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Table of contents
1.
Introduction
2.
What is Batch Data Processing?
3.
Essential Parameters for Batch Processing
4.
Batch in Big Data Analytics
5.
Advantages of Batch Data Processing
6.
Disadvantages of Batch Data Processing
7.
Use Cases of Batch Processing
8.
Batch Processing vs Stream Processing
9.
Frequently Asked Questions
9.1.
What distinguishes real-time processing from batch processing?
9.2.
How does batch processing aid massive data management and organisation?
9.3.
How can organisations ensure data security in batch processing?
10.
Conclusion
Last Updated: Mar 27, 2024
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Introduction to Batch in Big Data Analytics

Author Shiva
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Ashwin Goyal
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Introduction

Managing vast amounts of data is a recurring difficulty in Big data analytics. Imagine attempting to analyse the millions of photographs posted to social media daily or the enormous volume of financial transactions carried out worldwide. Handling all of this data in real-time would be quite difficult. Batch data processing can help in this situation! 

Introduction to Batch in Big Data Analytics

In this article, we will have an Introduction to Batch in Big Data Analytics.

What is Batch Data Processing?

Batch data processing is a method for organising and processing massive amounts of data in "batches." The data is divided into smaller units known as batches. Then, these batches are processed one by one, with the outcomes kept before moving on to the following batch. Businesses can increase their effectiveness and scalability by automating and streamlining data processing.

Consider that you wish to count the number of sweets in a large bag of candies. Counting them one by one would take a long time, but if you separate them into smaller piles, you can count each pile much more quickly and easily. Batch data processing with huge data accomplishes that.

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Essential Parameters for Batch Processing

Several crucial factors must be taken into consideration to achieve appropriate batch processing:

  • Fault Tolerance: Fault tolerance in batch processing refers to a system's ability to recover from errors or failures. It is crucial to have a fault-tolerant system in place to ensure that data is neither lost nor corrupted during processing.
     
  • Batch Size: The quantity of data processed once is called a batch. The batch size should be tailored to the precise work at hand as well as the capabilities of the system, much like the size of the heaps we discussed before. The proper batch size guarantees that the processing is effective and doesn't overtax the system.
     
  • Data Integration: Data integration combines information from several sources to produce a comprehensive dataset. It is essential to have a clearly defined data integration procedure to ensure that data is appropriately prepared and processed during batch processing.
     
  • Processing Frequency: This parameter controls how frequently batches are processed. Batches might be handled daily, weekly, or even monthly, depending on the demands of the business and the rate at which data is produced. Selecting the appropriate processing frequency is essential for staying current with the data and making timely choices.
     
  • Processing Time: It refers to the time required to process a single batch of data. It is important to optimise this period to keep data processing on schedule and prevent system bottlenecks.

Batch in Big Data Analytics

A Batch is the set of data in which processing takes place. This batch is stored over a period of time. For e.g. payroll or billing systems that take place monthly or weekly.

Batch processing offers scalability. Batch processing can handle large datasets by dividing them into manageable batches and processing them by a timetable. Businesses need to tax their infrastructure to process massive amounts of data. Additionally, it allows for parallel processing, speeding up and improving efficiency by handling several batches simultaneously.

Also, It is very economical. Batch processing helps businesses to use their resources more effectively. Therefore, it is less expensive than real-time processing. 

Advantages of Batch Data Processing

Let's explore some of the advantages of batch data processing:

  • Efficiency: Batch processing is the act of grouping jobs and processing them all at once. The computer won't have to start and stop for every data piece, saving time. It can handle a lot of information simultaneously, speeding up the procedure.
     
  • Scalability: Batch processing can manage increasing volumes of data without sluggishness as your data grows.
     
  • Reduced Overhead: Overhead is the additional work you must complete before beginning what you want to perform. You don't have to gather and organise data for analysis constantly. Batch processing helps you cut down on overhead. 
     
  • Easier Error Handling: Errors can occur when working with much information, making error handling simpler. Batch processing is advantageous since it makes it simple to identify the particular batch or group that malfunctioned during data analysis. 
     
  • Resource Management: Batch processing resembles resource management in several ways. Batch processing makes better use of computer resources. It organises jobs to use memory, computing power, and other computer resources as efficiently as possible.
     
  • Repeatable and Reliable: Batch processing enables you to do the same analysis on a fresh data set while getting the same results each time. This makes comparing and following changes over time simpler.
     
  • Offline processing: There are occasions when you need to analyse a lot of data but wait to require the results immediately. Batch processing enables you to perform other chores while the computer works on the data in the background. 

Disadvantages of Batch Data Processing

Let's explore some of the disadvantages of batch data processing:

  • Limited Interactivity: In real-time applications, such as online gaming or chat systems, users expect instant responses based on their actions. Batch processing cannot meet these real-time interactivity requirements.
     
