Big data is complex to understand. The complexity increases many times when it comes to choosing the right way to process this massive amount of data that is being generated every day. Currently, two famous ways of processing are prevalent. The batch and stream processing. If you are the one confused by the difference, Ksolves brings you this guide so you can differentiate between the two and choose the correct one.
In this blog, we will discuss some of the major lines of differences between batch and stream processing. Let’s get started.
Batch processing vs. stream processing
There are no exact terms to define both the processings. However, we can differentiate batch and stream processing with these points-
- In a batch processing model, data is collected over time. Once collected, it is fed into the analytics system. This batch of information will be sent for processing.
- In a streaming model, data is fed and processed in the tool piece-by-piece. The processing is done in real-time.
To understand the deeper meaning, let’s look at the reason to use batch processing and stream processing.
Batch processing use cases
Batch processing is generally used where a large amount of data is involved, or when data sources are legacy systems that are not capable of delivering data in streams.
Data that is generated in mainframes is a perfect example of data that is processed in batch form by default. In most cases, accessing and integrating mainframe data takes time which in turn makes streaming unfeasible. Batch processing works well in situations where real-time analytics is not required and processing large volumes of information is more important.
Batch processing can be used in several applications–
- Orders from customers
Stream processing use cases
Stream processing is very important for analytics in real-time. You can feed data into analytics tools by building data streams, as soon as it is generated and get near real-time analytics using Spark Streaming.
Stream processing is very helpful in tasks like fraud detection. If you process transaction data with stream processing, you can detect anomalies and fraud in real-time and can even stop the fraudulent transactions even before they are completed.
Several use cases for stream processing are–
- Fraud detection
- Social media analysis
- Log monitoring
- Analyzing customer behavior
Transforming batch data into streaming data
The nature of the data source plays a major role in defining if the data is suitable for batch or stream processing.
However, that doesn’t mean that you can not do anything to transform batch data into streaming data. If you are someone who is working with legacy data sources such as mainframes, you can take advantage of Ksolves’ expertise to turn your batch data into streaming data.
We can say that by setting up streaming, you can do things with your data that were not possible earlier. You can also obtain faster results and can create solutions so that you don’t lose the ability to leverage outcomes.
Ksolves is one of the leading big data consulting firms dealing in batch and stream processing. Our experts are specially trained in handling projects that require lots of insights. If you wish to learn more about stream processing, Give us a call or write to us in the comment section below.