Get more value from your data streams with Stream Processing

Respond immediately to events in your landscape, process your continuous data, and process enormous amounts of data with stream processing. Discover how stream processing can be of value and help you achieve your goals.

In this blog

In this blog, we want to discuss one of the ways to process real-time data within eMagiz iPaaS, namely stream processing. Stream processing allows you to process large amounts of data within a very small timeframe after receiving it. In this blog, we explain why stream processing can be beneficial for your organisation and we elaborate on some typical use-cases.

3 benefits of stream processing


As we discussed in one of my previous blogs stream processing is a great tool to obtain valuable insights from data streams while that data is flowing. But when do you need to process data within a stream? Overall, we identify three key situations when stream processing offers substantial benefits compared to other, non-realtime big data processing methods, such as batch processing.

First, stream processing is great for when you need to respond instantaneously to incoming data. Some information is more valuable when it is immediately derived from the data and loses its value over time. In a scenario where an abnormal event occurs, you want to take immediate action. Stream processing allows you to immediately react to events that occur, possibly minimizing potential losses or enhancing customer experience.

Second, stream processing is useful to process continuous datathat is less suitable to be processed on a per-event or batch basis. As an example, batch processing is less suitable to detect the length of a user session based on click events on a website, as these events would be distributed across batches. Processing the data stream allows to you detect patterns in continuous data as they emerge. This primarily applies to time-series data, such as metrics, IoT data, and transaction logs.

Third, stream processing is useful for pre-processing data, as it supports efficient processing of large amounts of data using a limited set of resources. Batch processing requires building up a large amount of information and then processing all the information at once, requiring substantial computational resources for a short period of time. With stream processing, you only need a limited set of resources as processing

 is a long-running continuous process. Furthermore, stream processing only processes data coming in and discards it afterward. It does not store the data. This is especially applicable for use-cases where large amounts of data with low relevance are produced, as all raw data can be immediately discarded when useful, clean data is extracted from it.

While there are some obvious benefits, there are also restrictions for using event stream processing. One of those restrictions is querying specific data, for instance looking up a specific value (such as finding customer data using his or her customer ID). Additionally, there are restrictions when there is a need to repeatedly iterate over a dataset, for instance to find missing data. A different example of this is in the field of machine learning. While stream processing can be used to apply machine learning models to process streaming data, it is less suitable to train and develop machine learning models as this requires access to a full dataset. In these instances, you can still benefit from stream processing to pre-process your data before transporting it to your data lake for further processing.

Typical use-cases for stream processing

But how can stream processing actually provide value to your organisation? We established some use cases explaining how it can add value to your business in particular situations. We elaborate on how stream processing can enable you to make decisions in real-time decision, how it can increase your data quality and how it may enhance your customer experience.

Real-time decision making

When processing big data sources (such as IoT data, metrics, or log data), its common to simply store all data in a data lake so that it can be used for analytics and data-driven decisions at some point in the future. However, this creates a gap between when a data-driven decision is made and when the events that drive this happen, decreasing the value of the decision. Stream processing can support real-time decision-making based on an incoming flow of data to instantly respond to events. A key application for real-time decision-making is security and monitoring to detect hacking attempts, downtime, and other incidents that impact the stability of your IT systems as they happen. Quickly intercepting these events allows organisations to immediately take action to reduce the impact of these events, for instance by shutting down certain systems or sending out an alert.

Increase data quality

Stream processing can be used to reduce stress on your data warehouse and lower the barrier for putting your data to work. Traditionally, working with big data sources results in a lot of raw data being stored in your data warehouse until at some point in time the data is cleaned, enriched, structured and stored in another place for use, such as machine learning or data-driven decision making. But why delay the processing up until this point? By using stream processing, it is possible to immediately pre-process the raw data, even before it is stored. This way you only store high-quality data that is ready to be used for analysis when needed. This lowers the barrier for putting your data to use, as consumers can explore and use the data without the need to pre-process it first.

Enhance customer experience

Finally, stream processing can support new applications based on live and continuous data. There is a wide range of opportunities for integrating live data into applications. For instance, stream processing can be used to discover trends in real-time, such as trending stocks or frequently bought products. However, the applications are broader than just discovering trends, it can also be used to support flight tracking, package tracking, or real-time building of search indexes. Overall, stream processors can help you with ingesting massive amounts of raw data and processing this into usable high-level information which can be used to further enhance the experience of customers.

In conclusion, stream processing is a powerful tool for a wide variety of use cases from real-time decision making to enabling continuous ETL. eMagiz iPaaS can help you develop and manage the infrastructure required for stream processing, so you can focus on the business objectives. Additionally, eMagiz can help you follow-up on the insights obtained during stream-processing by integrating with any back-end system in your application landscape. This allows your organization to turn your insights into actions.

By Mark de la Court, Product Owner @ eMagiz