Real-time vs Batch processing in ETL pipeline – which to choose?

real-time vs. batch processing

Anyone who plans a big data infrastructure will quickly come to the question of whether to use batch processing or real-time stream processing in data analytics.  

Batch processing involves collecting and processing data in predefined intervals or batches, such as nightly data updates for inventory management in e-commerce. On the other hand, real-time processing involves handling data immediately as it arrives, like processing online transactions instantly for fraud detection in financial transactions. 

How to balance the need for timely insights and responsiveness with the resources required for immediate data processing and infrastructure complexity? We’ll figure it out and help you decide between batch vs. real-time processing in the article below. 

What Is an ETL Pipeline? 

Before we delve into the details, let’s understand what a data pipeline is. 

A data pipeline is a collection of processes that move data between the source system and the destination storage. An ETL (Extract, Transform, Load) pipeline is one of the data pipelines that refers to a specific way in which data is collected, transformed, and loaded into target systems. 

An ETL pipeline is a series of processes that: 

  1. Extracts data from various sources (such as databases, files, and APIs). 
  2. Transforms the data depending on a predefined structure. This can mean cleansing, aggregating, enriching, or converting the data into another form. 
  3. Loads the data into a data warehouse or any other chosen destination. 

Let’s examine each of these steps more closely. 

The extraction phase collects data from multiple sources and loads it into a data warehousing area or intermediate destination. Common data sources are: 

  • SaaS applications 
  • CRM platforms 
  • Sales and marketing tools 
  • Event streams 
  • SQL or NoSQL databases 

Data from these sources can be synchronized synchronously or asynchronously, depending on the data analysis needs. For example, data from a CRM tool can be updated twice a week, while data from a customer app can be collected daily. 

Raw data from all sources must be cleaned, structured, and formatted to be used in data models for analysis. This process is data transformation, which includes: 

  • Normalization 
  • Cleanup 
  • Restructuring 
  • Deduplication 
  • Data validation 

Depending on the use case, it can also include summarizing, sorting, ordering, and indexing. Instead of developers and data teams manually coding transformations for each data set, they can use pre-built transformations to speed up this process and ensure future scalability. 

In the third step, the loading process transfers transformed data to a centralized destination, such as a database, data warehouse, data lake, or cloud data warehouse. Data can also be sent directly to business intelligence tools for faster analysis. 

ETL processes ensure that data is prepared and available for both real-time and batch processing workflows. Since real-time processing handles data streams as they occur, and batch processing handles data at large, predetermined intervals, businesses often struggle to prioritize between the two. Let’s examine batch vs. real-time processing in more detail. 

Real-Time Processing Explained 

Real-time analytics is the process of analyzing data as it becomes available in a system, maintaining incremental updates. Real-time data processing applies logic and mathematics to provide faster insights into this data, resulting in a more streamlined and better-informed decision-making process. 

Here’s how real-time analytics works: 

  1. Data ingestion: The process begins with continuously ingesting data from various sources such as sensors, applications, databases, or streaming platforms. This data is collected in real time and sent to the analytics system without delay. 
  2. Processing and analysis: Once the data is ingested, real-time analytics systems apply logic and mathematical algorithms to immediately process and analyze the incoming data streams. This involves performing calculations, aggregations, pattern recognition, or predictive modeling on the data to derive meaningful insights and detect trends or anomalies. 
  3. Actionable insights: Finally, the analysis results are presented in real-time dashboards, reports, or alerts, enabling decision-makers to take immediate action based on the performance metrics.  

Whether it’s identifying opportunities for optimization, detecting potential risks or threats, or personalizing user experiences, real-time analytics empowers organizations to make informed decisions swiftly and effectively. 

Two main challenges in real-time data processing are: 

  • Data latency and volume: Real-time data processing systems must handle large volumes of data streams on time, often leading to latency issues. Ensuring low latency becomes challenging as the volume of incoming data increases, potentially resulting in delays in processing and analysis. 
  • Data quality and accuracy: Maintaining data quality and accuracy in real-time processing environments can be challenging. Data streams may contain errors, duplicates, or inconsistencies, which can affect the reliability of the analysis results. Ensuring data quality and accuracy requires robust data validation, cleansing, and error-handling mechanisms in real-time processing pipelines.

Batch Processing Explained 

The key difference between real-time processing and batch data processing is that in batch data processing, large amounts of data are divided into groups according to transactions and collected over time before obtaining the results. Data integration does not take place in real time; rather, the data is collected continuously and within a certain time frame to derive insights from it. 

