Automation is the reality of today, and the future of tomorrow. We see this more commonly in technology and data jobs, where many tasks are iterative and do not always need human intervention.
The concepts of stream processing and batch processing allow for a certain level of automation when working with large volumes of data.
Stream processing allows for real time processing and analysis of live data and convert them to the desired form before releasing them to the public or another processing source. Batch processing allows machines to perform repetitive tasks, such as the ones mentioned above.
What is stream processing?
Stream processing is essentially a process that refers to processing and analyzing the streams of data. This data can be incoming as a continuous and never-ending data stream from various data sources.
This can include data from:
- Video-streaming applications such as Amazon Prime Video, Netflix etc.
- Weather applications sending live weather data
- Experimental data from scientific research and experimentation
- Data from financial trading websites
- Census-based data etc.
Stream processing completely changed the paradigm of how data processing worked. Earlier, structures like data lakes were used to store information. However, it was not the most efficient method and did not allow for large scale data analysis.
What is batch processing?
Batch processing refers to processing a series of tasks assigned to a system or machine in sequential batches or sets. The data fed into the computer is not dynamic or continuous. Instead, it is sequential and time-bound.
Batch processing deals with static data such as:
1. Payroll-based data
2. Financial number crunching
3. Census-based data that is systematically fed
4. Employee data for HR management
5. E-commerce-related data
Why are stream processing and batch processing used?
Stream processing is used for the real time processing of dynamic data whereas batch processing is used for static data.
Stream processing vs Batch processing: A comparison
|Used to process dynamic or live data
||Used to process static data that is fed into the system controlling it
|Quantity of data handled is evolving and thus technically infinite
||Quantity of data handled is static and finite
|This method responds to the data fed during the process of data feeding
||This method responds to the data fed after the entire process flow is completed
|Takes a short amount of time to process data
||Takes a longer amount of time to process data
|Processes the data in a limited or short number of cycles
||Processes the data in a larger or increased number of cycles
What are the challenges faced with stream processing and batch processing?
Disadvantages of stream processing
Drawbacks of the stream processing system include:
- Despite the dynamic nature of the data being processed, the data stacks processed through the various processing pipelines experience the same rigidity. This rigidity contrasts with the ease of processing data as promised by this method of processing data.
- Because of the dependence of this method on a central cluster of data processors, the fluidity of data processing could be impacted as the data will take longer to process.
- The need for data demarcation and allocation in streaming processing necessitates the restructuring of the existing structures that have stored and processed the data. This is oth time consuming and hampers efficiency.
- The widespread quantity of data can occasionally make accessing it difficult.
Disadvantages of batch processing
Drawbacks of the batch processing system include:
- An organization or a group of people carrying out batch processing cannot make several changes to the data being fed or which has been fed, making it impossible to edit wrong data. This inability to correct the erroneous data, if any, leads to a lot of wastage of time.
- Batch data processing is mostly outdated now, and is getting replaced by newer methods of data processing.
- Since the batch processing method utilizes static forms of data, users of these systems find it difficult to refer to them for updated information or analysis.
- Since batch data processing does not require human supervision during the process, there maybe inaccuracies in the output if the input data is incomplete or corrupt.
- Batch data processing is highly time-consuming.
What are the purposes of stream processing and batch processing?
The purpose of stream processing is to analyze and utilize real-time data to either transform it or process it and send it ahead to other processing systems until the desired data is synthesized.
The purpose of batch processing is to process certain amounts of data, which may be repetitive, over a period of time.
Why are stream processing and batch processing important?
Despite the drawbacks of stream processing and batch processing mentioned above, these processes also have a lot of benefits and positive points about them, such as the ones mentioned below: -
Benefits of stream processing
Mentioned below are some of the advantages of stream processing –
- The losses incurred, if any, are minimized due to the dynamic nature of the data being processed
- The customers that the organizations serve are quite satisfied due to the high speed of data processing
- Real-time input prevents several mishaps such as accidents etc. concerning live bus or train routes etc.
- The multiple volumes of data assist a variety of enterprises over the world.
Benefits of batch processing
Stated below are some of the benefits of batch processing –
- Does not require specialized hardware to run ut and thus reduces the company’s running costs
- Since the whole process of batch processing of data is automated or time-bound and the system knows the process flow, the knowledge ensures that users know when to expect the process termination, which helps them plan the overall process flow
- The manual nature of feeding the data ensures that the users dictate the flow of the data
- multiple users can share the same batch processing system as the system processes different batches of data as per the schedule of the data entered
What are the real-world applications of stream processing and batch processing?
Industry (real-world) applications of stream processing
- Geographic data requires applications, such as Google Maps, etc., require real-time location data to be able to provide accurate information, such as the approximate time required to travel to one spot from another, the incidence of traffic etc.
- Meteorological observatories and applications rely on the data gleaned from those offices or other sources, such as the incidence of rainfall, the wind speed, the humidity content etc., which are common functions that can be accessed on a weather app
- Responding to changes in the Information Technology infrastructure in Big Data- enabled corporations such as Twitter, Google etc., wherein the data received from the change sites need to be processed immediately to allow for smooth functioning of the company.
- To keep or maintain live inventories in e-commerce-related enterprises such as product warehouses, depots, etc., as the high volume of products entering and exiting the facility requires a dynamic approach to maintaining the same.
- To detect real-time instances of fraud in cybersecurity-enabled applications, such as Norton, Kaspersky etc., that detect malware on various devices.
Industry (real-world) applications of batch processing
- To accept orders from customers in e-commerce-enabled applications
- Functions such as billing and payments, etc., for both physical and digital stores
- Data management of employee payroll systems
- To map and process the large streams of data being fed into the system in electricity-monitoring companies etc.
To sum up, stream processing and batch processing are helping professionals streamline their work. Moreover, with these processes, managing data has become easier than ever, which also increases efficiency and saves time.
Stream processing and batch processing are commonly used in various data related projects. You can learn more about them and other key data concepts through comprehensive and industry-focused programs across machine learning and AI, data engineering, business analytics, and data science.