Streaming Analytics: A Simple but Detailed Introduction

Streaming analytics is a powerful platform for system observability. It’s a way to monitor your infrastructure and respond to real-time events.

Your clients expect you to respond to issues quickly. They expect your systems to always be available. If you want to remain competitive, you need to stay on top of your systems and respond to issues as soon as they occur. As a matter of fact, preventing issues would be an even better option! Streaming analytics makes these things possible.

In this post, we’ll discuss what streaming analytics is, how you can take advantage of it, and how Scalyr can help you get started quickly.

A computer screen with a play button signifying streaming analytics

What Is Streaming Analytics?

Streaming analytics is a form of real-time monitoring. It analyzes data in real time, rather than processing it in batches or bulk after the events have occurred. Many tools already graph monitoring and logging data streams. They generate alerts based on simple thresholds or basic calculations. Streaming analytics goes a step further. It operates on data streams with continuous queries. Then, you can display updated results as new data arrives, or you can use it to take action.

Business analytics and business intelligence have both been around for a long time. But those legacy systems are limited to batch processing. They ingest log data in large chunks, often after many hours, and generate reports. This information is valuable. It can go a long way toward helping you increase profitability and avoid problems. But it’s not enough. You need to be able to process events in real-time, correlate them, and respond when necessary.

Streaming Analytics Advantages

Streaming analytics can help your business in many ways. By harnessing your company’s data stream with continuous queries, you give yourself a competitive edge. You’re able to combat emerging threats as they arise. Plus, you can capitalize on opportunities before they disappear.

Manage Business Operations in Real Time

Analyzing activity after the fact tells you a lot about your business. But, by analyzing your business processes in real time, you can catch problems. You can also act on unique circumstances as they occur.

For example, streaming analytics can show you a sales spike as it happens. With this knowledge, you can arrange to increase your inventory or adjust the price. Learning about this spike in an end-of-day report is too late. You’re reading about an opportunity after it’s gone.

Streaming analytics avoids missed opportunities.

Real-time statistics tell you when the number of visitors to a sales page suddenly increases. Here again, you can take immediate action. Maybe it’s time to adjust the price, or the site navigation to drive more clicks. It could be a chance to introduce a new sales incentive. Streaming analytics means you avoid missing an opportunity to create more revenue.

Streaming analytics is a competitive advantage. Leave it to the competition to rely on batch processing; with streaming analytics, you’ll always be more agile than they are. Adjusting to real-time events makes customers happy and increases revenue.

Respond to Security Threats

Security is a problem for all businesses. You must be able to catch issues as they happen. Even being a few minutes late might mean the difference between falling to an attack or surviving.

With streaming analytics, you can recognize and respond to anomalous events. This goes beyond the scope of simple intrusion detection. For example, a well-crafted query can correlate a denial of service attack combined with a high number of failed login attempts.

This advantage isn’t limited to online businesses. Point-of-sale (POS) and inventory systems generate data streams, too. They can be used to identify issues and unusual conditions. As we’ll discuss below, streaming analytics isn’t just for online business.

Visualize Data

Graphing real-time information from monitoring systems is nothing new. But graphing the results of continuous queries takes it to the next level. You’ve probably already identified your key performance indicators (KPIs.) With streaming analytics, you craft queries that calculate them. Then you can graph them to visualize them as they change.

Streaming Analytics Applications

IoT Monitoring

IoT devices produce streams of data. These devices may be cameras, alarm sensors, or other equipment. Regardless of what they are, they produce data that tells you what’s going on in your stores or facilities.

When you apply continuous queries to a device stream, you can observe changes over time. Depending on the device, a set of changes may indicate an important event. So, streaming analytics is already an advantage, with one device.

But you can also combine data between multiple IoT devices, too. For example, you could combine motion sensor inputs and object recognition data from a camera. With these streams, you could track movement in a specific area in a factory.

Web Analytics

Web analytics has been available via free products for many years. Most of these products use batch processing. Most only support queries on historical data. Google Analytics has added a real-time component to its analytics product, but it has limitations. A few support real-time data processing, but their queries are rudimentary.

A true streaming analytics platform can query multiple data points in real time. With that kind of intelligence, an e-commerce company can tailor a client’s experience with navigation, inventory, and pricing based on where they are from and how they use the site.

Fleet Management and Logistics

The ability to report GPS information from fleet vehicles is more or less expected these days. This information is invaluable for scheduling pickups. It’s useful for managing deliveries. With real-time monitoring, you can detect issues that require rescheduling. And, of course, you monitor employee compliance.

But with streaming analytics, you can take your real-time analysis to the next level. For example, you can correlate traffic and weather information with fleet data, so you can use this information to reroute vehicles to avoid delays.

You can also combine inventory information with POS data to improve logistics. This means you can respond to a sudden upturn or downturn in sales by adjusting delivery schedules.

Streaming analytics takes you to the next level.

Inventory and Point of Sale

For a long time, e-commerce businesses have had an edge. They could adapt to changing conditions much more quickly than brick-and-mortar stores. That’s not the case anymore. With the data provided by IoT sensors, POS systems, and client cell phones, they can adapt, too.

All those devices generate data streams. You can craft queries about where products are located, add which products are selling, and even see where clients are. Then, based on the results, you can reroute inventory, tweak pricing, and create promotions on the fly, just like your online competitors.

How Scalyr Can Help

Scalyr’s event data cloud lets you ingest and search massive amounts of data in real-time, store it, and access it via APIs. While the event data cloud is not a streaming analytics platform, it does share several properties with one.

The event data cloud facilitates the extraction, transformation, and loading of data. It gives you a way to process that data in real time. But it’s not built for analyzing a user’s web experience and modifying content. However, the event data cloud excels at supporting complex data, which it can ingest in real time. Then, it can provide results to ad hoc and predefined queries.

Streaming Analytics Gives You an Edge

In this introduction to streaming analytics, we’ve discussed how it uses continuous queries to process data streams in real time. We’ve covered its many benefits and how you might be able to use it to improve your business’s profitability.

And we’ve discussed how Scalyr’s event data cloud might be able to help you get started with processing real-time data queries. Sign up for a free trial today!

This post was written by Eric Goebelbecker. Eric has worked in the financial markets in New York City for 25 years, developing infrastructure for market data and financial information exchange (FIX) protocol networks. He loves to talk about what makes teams effective (or not so effective!).