What is streaming analytics?

Streaming analytics is the process of deriving insights from a real-time data stream. It uses continuous queries to analyze data from multiple streaming sources. Health monitoring systems, traffic monitors, web activity logs, and financial transactions are just some of the sources of stream data. Streaming analytics help organizations to recognize critical events and respond to them quickly and efficiently.

One example of the practical application of streaming analytics is in the healthcare industry, where data from health-monitoring can be used to detect a person's imminent health risks. Assume that a health-monitoring device continuously senses the blood pressure of a person and sends it to a stream analyzer. If there is a critical event, such as a high blood pressure reading, it will quickly be detected and reported. It is important to be able to analyze such data in real-time so that a potential health crisis can be averted.

Another application of streaming analytics is in the manufacturing industry. Sensors that monitor the condition of the manufacturing equipment continuously generate and transmit data to a stream analyzer. The stream analyzer then examines this data on the fly to detect any possible malfunctions, preventing what could otherwise be significant and costly damage.

What’s the significance of streaming analytics?

In this era of information explosion, data is a crucial asset for organizations. Every organization has elaborate data warehouses and systems for analyzing historical data. In recent years, with the advent of the Internet of Things (IoT), the nature of data is continuously changing. The Internet itself is a major source of stream data. As the number of internet users skyrockets every day, so does the number of eCommerce customers. Traditional data analysis systems cannot handle such huge volumes of continuous, real-time data.

The number of data sources also has increased tremendously in recent years. IoT has introduced millions of data generators that are geographically separated. It is crucial for organizations to combine all this data, clean it, correlate it, and analyze it. In a nutshell, the recent technological evolutions have made streaming analytics a mainstream data processing technology.

How do streaming analytics work?

Analysis of real-time, moving data requires a system that is fast, scalable, and fault-tolerant. While multiple stream analyzers with different architectures are available commercially, the general workflow of a streaming analytics system is more or less the same. We can broadly divide it into the following steps:

Continuous data collection

The stream data originates from multiple sources that are geographically separated. In the case of the IoT, internet logs or financial data, sensors can be located in a wide variety of places. The first step of stream analytics is the continuous collection of multiple streams, buffering them temporarily, and then passing them on to a stream analytics core.

Streaming integration

Streaming integration is a pre-processing step before analytics can be applied to a data stream. Stream integration consists of multiple components; those may be applied together or individually on a stream.

Filtering: The incoming data stream needs to be cleansed, removing any unwanted components of the data. For example, the data stream of a financial transaction may contain additional information irrelevant to the analytics, such as the details of the items that were purchased. Filtering can remove these unwanted details, as well as any other pieces of duplicate data, from the stream.

Aggregation: Even though the data stream is continuous, an analytics system might process it in small windows of about thirty seconds. Aggregation collates some aspects of the stream data and presents it to a stream analyzer as a single data point. For example, if a user clicks on various links on a webpage, a different event is generated every time the user clicks a link, meaning that the data stream will consist of multiple such events. What the aggregator does is look at the data stream in a time window, and if there are five click events from a user, it combines them into a single event that says the user clicked five times. Aggregators help to reduce the volume of the stream data by bundling multiple events into one.

Data Enrichment: The stream data, which is real-time and continuous, might lack context. For example, in case a health monitor from a particular user generates continuous signals, the stream will only have a unique numerical identifier for the user. The data enrichment adds additional information such as the user’s age or other vital data so that the stream analyzer gets more context with which to work on the data.

Streaming Analytics: Once the streaming integration component has processed the incoming data, it’s the streaming analytics engine’s turn. In traditional data processing, often, an analyst writes queries and runs it against a database. In streaming analytics, however, a set of standing queries is used instead. The streaming data continuously passed through is a query, generating continuous results. Multiple tasks come under streaming analytics.

Multi-Stream Correlation: Multi-stream correlation is a technique that can find the relationship between two or more data streams. For example, consider the stream data that conveys the real-time stock value of two companies. A stock trader might want to measure the dependency of one stock price on the other. The same requirement might apply to thousands of streams. Hence, stream correlation performs a near real-time comparison of thousands of data streams.

