What is streaming data?

To understand streaming data better, it’s best to start with the concept of streaming itself. Streaming refers to a non-stop flow of data that does not have a starting or an end point. This constant flow of data can be used without ever needing to download it. It is similar to the flow of a river. Lots of tiny creeks, tributaries and water bodies flowing at varying speeds and intensities to merge into a single river, with no beginning or end from your vantage point.

Similarly, data streams originate from a range of sources in numerous formats and volume intensities. These sources can be apps, networked devices, server log files, online activities of various kinds, as well as location-based data. All of these sources can be collected in real-time to form a single main source for real-time analytics and information.

One example of streaming data is a ride-sharing app. If you make a booking on Uber or Lyft, you will be matched with a driver in real-time, and the app will be able to tell you how far he is from you and how long it will take to get to your destination based on real-time traffic data. Other examples of streaming data include real-time stock trades and retail inventory management.

How streaming data works

The concept of data processing is not a new one. In its earlier years, legacy infrastructure was more easily structured because data was generated from much fewer sources. Entire structures could be created in a manner that would work on specificity and unification of data and the source structures.

Modern data, however, comes from an infinite number of sources, which can be anything, including hardware sensors, servers, personal devices, apps, and internet browsers. This makes it impossible to regulate or enforce structure for data or allow for the control of the intensity and frequency of the data being generated.

To be able to handle modern data flow, applications with the ability to analyze and process data streams one data packet at a time, in a sequence, is necessary. Every data packet that is generated will also need to have the source and timestamp with it, and this helps applications work with data streams.

The applications that are used for working with data streams need two main functions: storage and processing. For storage, it must have the ability to record massive streams of data in a sequence and in a consistent manner. For processing, the software should be able to handle interaction with storage, consumption of data stored, analysis of the stored data, and running required computation on the data.

There are several considerations and challenges that come with building data streams, and today there are a range of platforms as well as tools that organizations can use to help companies build their streaming data infrastructure. Data streams play an integral role in big data and provide the basis for real-time analyses as well as data integration and data ingestion.

How legacy batch processing differs from real-time streams

Compared to the early days of legacy batch data processing methods, modern real-time streams can be quite different. In legacy batch processing, data is collected in batches, after which it is processed, stored or analyzed depending on the need. In streaming data, the data input flow is continuous, and it is processed in real time. There is no waiting for data to arrive in batch form.

Data today flows in constant streams and comes in a variety of volumes and formats, from scores of locations and from the cloud, on-premises or even a hybrid cloud. Legacy data processing methods have become by and large obsolete in present day situations. Organizations today use real-time data streams that are up-to-the-millisecond latest, which offers businesses a wide range of ways to transform the way they work.

Benefits of streaming data

Here is a look at how streaming data can be applied to help real-world work situations.

Enhanced alerting

The immediate and most obvious benefit of the abilities provided by streaming data is how it helps streaming analytics. There is instant feedback when an event, anomaly or trend begins to occur. Alerting is not a unique aspect of streaming but the simple fact that those receiving alerts can immediately action a response makes it an important one. This can happen because, unlike batch processing, there is no delay technologically. Here are some examples of how alerting can work:

• In the case of cyber-security, streaming data can be used to flag out-of-character behavior in the course of an investigation. A large number of cyber-security environments are opting for machine learning to help identify potentially suspicious behavior as soon as it may occur in a network. Making use of alerting visualizations along with machine learning outputs is the best way to enable a wide group of cyber-analysts to detect threats. This way a business can expand its security network to a wider range of people than just security experts and developers.
• The retail industry also benefits immensely from alerting. Every store prioritizes different things and information technology teams should be given priorities for which code can be customized. Streaming data can be used to detect things such as low inventory or unusually high customer interest. Analytical tools are triggered to send out alerts to non-technical staff rather than the technical staff, and this enables positive responses where it matters the most - on the shop floor.

Using historical and data stream based analysis in tandem

There are numerous situations where historical data is used alongside real-time data analysis to provide organizations with a more comprehensive picture of their business. The best use case to explain this would be in risk assessment for financial institutions. The processes take into account the whole circle of transaction—from past, which has already been executed, to present, involving changes, transfers or closures.

When placing a trade event in context, this means that the data on transactions from the event will help organizers understand patterns that are applicable to their larger set of portfolios. The information gathered from an analysis of historical and real-time data in this situation can mean the difference between success and a massive loss for future events.

