Batch Processing vs Stream Processing

Introduction to Batch and Stream Processing

In today's Big Data landscape, developers must analyze terabytes and even petabytes of data in any given period. Data attracts more data(like metadata). This gives us several advantages, yet it can be perplexing at times to possibly know the desirable way to speed up and accelerate these technologies, especially when quick reactions are necessary to meet business requirements. For cloud-native corporations, an intriguing question has become how to efficiently apply batch processing and stream processing in their use case.

What is Batch and Stream Processing?

Batch processing is designed to be a completely automated process without human intervention that runs high-volume, repetitive data jobs and

Stream processing is a data management technology that processes continuous data flow from sources.

Batch Processing

Batch processing is the processing of transactions during a group or batch. No user interaction is required once execution is underway. This differentiates execution from transaction processing, which involves processing transactions one at a time and requires user interaction.

What is the need for batch processing?

Needs for Batch processing are:

Performance improvement
A certain quantity of knowledge is often processed during a batch.
Jobs can be executed in parallel/in multiple.
Recovery in case of an abnormality Jobs can be re-executed (manual/schedule).
When reprocessing, it's possible to process only unprocessed records by skipping processed records.
Various activation methods for running jobs
Synchronous execution is possible.
Asynchronous execution is possible.
DB polling and HTTP requests are often used as opportunities for execution.

Stream Processing

Stream processing is used to process continuous data streams in real-time without using a Data-Store for persisting the data. Analysts can continuously monitor the streaming data to monitor a stream of data to achieve different results or perform analytics to provide Data Visualization to help improve businesses.

What is the need for stream processing?

Needs for Stream processing are:

Keep the data flowing: A real-time processing engine processes messages in-stream without any requirement to store them to perform any operation or sequence of operations.
Process and respond instantaneously: Stream processing engines must have a highly-optimized and minimal overhead execution capability to deliver a real-time result for high-volume applications.

What is the difference between stream and batch processing?

The differences between Stream processing and Batch processing are highlighted below:

Speed

Batch Processing processes a massive volume of data simultaneously, whereas Stream Processing processes streaming data in real time.

Hardware

Batch processing is mainly used while dealing with vast amounts of data not deliverable in streams. Meanwhile, Stream Processing is used for real-time data analysis like sensors and fraud-detecting devices.

Performance

Batch Processing requires a longer time to process data, whereas Stream processing requires only a few milliseconds.

Data set

Batch Processing processes finite data of known size, whereas Stream Processing processes streaming data of unknown size and infinite quantity.

Batch Processing processes data in multiple passes, whereas Stream Processing processes data in a few passes. The input graph is static in the case of Batch Processing and dynamic in the case of Stream Processing.

Analysis

Batch Processing analyzes data on a snapshot, whereas Stream Processing analyzes data continuously.

Batch Processing responds to job completion, whereas Stream Processing responds immediately.

What are the applications of Batch Processing and Stream Processing?

The applications of Batch Processing and Stream Processing are below mentioned.

Application of Batch Processing

Batch processing handles large amounts of non-continuous data to minimize/eliminate the need for user interaction and improve job processing efficiency.
Batch processing can be ideal for managing database updates and transactions and converting files from one format to another.
Batch processing can be used when running complex algorithms against large datasets.

Application of Stream Processing

Stream processing is most effective in algorithmic trading and stock market surveillance, computer system and network monitoring and wildlife tracking, predictive maintenance, intelligent devices, and intelligent patient care applications.
Sensors in industrial equipment, vehicles, farm machinery, etc., send streaming data to an application that monitors the device's performance or detects and helps fix any potential defects to prevent equipment downtime.
We track data from consumers' mobile devices to make real-time property recommendations based on geo-location.
Video game digital distribution services like Steam, Ubisoft, etc., collect streaming data like user-gaming preferences and analyze these data in real-time to offer discounts for in-game purchases or offers on other games and other dynamic experiences to engage its players.

Scenarios where batch and stream processing are both used

Batch and Stream Processing can be used in scenarios where we need new data, but not necessarily in real-time. This means we don't have to wait an hour or a day for the data to process. Also, we don't need to know every second of the data analysis. One such scenario can be web analytics. Data analysts will immediately monitor how this affects user behavior if a renowned eCommerce site changes its user interface. This is because a drop in conversion rates can lead to significant sales loss. In this case, a day's delay is too long, and a minute's delay is not an issue.

Is Spark Batch processing or Stream Processing?

Spark is a data processing engine that can process both batch and stream data; hence it can do batch and stream processing.

What are the use cases of batch processing and stream processing?

The use cases of batch processing and stream processing are described below:

The use cases of Batch Processing

We use Batch processing when the data size is known and fixed. It takes a little longer to process the data. It requires dedicated staff to handle the issues.
Batch processing processes data in several passes.
After the data is collected over time and similar data are batched/grouped, batch processing is used.

The use cases of Stream Processing

Stream processing is used when the data size is variable and continuous, where it takes a few seconds/milliseconds to process data. The stream processor processes data in a few passes. It is used when a data stream requires an immediate response.

Log-Analysis:- Stream Processing can be used to analyze real-time logs to gain insights. Cloud watch logs can be streamed using lambda and kinesis to gather information about EC2 clusters, Beanstalk applications, or Docker Containers deployed in ECS.

Fraud Detection:- Processing of streaming transaction data can help us detect anomalies to identify and stop fraudulent transactions in real time.
IoT:- Telemetry data coming from sensors, PLC(Programmable Logic Controllers), etc., can be processed using ML/DL algorithms to generate real-time analysis, to use with automation applications, or to monitor environmental changes.
Other applications include Online adverts and Database Migrations.

What are the limitations of Batch processing and Stream processing?

The limitations of batch processing are:

Debugging a Batch Processing system is difficult as it requires a team of professionals dedicated only to fixing the error. For this system, training is expensive as one needs to understand batch scheduling, notification, triggering, etc.
Each batch can be subject to meticulous quality control and assurances, potentially causing increased team member downtime.
Processing large batches of data require massive storage and processing resources, leading to increased costs when scaling up.

The limitations of stream processing:

Data input and output rate can create a problem in Stream Processing because it must cope with enormous amounts of data and respond immediately.
The biggest challenge that organizations face in stream processing is that the rate of long-term data release should be faster or faster than the rate of long-term data entry; otherwise, the system will start to have problems with storage and memory.

Conclusion

There is no universally superior method in data processing as Batch and stream processing each have strengths and weaknesses, depending on your applications. To stay agile, corporations hold to glide in the direction of stream processing. However, batch processing is widely used and may be used so long as legacy structures remain a vital component of the data ecosystem. Flexibility is a significant factor in data processing. As different projects call for different approaches, developers must have the capability to find optimal solutions for each use case. There is no clear winner between stream and batch processing. Teams that can work with both win.