Reliable, fast access to big data is crucial to the success of any business seeking to improve its competitive edge. But how fast does access need to be? To stay ahead of the curve, companies need to be able to work with data in real-time, engaging with and utilizing data as it is entered or received. Stream processing makes that possible.
What is stream processing?
Stream processing, also known as data stream processing, is a method of handling data in which information is analyzed and organized as it is generated. Once data is produced from an event (such as user activity on a website or mobile app, or via a sensor) it is instantly processed and delivered to its source destination.
Many types of data are time-sensitive. In other words, their relevance and value are greatest at the time they are generated, but diminish as they age. Think of the fast-moving world of retail sales; customer feedback can reveal critical trends in consumer preferences or help detect problems with the ordering process. In order to remain competitive, a company needs to have access to data as quickly as possible in order to make fast decisions affecting their supply chain or to solve issues with their point-of-sales systems.
Often referred to as “data in motion” or “streaming data,” stream processing simplifies and speeds up data analysis in order to provide fast access to information. Stream processing allows data to continuously flow through the processor so that it can be applied when it is most meaningful, or stored for future use.
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Batch processing vs. stream processing
Before going any further, it might be helpful to compare stream processing with its predecessor, batch processing. Although each method has its place in a modern approach to data management, batch and stream processing differ in the way they collect, process, and deliver information.
Batch processing collects data over a period of time, and then feeds that data into its destination for processing at a specified interval. For example, a small business might batch process its credit card transactions once a day, or a government agency may collect economic data for entire month and then process it all at once. In contrast, stream processing works in the moment, collecting data piece by piece and then sending it to its destination in near real-time.
Batch processing is best used for very large amounts of data, or data from legacy systems that are not capable of streaming. For example, information on a mainframe is typically already processed as a batch, so the conversion to stream processing can be a challenge. Batch processing is also typically used when time is not a critical factor. Batch processing is most helpful when processing large volumes of data is more important than getting results from analytics quickly.
Stream processing is used when near-instant results are desired. For example, it is commonly used in situations that require fraud detection in order to discern suspicious activity as it happens.
How stream processing works
Let’s talk about milliseconds. That’s how quickly some stream processors can analyze and extract insights from data. But how exactly does it work?
The stream is a never-ending flow of data. New data arrives continuously and is analyzed instantaneously. A single record in a stream is called an event. Stream processing code directs the processor to collect data generated from events as they arrive, as well as to collect data that will arrive in the future. It then directs the processor to analyze the data and deliver the output (processed data) to the appropriate location. During stream processing, the following steps take place:
- Data is collected. As data is generated by events, it is gathered for processing.
- Data is scaled. Stream processing shuffles through large amounts and retains only that which is useful.
- Data is cleansed. Stream processing sorts through and removes failed data.
- Data is organized and delivered. Targeted storage areas include databases, micro services, and messaging systems.
Stream processing uses the collected information to allow the user to identify patterns, inspect results, compare multiple levels of focus, and review data from multiple streams all at the same time. It is especially suited for identifying patterns in time series data.
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Stream processing, IoT, and the cloud
Advanced data solutions such as stream processing are increasingly relying on cloud-native technologies. The advantages of cloud technology include increased security, faster processing speeds, and the ability to scale. Cloud application platforms streamline operations, emphasize adaptability by integrating data from multiple sources, and, for many companies, the cloud offers additional benefits in terms of total cost.
The cloud isn’t the only technology that’s making an impact on data processing. The Internet of Things (IoT) is now allowing businesses to collect data from wider range of sources than ever before. Wearable technology, smart appliances, health technology, and the quickly expanding universe of connected devices is continually generating valuable data. Stream processing allows companies to make the most of that data by analyzing and processing it in real time.
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Stream processing examples
Data streaming makes big data accessible in the moment. It can sort and assign tasks based on results as they are entered, helping a company act quickly in order to capture potential customers, as well as respond to the needs of existing clientele.
Babou — using stream processing to analyze 650K events
French discount retailer Babou created a barcode system and added a loyalty card that generated a large amount of data that was difficult to process. Working with Talend, Babou developed a system to retrieve and analyze 11 categories of transactions and 650,000 monthly credit card events. As a result, Babou now utilizes data in real-time in order to streamline operations, increase revenue, enhance customer experience, and improve response times.
Credit Agricole — accelerating business operations
Credit Agricole, a major player in the European credit market, needed a platform to accelerate all consumer finance operations, from the initial application stage to credit issuance, across all digital channels. Improving the customer experience was key, so the company embarked on a digital transformation.
Talend helped them develop an efficient system for data ingestion and processing that monitors the company’s website performance and enables detailed analysis of internet users’ click stream patterns. Now Credit Agricole customers now have the capability of using their social network data (with permission) to speed up the online credit simulation process.
Getting started with stream processing
If you’ve decided it’s time for stream processing, you know the value of time and the impact of speed on your business. You need a data management solution yesterday so that you can see results tomorrow.
Talend Data Streams is the self-service, cloud-native, data integration tool to get you up-and- running fast. Get started with the free edition of Data Streams today and see for yourself the difference stream processing can make for your business.