  • Data Loss Risk: If an error occurs during batch processing, there is a risk of data loss for the current batch. Since data is processed in larger portions, recovering lost data can be challenging and might require reprocessing the entire batch.
     
  • Time Delay: In batch processing, data is collected over a period, and then the analysis is performed on the entire dataset simultaneously. This means that insights and results might not be available in real-time, which can be problematic for time-sensitive applications like fraud detection or monitoring critical systems.
     
  • Stale Data: Since batch processing involves analysing data in chunks or batches, the information used for analysis might already be outdated by the time the processing completes. This can lead to decisions or recommendations based on old information, which may not reflect current affairs.
     
  • Resource-Intensive: Running complex analyses on massive datasets can be time-consuming and expensive. This makes it less suitable for scenarios that demand rapid data processing or have tight resource constraints.
     
  • Inefficient for Small Data: Batch processing can be overkill and inefficient when dealing with small datasets. It involves processing data in fixed intervals, which might lead to wasting resources if the data volume is small and the processing time is relatively short.
     
  • Complex Error Handling: Detecting and handling errors in batch processing systems can be complex. If an issue arises during processing, identifying and resolving the exact cause can be more challenging than real-time systems.
     
  • Incomplete Picture: Because batch processing deals with data in chunks, it might provide a partial picture of evolving situations. Certain events or anomalies between batch runs might be missed or not accounted for in the analysis.

Use Cases of Batch Processing

Batch Processing is used widely in various industries. Here are some: 

  • Social media Analysis: Have you ever wondered how social media platforms suggest friends or present you with advertisements that are relevant to your interests? To identify trends in the interactions, likes, and postings of millions of users, they analyse the data in batches. They can then display advertising that is more likely to attract your attention and make personalised suggestions for you.
     
  • Traffic Optimisation: City planners and transportation agencies can enhance traffic flow using batch processing. The number of cars on the road and their location are two examples of the data they gather from cameras and sensors throughout the city. Afterwards, they employ batch processing to examine the data, identify traffic trends, and develop better road and traffic light designs.
     
  • Healthcare Insights: Researchers and doctors can uncover patterns and insights by analysing vast amounts of medical data in batches. To determine the best therapies, they might examine data from individuals with comparable ailments or search for trends that could aid in diagnosing and preventing diseases.
     
  • E-commerce Recommendations: Have you ever noticed how certain internet stores recommend goods based on previous purchases? Batch processing works its magic in that situation. These websites gather information about the products you and other users have bought, analyse them, and suggest goods based on your preferences.
     
  • Weather Prediction: Forecasting the weather with the use of batch processing requires the analysis of enormous amounts of weather data. They can forecast the weather for the upcoming days or weeks by examining previous weather patterns, which is useful for making plans and remaining safe.
     
  • Financial Analysis: Large financial institutions utilise batch processing to manage enormous volumes of data concerning stock prices, market patterns, and economic indicators. By doing so, they can better manage risks and make informed investment decisions.

Batch Processing vs Stream Processing

  Batch Processing Stream Processing
Data Processing Deals with data in fixed-size batches or chunks. Processes data in real-time as it arrives.
Processing Time Takes longer processing time due to batch size. Provides faster insights with low latency.
Scalability Highly scalable and suitable for large datasets. Less scalable and may need help with big data.
Resource Usage Efficiently uses resources due to batch grouping. Requires more resources due to continuous flow.
Real-time Insights Not suitable for real-time insights. Provides real-time insights and actions.
Cost-Effectiveness Cost-effective for processing large volumes. It can be more expensive due to resource demands.
Use Cases Finance, Healthcare, Retail, Manufacturing, etc. Real-time analytics, IoT applications, Monitoring
Example Daily sales report generation in a retail store. Real-time stock market data analysis.

Frequently Asked Questions

What distinguishes real-time processing from batch processing?

Real-time processing analyses data as it enters, giving quick insights, while batch processing processes data in fixed-size batches. Large datasets are better suited for batch processing, which is also more economical, whereas real-time processing provides immediate results.

How does batch processing aid massive data management and organisation?

Batch processing divides large amounts of data into smaller, more manageable batches. This organised data processing makes handling, analysing, and storing the data easier.

How can organisations ensure data security in batch processing?

Encryption, access controls, and secure data transport can all help to protect data during batch processing. Regular audits and monitoring also aid in locating and fixing security flaws.

Conclusion

In the article, we looked at the introduction of batch of Big Data Analytics. We looked at What is Batch Data Processing, its essential parameters, batch data analytics, advantages and disadvantages, its use cases and the difference between batch processing and stream processing.

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