Anomaly Detection: The majority of IoT devices send a continuous stream of data into a processor, which can include anomalies. A temperature monitor could be an example of an IoT device, and a stream analytics engine should be able to detect a high or low-temperature event, based on some previously set rules. This would constitute an anomaly, and a stream analytics engine should work fast in real-time to detect such anomalies and report them to a higher layer.

Pattern Matching: One example of pattern matching would be if a security agency needed to constantly monitor internet usage data for a particular set of words that had been deemed a threat. A stream analytics engine could continuously analyze the incoming activity logs to recognize such patterns.

Data visualization and real-time actions

It’s not sufficient to simply analyze a real-time stream and derive insights. The streaming analytics engine needs to pass the data to an upper layer so that the insights can be turned into actions. Data visualization systems convert the output of the stream analyzer into a human-readable format. Organizations can make use of such visual dashboards to easily understand and take actions based on real-time data. Organizations also use the insights from streaming analytics for triggering an alarm or passing on an alert. For example, if a stream analytics engine finds an anomaly in the motion sensor data from a home security system, it can trigger a burglar alarm. Likewise, if a health monitor shows undesirable data, doctors could be alerted to the real-time health of a patient.

Streaming analytics vs. traditional data analytics

Traditional data analytics systems work on data that isn’t time-critical. Therefore, they can afford delays in processing. Streaming analytics works on real-time, continuous data that may turn stale after some time.

Streaming analytics often use in-memory processing, where the entire data is stored in a random-access memory. In traditional data analytics, only the relevant portion of data is loaded to memory from time to time.

In traditional data analytics, there is a static database, and queries are often created on the fly and run against the static database. In streaming analytics, there is a long-standing query repository that runs against the continuous data.

Benefits of streaming analytics

Streaming analytics helps a wide range of businesses and organizations, enabling them to derive valuable insights from time-critical data and take immediate action.

Risk mitigation

Because organizations are able to use stream analytics as a means of identifying anomalies in incoming data and taking immediate corrective action, this makes stream analytics a vital risk mitigation tool. Stream analytics can be used for the timely detection of financial fraud, manufacturing malfunctions, disruption of brand image, security breaches, and other compliance issues.

Customer experience

Stream analytics help companies to examine customer data and respond to customer needs in real-time, greatly enhancing the customer experience. Continuous analysis of real-time social media data helps organizations to address customer issues and push relevant content.

Competitive edge

Streaming analytics helps organizations to act proactively based on real-time data. This is a huge benefit, especially in the case of stock markets. A few seconds might be crucial in making or losing millions of dollars. Also, streaming analytics helps organizations to detect trends in the market that can be used to increase profitability.

Challenges of streaming analytics

Time constraint

Stream data is time-sensitive, in that it may lose relevance over time. As a result, streaming analytics systems should be able to quickly process the incoming data. Any delay in processing could cause issues, including operational and manufacturing losses, customer dissatisfaction, health and safety risks, and reduced productivity.

Solution: Often, stream-analytics systems use in-memory analytics and parallel processing for speed and fault-tolerance. Distributed systems ensure enough redundancy that even if one of the systems fails, the analytics do not have to stop.

Irregular data volume

Just like the stream data content changes in real-time, the volume of data also changes. Sometimes the data inflow could be moderate, but with unpredictable spikes in the data. While this is negligible in the case of a single stream, when thousands of data streams are combined, the fluctuation in data volume becomes significant and poses a challenge for streaming analytics systems.

Solution: The streaming analytics system must be scalable in real-time. A system that can scale up and down based on the real-time demand is known as an elastic system. Streaming analytics systems need to be elastic, and should be able to selectively use resources depending on the volume of the input data.

Interpretation of real-time

The phrase “real-time” is vague, and its definition changes depending on the application. Does real-time mean the analytics results should be available within milliseconds after receiving the data stream? Or should the system deliver the data only after a few seconds?

Solution: When an organization dealing with streaming analytics enters into a service level agreement, they should clearly interpret the definition of real-time. Clear service level agreements (SLA) will help organizations to interpret the definition of real-time correctly.

Patient analytics screenshot

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