What are the challenges and limitations of batch data processing in real-time scenarios?

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Batch data processing is a method of handling large volumes of data by dividing them into batches and processing them sequentially or in parallel. It is often used for tasks that do not require immediate results, such as data analysis, reporting, backup, or archiving. However, batch data processing also has some challenges and limitations when it comes to real-time scenarios, where data needs to be processed and delivered as fast as possible. In this article, we will explore some of these challenges and limitations, and how they can be addressed or mitigated.

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Pedro Ferreira

Senior Data Engineer | knok

1 Latency and responsiveness

One of the main challenges of batch data processing is latency, which is the time delay between the data input and the data output. Latency can affect the responsiveness and accuracy of data-driven applications, such as dashboards, alerts, or recommendations. For example, if a batch data processing system takes hours or days to process and update the data, the users may not see the latest information or receive timely feedback. To reduce latency, batch data processing systems can use techniques such as incremental processing, which only processes the new or changed data, or stream processing, which processes the data as soon as it arrives.

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Pedro Ferreira

Senior Data Engineer | knok
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Based on my experience, the delay in a batch job is an important aspect. When an ETL process that is executed during the night takes more than expected it can affect the stakeholder's day and we, as data engineers, should consider this and keep an eye on the performance of our batch jobs to understand if we are facing a degradation in performance and act preventively and not reactively

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Shivani Shrivastav

Open to Data Scientist & Analytics Roles | Master’s in Data Science @ Illinois Tech | Python | R | ML | AI | Big Data | Power BI | AWS | SQL | ETL #OpenToWork #DataScience #Analytics #Python #ML #AI #BigData
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Batch data processing in real-time scenarios struggles with latency, scalability, and cost, but other key challenges include data freshness, inefficient resource utilization, complex error handling, and integration issues with real-time systems. Balancing batch and real-time processing is crucial.

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Shivani Shrivastav

Open to Data Scientist & Analytics Roles | Master’s in Data Science @ Illinois Tech | Python | R | ML | AI | Big Data | Power BI | AWS | SQL | ETL #OpenToWork #DataScience #Analytics #Python #ML #AI #BigData
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Delayed insights due to scheduled batch runs. Complexity & Scalability – Handling large datasets efficiently requires robust infrastructure. High storage and compute costs for frequent batch processing. Ensuring data privacy and governance across batch jobs. Batch processing lacks real-time updates, leading to outdated insights.

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2 Complexity and scalability

Another challenge of batch data processing is complexity, which is the difficulty of designing, developing, testing, and maintaining the batch data processing system. Complexity can arise from factors such as the diversity and heterogeneity of data sources, the variety and frequency of data transformations, the dependencies and coordination of data flows, and the quality and consistency of data outputs. Complexity can also affect the scalability of the batch data processing system, which is the ability to handle increasing amounts of data and requests. To manage complexity and scalability, batch data processing systems can use frameworks and tools that provide abstraction, automation, orchestration, and monitoring of the batch data processing tasks.

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Pedro Ferreira

Senior Data Engineer | knok
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From my experience, a batch job can transform into a very complex task that is hard to maintain and debug. What works for me is trying to split the batch job into smaller batch jobs with simpler logic and string together those smaller batch jobs making the one complex batch job into a sequence of smaller and simpler ones. This will make our super-complex batch job into small and easy to maintain and debug ones

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3 Cost and performance

A third challenge of batch data processing is cost, which is the amount of resources and money required to run the batch data processing system. Cost can depend on factors such as the size and speed of the data, the type and number of data processing nodes, the storage and network capacity, and the service level agreements. Cost can also impact the performance of the batch data processing system, which is the measure of how well the system meets the expectations and requirements of the users and stakeholders. For example, if a batch data processing system is under-provisioned or over-provisioned, it may result in poor performance, such as errors, failures, or delays. To optimize cost and performance, batch data processing systems can use techniques such as load balancing, fault tolerance, elasticity, and tuning.

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Olga Kokhan

CEO at Tinkogroup | Data Entry, Data Annotation, Lead Generation Services
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?? Batch data processing is great for handling large volumes of data at once, but let’s be real—the cost can add up fast. Running heavy processing jobs requires computing power, storage, and sometimes specialized tools, all of which aren’t cheap. ?? From my experience, the best way to manage costs is optimizing batch schedules — run jobs during off-peak hours, use cloud services wisely, and avoid unnecessary processing. It’s also worth exploring compression techniques and automation to cut down on resource usage. ? Batch processing is powerful, but if you don’t keep an eye on costs, it can drain your budget fast. Work smart, not just big.

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4 Security and compliance

A fourth challenge of batch data processing is security, which is the protection of the data and the system from unauthorized access, modification, or disclosure. Security can be compromised by factors such as malicious attacks, human errors, or system vulnerabilities. Security can also affect the compliance of the batch data processing system, which is the adherence to the laws, regulations, standards, and policies that govern the data and the system. For example, if a batch data processing system handles sensitive or personal data, it may need to comply with rules such as encryption, anonymization, or retention. To ensure security and compliance, batch data processing systems can use techniques such as authentication, authorization, auditing, encryption, and backup.

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5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are the challenges and limitations of batch data processing in real-time scenarios?

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1 Latency and responsiveness

2 Complexity and scalability

3 Cost and performance

4 Security and compliance

5 Here’s what else to consider

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