What are the 9 difference between batch processing and stream processing

Data Processing is the collection, manipulation, and processing of collected data for the intended use. It is a computer-assisted technique that involves retrieving, transforming, or classifying data. Computerized Batch Processing is a method of automatically running software programs known as jobs in batches. While users must submit jobs, they are not required to do anything else for the batch to be processed.

Stream Processing refers to the processing of data in motion or computing of data as it is created or received. The majority of data is created as a series of events over time, such as sensor events, website user activity, and financial trades.

This article talks about Batch Processing Vs Stream Processing. It also explains Stream Processing and Batch Processing in detail.

What is Data Processing?

Data Processing is a technique for manipulating information. It refers to the transformation of unstructured data into content that is both meaningful and machine-readable. It refers to the processing of commercial data using automated methods. Raw data is the source of information that is processed to produce useful results.

For businesses to develop better business strategies and gain a competitive advantage, Data Processing is critical. Employees throughout the organization can understand and use the data if it is converted into a readable format such as graphs, charts, and documents.

Based on the data source and the steps taken by the processing unit to generate an output, there are various types of Data Processing. For processing raw data, there is no such thing as a one-size-fits-all solution. The different types of Data Processing are:

• Batch Processing: Batch Processing is a type of data processing that involves processing multiple cases at the same time. It is most commonly used when the data is homogeneous and in large quantities, and it is collected and processed in batches.
• Real-time/Stream Processing: This method allows the user to interact directly with the computer system. This method simplifies data processing. This technique, also known as a direct mode or interactive mode, was created specifically to perform a single task. It’s similar to online processing in that it’s always running.
• Online Processing: This method allows for direct data entry and processing, rather than storing or accumulating data first and then processing. The method is designed to reduce data entry errors by validating data at multiple points and ensuring that only correct data is entered. This method is commonly used for online applications.
• MultiProcessing: MultiProcessing is a data processing method in which two or more processors work on the same dataset simultaneously. Multiple processors are housed within the same system in this case. Data is divided into frames, and each frame is processed simultaneously by two or more CPUs in a single computer system.
• Time-Sharing: This is another type of online data processing that allows multiple users to share an online computer system’s resources. When quick results are required, this method is used. Furthermore, as the name implies, this system is based on time.

What is Batch Processing?

Herman Hollerith, an American inventor who invented the first tabulating machine, used the Batch Processing method for the first time in the 19th century. This device, which was capable of counting and sorting data organized on punched cards, became the forerunner of the modern computer. The cards, as well as the information on them, could then be collected and processed in batches.

Large amounts of data could be processed more quickly and accurately with this innovation than with manual entry methods. Batch Processing is a technique for consistently processing large amounts of data. The batch method allows users to process data with little or no user interaction when computing resources are available.

Users collect and store data for Batch Processing, which is then processed during a “batch window.” Batch Processing boosts productivity by prioritizing processing and completing data jobs when it’s most convenient.

Batch Processing has become popular due to its numerous benefits for enterprise data management. It has several advantages for businesses:

• Efficiency: When computing or other resources are readily available, Batch Processing allows a company to process jobs. Companies can schedule batch processes for jobs that aren’t as urgent and prioritize time-sensitive jobs. Batch systems can also run in the background to reduce processor stress.
• Simplicity: Batch Processing, in comparison to Stream Processing, is a less complex system that does not require special hardware or system support. For data input, it requires less maintenance.
• Improved Data Quality: Batch Processing reduces the chances of errors by automating most or all components of a processing job and minimizing user interaction. To achieve a higher level of data quality, precision, and accuracy are improved.
• Faster Business Intelligence: Batch Processing allows companies to process large volumes of data quickly, resulting in faster Business Intelligence. Batch Processing reduces processing time and ensures that data is delivered on time because many records can be processed at once. And, because multiple jobs can be handled at the same time, business intelligence is available faster than ever before.

What is Stream Processing?

Stream Processing is the act of taking action on a set of data as it is being generated. Historically, data professionals used the term “real-time processing” to refer to data that was processed as frequently as was required for a specific use case. However, with the introduction and adoption of Stream Processing technologies and frameworks, as well as lower RAM prices, “Stream Processing” has become a more specific term.

Stream Processing is concerned with real-time (or near-real-time) data streams that must be processed with minimal latency to generate real-time (or near-real-time) reports or automated responses. Sensor data, for example, could be used by a real-time traffic monitoring solution to detect high traffic volumes. This information could be used to automatically initiate high-occupancy lanes or other traffic management systems or to dynamically update a map to show congestion. 

Some benefits of Stream Processing are:

• The amount of time it takes to process data is minimal.
• The information is current and can be used right away.
• You would need fewer resources to sync systems with Stream Processing.
• Stream Processing also allows you to improve your uptime.
• It aids in the identification of problems so that immediate action can be taken.

Batch Processing Vs Stream Processing

• Batch Processing Vs Stream Processing: Definition
• Batch Processing Vs Stream Processing: Purpose
• Batch Processing Vs Stream Processing: Use Cases
• Batch Processing Vs Stream Processing: Hardware
• Batch Processing Vs Stream Processing: Performance
• Batch Processing Vs Stream Processing: Data Set
• Batch Processing Vs Stream Processing: Analysis
• Batch Processing Vs Stream Processing: Technology Choices
• Batch Processing Vs Stream Processing: Response and Programming Platforms

Batch Processing Vs Stream Processing: Definition

Batch Processing refers to the processing of large amounts of data in a single batch over a set period.

