to scammers, and banks and creditors want to take as little a financial hit as possible while protecting clients. That's why major banks and credit card companies want to have constant, real-time visibility into all customer transactions. This is the best way to spot patterns of suspicious activity on credit or debit cards and initiate blocks as needed, and it's made possible by leveraging streaming data.
While more traditional data ingestion and analysis methods like batch processing are not without their uses in modern analytics, fraud detection simply cannot be one of them—it doesn't work nearly fast enough. Results or insights have to arrive in real or near-real time, and only streaming analytics processes and tools can deliver on that need. Streaming data architecture is built to handle an endless stream of event data, because it can be easily scaled—especially when the components of that architecture are cloud-based—and quickly detects patterns that lead to critical insights. Artificial intelligence (AI) and machine learning (ML) are key elements of most data stream analysis tools.
In addition to fraud detection, some major areas where big data streaming in real time can have immense value include:
- Edge computing: Processing data from edge sources and integrating it in real time allows end users to have the most low-latency experience possible with critical business apps.
- IT and security: Monitoring IT systems is much simpler with real-time analytics. Along similar lines, using streaming data analytics for security information and event management (SIEM) ensures comprehensive visibility into key metrics and patterns that can help cybersecurity professionals spot vulnerabilities or active threats.
- Finance: Tasks in the finance world ranging from routine checkups on credit scores to high-stakes, real-time stock trades are made possible with streaming data.
- Industrial maintenance: With data collected in real time from sensors that monitor machine performance, facility managers can establish preventive maintenance policies that minimize downtime and extend the lifespan of equipment.
Two recent real-world use cases present even more concrete evidence of streaming data analytics' value:
Saudi Telecom Company (stc)
Streaming data is essential to creating personalized experiences for more than 41 million users of stc's mobile telecom services. The company uses data from real-time customer interactions across digital, mobile, and voice channels. This data stream leads to insights that help reduce call setup times, prevent unsuccessful dropped calls, maximize speech connection quality, and improve internet connectivity and bandwidth speeds.
Quality service and operational efficiency help keep customers satisfied—more likely to upgrade, upsell, or accept a cross-sell. In turn, stc can boost revenue, increase its customer base, and create exciting new growth opportunities.
France's flagship air carrier is constantly working to adapt to the evolving needs and overall experience of the approximately 100 million customers it serves each year. While passengers are booking, checking in, arriving, or rescheduling, Air France's analytics team uses streaming data to analyze everything from pre-booking data—e.g., web searches—to post-travel communications and unstructured social media data. This allows Air France to quickly identify ideal promotional opportunities for every customer, minimize and manage churn, and optimize web and call center experiences.
Potential challenges of streaming data processing
A streaming data pipeline continuously delivers information at a massive scale. The specifics of that amount can surge suddenly—one minute, it's something that could be handled by more traditional data architecture, the next, it's a large volume that threatens to overwhelm the system. Examples of this include spikes in stock purchases and sales near the end of trading hours, as well as surges in online gaming during the evening. In these and other, similar situations, if the data stream isn't designed with scalability in mind, sequencing, consistency, and availability can all suffer, and severe cases can cause significant disruptions to services.
Additionally, because a typical streaming application relies on data integration—drawing from many different data sources and locations—it's possible that a single point of failure could cause trouble across the entire enterprise data ecosystem. Every data engineer, scientist, and executive who works with stream processing must be prepared for such a possibility and work to design a system that can mitigate it.
It's also critical to note that there is no one-size-fits-all tool for stream processing: Open-source tools are often involved and can be very useful, but they must be used in conjunction with the right complementary technologies, including a versatile data analytics engine. If even one of the components isn't interoperable with its counterparts, then the streaming data "solution" will be just another problem.
Making the most of streaming data analytics
In the last few years, streaming data analytics has become important for many enterprises. As technologies like AI and ML grow more commonplace, and consumers and business users alike expect real-time operations in more aspects of their lives, the value of streaming data will only rise. Trends like the rise of 5G and the continued proliferation of the IoT will also drive up the value of stream processing. That's why it's so important for your organization to put together a reliable and interoperable data streaming technology solution now, so you don't end up being behind the curve in the near future.
Processing and extract, transform, and load (ETL) tools are the foundation of streaming operations, and open-source tools fill these respective roles quite well. A data lake, meanwhile, serves as an ideal low-cost storage method for the massive stores of data involved in most streaming analytics use cases. Between ETL operations and the storage phase, a cloud-first data analytics platform like Teradata Vantage is necessary to make sense of the stream and derive the most impactful actionable insights from it.
Vantage gives enterprises comprehensive visibility into streaming analytics workloads. The platform brings workloads into the cloud, where the elastic resources that are essential to low-latency real-time processing can be harnessed. It's compatible with all major public cloud providers and their streaming data tools, as well as open-source solutions. Organizations that use Vantage alongside Teradata's Data Stream Architecture (DSA) backup solution can further optimize stream processing by eliminating redundancies and reducing the burden on storage. Harnessing the full power of your streaming data can improve operational efficiency and the customer experience in the short and long term.
To learn more about Vantage's capabilities as part of a streaming data architecture, check out our blog. Posts by our experts cover subjects including compatibility with AWS Glue Streaming ETL and Kinesis Firehose.