Benefits in the creation of complete records

In almost every aspect of day-to-day life, business or otherwise, the Internet of Things (IoT) is the way forward, and is already in use by many organizations. However, the one big issue here is that multiple identical records can be generated from streaming data, resulting in a duplication of information. Keeping track of the data source, while essential, will result in the same information repeating multiple times. With thousands of source points, this can quickly turn problematic and render much of the data redundant. To make utilizing IoT a more viable option, what can be done is to put all the repetitive information in a single lookup table. Joining the data stream with the lookup table will help create a complete record without the issue of repetition.

We can see an example of this solution in action on an oil rig, with the repetitive information of manufacturer name and location. Placing these two details in a lookup table and joining it with the data stream with a key, such as ‘manu_id’, will save large amounts of data space. This key can then be used to determine if the location impacts various aspects such as wear and tear, performance capabilities, additional maintenance requirements, and more. Through the use of a lookup table, non-productive time can be reduced considerably.

Insights that cannot be found elsewhere

Currently, there is unprecedented interest and development centered on streaming technologies. This is being driven by technological advancements and further pushed by the realization that streaming data analytics brings in immense business value. Businesses that are looking for their next edge over the competition will turn to streaming data to gain insights that they cannot generate from their existing approaches to analytics. Some of the areas where this technology has the most immediately obvious beneficial applications include:

• The utilization of location data
• Fraud detection
• Real-time stock trades
• Marketing, sales, and business analytics
• Monitoring and analyzing customer or user activity
• Monitoring and reporting on internal IT systems
• Assisting with log monitoring
• Security Information and Event Management (SIEM)
• Retail and warehouse inventory across multiple channels
• Enhancing rideshare matching
• Combining data for use in machine learning and artificial intelligence-based analysis
• Open up newer avenues in predictive analytics

Challenges in building data streaming applications

As with most technological systems, data streaming also comes with its share of challenges. Here is a look at some of the difficulties associated with building data streaming applications:

Scalability in a working environment

In the case of a system failure, the log data coming in from every device can go up from a send rate of kilobits per second to megabits per second. When aggregated, the send rate can even scale up to gigabits per second. The necessary increase in capacity, resources, and required servers as these applications scale up and the amount of raw data generated increases alongside it needs to happen instantly. Being able to design seamless applications that can scale up in working environments streaming data is a demanding job that requires having to consider many different simultaneous processes.

The importance of sequences

Determining the sequence of data in a data stream is not a small issue. The sequence of data within a data stream is key to how well it can be utilized by applications. If developers are looking to debug an issue with a bot chat application, the sequence of the conversation is important to determine where things may be going wrong. Each line in the aggregated log review needs to be in sequence. The problem usually arises from discrepancies in the sequence of the data packet generated and the sequence in which the data packet reaches the destination point. There may also be differences in timestamps as well as the clocks of the devices that are generating the data.

Maintaining consistency and durability

Among the hardest problems with processing streaming data is its consistency and access. The generated data is often distributed to multiple data centers across the world. There are chances that by the time it is accessed in one data center, it may already have been used and made redundant in another. The durability of data when working with data streams on the cloud is also a constant challenge for developers.

Fault tolerance and data guarantees

It’s important to consider both fault tolerance and data guarantees when working with processing streaming data over distributed systems. When you have data coming in from numerous sources and locations in a range of formats and varying volumes, organizational systems need to be geared to prevent disruptions that may arise from a single point of failure. These systems should be able to store massive streams of data in a durable manner. Ensuring this is no easy task.

Any disruption in the constant stream of data also backs the system up. If the system cannot store the disrupted information and then have the capacity to catch up, the entire system is carrying a heavy burden of delayed data.

The future of streaming data

There has been and continues to be a rapid growth and interest in the use of software-as-a-service, mobile and Internet-based applications, and the use of data science and advanced analytics by a wide spectrum of organizations. Almost every midsize to large company has some form of a streaming data project that’s either ongoing or in the pipeline. All this is based on the desire to stay ahead of the game and analyze customer journeys, clickstream data, and several other use cases that can generate useful reports.

There was once a point when streaming data was concentrated around a small set of people within an organization—primarily big data engineers and data scientists. These professionals worked with incredibly complex skill sets and on streams such as Spark, Flink, MapReduce, and Scala. They worked in tandem with business analysts and business intelligence professionals, all with a primary focus on running SQL queries against relational databases.

As we step into a new year, this is poised to change. With more and more businesses relying on streaming sources, business users are going to want to be able to work with streaming data they are able to with other datasets—in the form of interactive dashboards as well as ad-hoc analytics, just as software development teams would. This will enable data being more accessible to all people across hierarchies in an organization.