Credit card transactions, bill generation, input and output processing in the operating system, and so on are all examples of Batch Processing.

Stream Processing is the processing of a continuous stream of data as it is generated.

Data streaming, radar systems, customer service systems, and bank ATMs are examples of Stream Processing. These systems require immediate processing to function properly.

Batch Processing Vs Stream Processing: Purpose

Batch Processing is frequently used when dealing with large amounts of data and/or when data sources are legacy systems that cannot deliver data in streams.

Mainframe data is a good example of data that is processed in batches by default. It takes time to access and integrate mainframe data into modern analytics environments, making streaming data unfeasible in most cases. Batch Processing is useful when you don’t need real-time analytics and it’s more important to process large volumes of data than it is to get quick analytics results (though data streams can include “big” data as well – Batch Processing isn’t a requirement for working with large amounts of data

If you want real-time analytics, you’ll need to use Stream Processing. Using platforms such as Spark Streaming, you can feed data into analytics tools as soon as it is generated by creating data streams.

Tasks like fraud detection benefit from Stream Processing. You can detect anomalies that indicate fraud in real-time and stop fraudulent transactions before they are completed if you stream-process transaction data.

Batch Vs Stream Processing: Use Cases

Batch Processing use cases include:

• Payroll: You can run multiple payroll runs at the same time using payroll batch processing. As a result, you can process payroll for multiple groups of employees on different pay cycles at the same time.
• Billing: The practice of processing multiple authorized transactions at once is known as batch payment processing. A merchant may perform one batch processing per day, in which all authorization codes from its customers’ credit cards are sent to each of their banks for approval. 
• Orders from Customers: Batch processing eliminates the need to manually process each order, saving you time, allowing faster shipping, and improving customer satisfaction.

Stream Processing use cases include:

• Fraud Detection: Streaming transaction data can detect anomalies that signal fraud in real-time, allowing you to stop fraudulent transactions before they happen. Fraudulent transactions can also be detected and stopped in the middle of the transaction by inspecting, correlating, and analyzing the data, which can happen in a variety of industries.
• Log Monitoring: The technical approach relies on real-time distributed processing of log streams. Stream processing is a technique for querying and processing continuous data streams. It can perform stream and transaction analyses before extracting data from existing streams and creating new streams for new use cases.
• Analyzing Customer Behavior: Stream processing on streaming data is very useful in the online advertising industry. It is used in social networks to track user behavior, clicks, and interests, and then serve ads to each user based on this information. It promotes advertisements that may be of interest to the users. As a result, stream processing aids advertising campaigns by real-time processing of user clicks and interests and displaying sponsored content.

Batch Processing Vs Stream Processing: Hardware

Batch Processing can be executed with standard computer specifications. To process large batches of data, Batch Processing necessitates the use of the majority of storage and processing resources.

Stream Processing necessitates a sophisticated computer architecture and high-end hardware. To process the current or recent set of data packets, Stream Processing requires less storage. Computational requirements are reduced.

Batch Processing Vs Stream Processing: Performance

The time it takes for your data to appear in your database or data warehouse after an event occurs is known as Data Latency. Latency usually means delay and it determines the performance of Data Processing.

In Batch Processing, Latency can range from minutes to hours to days.

In Stream Processing, Latency must be in seconds or milliseconds.

Batch vs Stream Processing: Data Set

Batch Processing is the simultaneous processing of a large amount of data. Data size is known and finite in Batch Processing.

Stream Processing is a real-time analysis method for streaming data. Data size is unknown and infinite in advance when using Stream Processing.

Stream vs Batch Processing: Analysis

Batch Processing is used to perform complex computations and analyses over a longer period.

Simple reporting and computation are done using Stream Processing.

Batch Processing Vs Stream Processing: Technology Choices

For Batch Processing, there are a variety of technologies to choose from:

• Azure Synapse Analytics is a Big Data analytics service that connects enterprise data warehousing and analytics.
• Azure Data Lake Analytics is an on-demand analytics job service used to make big data easier to understand.
• HDInsight is a cloud-based open-source analytics service that includes Hadoop, Apache Spark, Apache Kafka, and other open-source frameworks.
• Azure Databricks allows us to use open-source libraries and includes the most recent version of Apache Spark.
• Azure Distributed Data Engineering Toolkit is used to provision Spark on-demand on Docker clusters in Azure.

For Stream Processing, there are a variety of technologies to choose from:

• Azure Stream Analytics is real-time analytics and event-processing engine that can analyze and process large amounts of fast-moving data from a variety of sources.
• HDInsight with Storm: Apache Storm is a distributed, fault-tolerant, and open-source computation system that works with Apache Hadoop to process data streams in real-time.
• Azure Databricks and Apache Spark
• APIs for Azure Kafka Streams
• HDInsight with Spark Streaming: On HDInsight Spark clusters, Apache Spark Streaming provides data stream processing.

Batch Processing Vs Stream Processing: Response and Programming Platforms

The response is given after the job is finished in Batch Processing. Some examples of distributing programming platforms for Batch Processing are MapReduce, Spark, and GraphX.

The response is given immediately in Stream Processing.  Some examples of distributing programming platforms for Stream Processing are Spark Streaming and S4 (Simple Scalable Streaming System).

Conclusion

This blog extensively describes Batch Processing vs Stream Processing. In addition to that, it gives a brief introduction to Batch Data Processing and Stream Data Processing. You can also understand batch processing vs stream processing pros and cons from the different parameters considered in the blog.

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