Since businesses collect a colossal amount of data, it has become essential to process and manage this massive data for business growth. Therefore, businesses use 2 well-known Data Processing methods – Batch Flow and Continuous Flow – to process this vast data. Both methods help businesses process Big Data seamlessly to be used as a foundation for Data Analysis or Decision Making. Although these methods are used for the same objectives, their functioning is different.
In this article, you will learn about Batch Flow Processing and Continuous Flow Processing along with their differences.
Table of Contents
Basics of Data Processing.
What is a Batch Flow?
Batch Flow Processing refers to processing high volumes of data in batches or groups sequentially. It is used for storing and extracting data in batches. Some storing processes include ETL (Extract, Transform, and Load) and the ELT (Extract, Load, and Transform) processes, where the data is extracted from external sources, transformed according to the requirements, and stored in a Data Warehouse for analysis. These processes can take long hours, depending on the data size. It is generally processed post business hours since the processing units are available for handling large datasets.
Batch Flow Processing is used not only for storing purposes but also for fetching data from a centralized resource for your project needs. For example, you want to work on the Indian Weather dataset but do not need the entire weather data. In this case, you can use SQL to fetch only the data you need in batches for your project from the whole dataset.
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Key Concepts in Batch Flow Processing
The Batch Flow Processing systems consist of the below key concepts.
- Batch: A batch is a group of programs or processes executed regularly, ranging from a few hours to a few months.
- Job: Job is the individual program or process to be executed in a batch or a group.
- Batch Jobs: Batch Jobs are jobs or scheduled programs in a Batch Processing system that run without user interaction.
Advantages of the Batch Flow Processing
Here are some of the key advantages of Batch Flow Processing systems.
- No Human Interaction: In a Batch Flow Processing system, the operational costs like labor and equipment are low as it uses physical hardware like computers, CPUs, and more and does not involve any human interaction in the entire process.
- Hands-off Policy: As Batch Flow Processing systems do not include human interaction, Managers and Developers can focus on crucial tasks without spending time supervising Batch Processing.
Alerts are sent whenever there are any faults or errors in the Batch Processing systems. As a result, this process provides professionals with a hands-off policy for Batch Processing.
What is Continuous Flow Processing?
Continuous Flow Data Processing is also known as Stream Processing. With Stream Processing technology, the data stream can be processed, stored, and analyzed in real-time. Unlike the Batched Jobs, Stream Processing does not require waiting until all the data has been collected before analyzing or getting results.
Continuous Flow Processing describes continuous, never-ending data streams with no beginning or end, providing a constant feed of data that can be utilized. Data in the Continuous Flow Processing are generated by all sources in different formats and values, from applications, network devices, and server logs to banking transactions, website activity, and location data. These all can be aggregated to gather real-time information and analyze data seamlessly.
An important example of Continuous Data Flow Processing is real-time fraud and anomaly detection. One of the world’s largest Credit Card providers has been able to reduce its faults due to anomaly detection powered by Continuous Flow Processing. Credit Card Processing delays can harm the experience of both the customers and the Credit Card providers.
Earlier Credit Card providers performed their Fraud Detection processes in a Batch Processing system, which consumes a lot of time. But, now, with a Continuous Flow Processing system, Credit Card providers can deploy algorithms for recognizing and blocking fraudulent charges and alerts without making their customers wait as soon as they swipe the card.
Advantages of the Continuous Flow Processing
Here are some of the key advantages of Continuous Flow Processing systems.
- Increase ROI: The Data Processing techniques in the continuous Data Flow process have the potential to quickly collect, analyze and function on the current data or the real-time data. These processes give organizations a competitive edge in their marketplace as they can respond to changing needs for business growth.
- Customer Satisfaction: In the Continuous Data Flow, the data is processed and sent to the analytics system at the runtime. As a result, Streaming Data Processing helps organizations respond quickly to customer complaints or reviews, ultimately increasing the organization’s reputation and customer satisfaction.
- Reduce Losses: Continuous Data flow Processing not only helps customer satisfaction but also helps prevent organizational losses by providing warnings. It can warn of impending issues such as data breaches, system outages, and more. With the help of such warnings, organizations can prevent the upcoming defects by resolving the warnings.
Batch vs Continous Streaming: Differences
Here are some of the key differences between Batch and Continuous Streaming systems.
Processing of Data
In a Batch Flow Processing system, the processing is performed on stored data from a Data Warehouse or Data Lake. For example, organizations’ Payroll and Billing system data need to be processed monthly. However, in a Continuous Data Flow Processing system, the processing is performed as the data flows through the system, resulting in real-time analysis and reporting of events—for example, fraud detection or intrusion detection.
Speed and Real-time Analytics
The Batch Flow Processing system is suitable for large datasets and a project that involves deeper Data Analysis. To start a project, you must pull desired data from the Central Storage System. This data can contain Terabytes and Petabytes of information. As a result, processing these large datasets might take long hours to execute.
In contrast, in the case of Continuous Data Flow Processing, the data is directly fed into the analytics system piece-by-piece as soon as it is generated. Therefore, instead of processing a batch over time, Continuous Data Flow directly sends data to the analytics system and gains its insights in real-time. As a result, Continuous Data Flow Processing requires much less time to process and is suitable for projects requiring high speed.
Modification in Workflows
Since the Batch Processing process fetches data in batches, you must wait until the entire batch is executed. For example, suppose you are fetching any medical data for your project needs in batches from a centralized repository and forgot to fetch critical information or fetched undesired data, you will lose a lot of time in either stopping the Batch Processing or fetching the desired data again.
Therefore, you must check and verify commands to ensure you are fetching all the necessary data. But, in Continuous Flow Processing, you can quickly identify any issues and resolve them to gain real-time insights quickly. However, if you miss any data while collecting in Continuous Flow Processing, it will be lost forever, as real-time data are usually consumed immediately without storing it.
Examples and Applications
Examples of Batch Flow Processing systems are distributed programming platforms such as MapReduce, Spark, GraphX, and more. Batch Flow Processing systems are used in Payroll and Billing systems. In contrast, the examples of Continuous Flow Processing systems are Spark Streaming, S4 (Simple Scalable Streaming System), and more. Continuous Flow Processing systems are used in stock brokerage transactions, eCommerce transactions, customer journey mapping, and more.
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Which is Better, Batch Flow or Continous Flow?
Both the Data Processing methods – Batch Flow and Continuous Flow processing – are popular in almost every organization. However, Continuous Flow Processing is best for use cases where time is a priority, and Batch Processing is used where you do not need real-time insights. Therefore, choosing the best Data Processing method depends on your business objectives and requirements.
In this article, you learned about two kinds of Data Processing techniques – Batch Flow and Continuous Flow – and their advantages, challenges, and differences. Data Processing becomes essential for organizations to process their business data in a specific format for further analysis.
Organizations can use Batch Flow Processing for large amounts of data while Continuous Flow Processing for real-time Data Processing. However, it’s easy to become lost in a blend of data from multiple sources. Imagine trying to make heads or tails of such data. This is where Hevo comes in.
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Share your experience of understanding Batch Flow and Continuous Flow in the comments section below.