Today’s world is fast-paced, and there’s no time to wait for anything. There is no tolerance for delays or inefficiency, especially about data that affects critical enterprises like manufacturing or finance. With the ever-increasing volume of data in various industries, it has become essential to find a processing system that is both agile and accurate. Stream processing has emerged as the perfect solution to this requirement.
This article will help you understand what stream processing is, how it works, and how it improves the existing block processing system in various use cases.
What is stream Processing?
Stream processing serves as a system of acting upon multiple data sets at the exact time that the data is generated. With the increasing adoption of technologies with stream, and decreasing RAM prices, “stream processing” has been redefined for more specific use cases.
Stream processing helps process a continuous data stream to find conditions over a short period after the data is received. The workflow is called a “stream processing pipeline” that generates the data, processes it, and delivers it to an endpoint.
Stream processing involves:
# Aggregations: calculations like standard deviation, sum, and mean
# Analytics: predicting future events based on data patterns
# Transformations: change of number into the format of a date
# Enrichment: creating more context and meaning by combining various data points with different sources of data
# Ingestion: inserting of data
Following are some of the main advantages of data streaming:
# Some insights derived from processing data are more valuable immediately after it has happened, while the value diminishes quickly over time. In such scenarios, stream processing provides faster insights, usually milliseconds to seconds after the trigger.
# Some data naturally occurs as a never-ending stream of events. Batch processing would involve storing the data, stopping collection at some time, and then processing it. You would then have to do the next batch and perform complex aggregation across multiple batches. Stream processing, on the other hand, handles never-ending data streams naturally and gracefully. You can use it to detect patterns, examine results, observe multiple focus levels, and simultaneously look at data from various streams.
# Stream processing also helps identify the length of a web session in a never-ending stream.
# Stream processing has less hardware requirement as compared to batch processing, and it also allows query processing through load shedding. Thus, stream processing is suitable for use cases where the answers need not be accurate, but just approximate.
# Sometimes, when huge data needs to be processed, storing it becomes impossible. Stream processing allows you to handle large amounts of data while retaining only the useful bits.
Stream processing v/s batch Processing
In the past, batches were used to process data contingent on a set schedule or an agreed threshold (for example, every hundred rows, every morning at 6 am, or when the volume becomes four terabytes). However, the pace and volume of data have both increased, rendering batch processing inadequate.
Rather than grouping and collecting data at a predetermined interval, stream processing applications perform data collection and processing as soon as the data stream is generated. Thus, stream processing permits applications to react to new events as soon as they occur. Therefore, it has become essential for various modern applications like a user interacting with a website.
Working of Stream Processing
It is frequently used on data generated from events, coming in a series. This includes data from application and server logs, payment processing systems, and IoT sensors. Frequently used paradigms include sink/source and subscriber/publisher. Events and data are produced by a source or publisher and delivered to an application that executes stream processing. The data can then be tested to overcome fraud using algorithms, augmented, or transformed prior to the application conveying the result to the sink or subscriber. From a technical perspective, common sinks and sources used are Apache Kafka®, TCP sockets, big data repos like Hadoop, and data grids.
BangDB: a non-traditional approach towards stream Processing
BangDB, a converged NoSQL platform that adopts a non-traditional approach towards stream processing. BangDb’s high-performance platform is designed keeping high speed data in mind, making it ideal for EdgeAI that needs more than a simple NoSQL store.
Next-generation apps like EdgeAI require the capability to efficiently ingest data, analyse and process it for patterns or predictions, and store it such that powerful queries can run optimally for dashboards or reports.
BangDB uses a combination of NoSQL, streaming, and AI, which helps bring out convergence of various data points to resolve these problems easily and efficiently.
Use cases for stream Processing
Stream processing is typically used for handling event data created as a result of an action and upon which some decision needs to be taken immediately. Some use cases are:
# Real-time detection of anomaly and fraud: Providers of credit card have historically been performing time-consuming fraud detection processes using the batch method after the transaction. However, nowadays, delays in processing credit cards are substantially damaging to the store’s experience, which is attempting to run the card, the end customer trying to pay, and any customers that are in-line. The provider can run the algorithms thoroughly using stream processing to identify and block charges, which are fraudulent, without making their non-fraudulent customers wait. Alerts are also immediately triggered for charges that are anomalous, which warrant additional inspection. Due to fraud and anomaly detection powered by stream processing, one of the largest credit card providers in the world has managed to decrease the write downs of their fraud by over $0.8 billion per year.
# Edge analytics of the IoT (Internet of Things): Those architecting smart buildings and cities, and companies involved in oil, transportation, and manufacturing, have started leveraging this to manage data from several “things.” An epitome of analysis of Internet of Things data is identifying variations within manufacturing that must be fixed promptly to enhance operations and improve yields. Due to real-time processing, the manufacturer can receive real-time alerts when some production line generates several anomalies instead of discovering a complete bad batch when the day’s shift completes. The immediacy of stream processing helps prevent massive wastage by allowing the manufacturer to pause the line to conduct repairs immediately.
# Personalization advertising and marketing: Real-time processing allows companies to deliver personalized experiences to suit their customers’ preferences. A good example of this personalization would be ads similar to recently viewed products or a rebate on something a customer added to their cart but didn’t purchase immediately. Social media platforms also use real-time processing to recommend connecting with friends who have recently registered on the same website.
While some industries may still use block processing, most others have already adopted stream processing to improve their data management operations. Stream processing has revolutionized the world of data management with end-to-end real-time processing solutions like Apache Kafka, MongoDB, and most recently, BangDB. These stream processing systems enable businesses to easily reduce the cost and complexity of correlating data streams with complex events. The result — business efficiency at its best!