Here’s how batch analytics works: 

  1. Data collection: In batch data processing, data is collected over time from various sources such as databases, files, or APIs. This data is stored until a predefined batch size or time interval is reached. 
  2. Batching: Once a sufficient amount of data is accumulated, it is divided into batches based on predefined criteria such as time intervals (e.g., hourly, daily) or transaction boundaries. 
  3. Processing: The batches of data are processed sequentially using batch processing frameworks or tools. This typically involves running computations, transformations, and analyses on each batch of data independently. 
  4. Result generation: After processing, the results are generated and stored in a target destination such as a database, data warehouse, or file system. These results are typically available for analysis or reporting once the entire batch has been processed. 

Challenges in batch processing include the following: 

  • Complex maintenance: Batch processing requires ongoing maintenance to ensure the stability and reliability of pipelines. Even minor changes like incorrect date formats or encoding issues can cause pipeline failures if not appropriately addressed. 
  • Handling missing records: Dealing with missing records poses a significant challenge in batch processing. It necessitates the development of comprehensive processes to address data that cannot be processed in real time. Moreover, missing records can disrupt subsequent steps in the processing pipeline, leading to blockages in the entire process. 
  • Resource-intensiveness: Batch processing can be resource-intensive, both in terms of hardware and software requirements. Processing large amounts of data requires powerful hardware and software, which can be expensive to purchase and maintain. 

Choosing the Right Approach 

Whether you opt for real-time or batch data collection, it can mean the difference between seizing opportunities and falling behind. Let’s compare batch processing vs. real-time processing in terms of their approach to data processing and review practical use cases. 

Criteria Real-time data processing Batch data processing 
Data volume Suitable for large data streams requiring immediate processing and analysis, such as sensor data, web logs, or clickstream data. Suitable for low-volume data sets processed and analyzed regularly, such as sales data, inventory data, or customer feedback data. 
Data speed Suitable for high-speed data with rapid changes and short shelf life, such as stock prices, weather data, or social media data. Suitable for low-velocity data with slow changes and long shelf life, such as historical data, financial data, or demographic data. 
Data diversity Suitable for diverse data that is heterogeneous, unstructured, or semi-structured, such as text, images, videos, or audio. Suitable for low-diversity data that is homogeneous, structured, or tabular, such as CSV, JSON, or XML. 
Data value Suitable for high-value data directly impacting business operations and decisions, such as fraud detection, anomaly detection, or real-time recommendations. Suitable for low-value data indirectly impacting business operations and decisions, such as reports, dashboards, or trend analyses. 

Use cases for real-time processing 

Real-time processing offers benefits across industries in today’s markets. With an increasing focus on big data, this system for processing and generating insights can help companies achieve new efficiency levels. Some real-world applications of real-time processing include: 

Banking: Real-time processing enables fraud detection systems to swiftly identify suspicious transactions as they occur, preventing financial losses and protecting customer assets.  

Major multinational financial institutions like JPMorgan Chase utilize real-time processing to swiftly detect fraudulent credit card transactions. Machine learning models, such as anomaly detection algorithms or neural networks, are trained on historical transaction data labeled as fraudulent or legitimate. Based on the model scores, transactions are either flagged as potentially fraudulent or deemed legitimate, thus minimizing financial risks. 

Data streaming: Media platforms utilize real-time processing to deliver personalized content recommendations to users instantly based on their browsing behavior.  

For instance, Netflix employs real-time processing to deliver personalized content recommendations to users immediately after they finish watching a show. The system selects relevant content options from the platform’s vast library, considering factors such as genre, language, release date, and user ratings. 

Customer service structures: E-commerce platforms leverage real-time processing to provide immediate support through chatbots, resolving customer inquiries and issues in real time.  

Amazon utilizes real-time processing to power its chatbot customer support, instantly resolving customer inquiries about order status and product queries. When a customer initiates a chat conversation with the platform’s chatbot, their inquiry triggers NLP algorithms to understand the customer’s intent and the context of the message. The chatbot captures feedback from the customer interaction, such as satisfaction ratings or additional comments, and uses this data to continually refine its responses and improve user experience over time. 

Use cases for batch processing 

Unlike the fast and continuous data ingestion and output system in real-time processing, batch processing occurs only when workloads are present. In addition, computing power is used more efficiently because batch processing and implied data consistency are more economical. The sorting function divides similar jobs into groups, then processed simultaneously. This type of processing is based on the measured values and works in contrast to the action-oriented structure of real-time processing. The examples are widespread in the following industries: 

Healthcare: Batch processing is used in medical research institutes to analyze large volumes of patient data collected over time, identifying trends, patterns, and correlations to improve treatment protocols and patient outcomes. 

Renowned medical research institutes like the Mayo Clinic utilize batch processing to analyze extensive datasets of genetic information and treatment outcomes collected over the years. Researchers interpret the results of the batch processing analysis, identifying significant findings, associations between variables, and potential predictive markers for disease progression or treatment response. 

Publishing: Publishing companies utilize batch processing to analyze sales data from various channels over specific periods, allowing them to track book sales trends, identify bestselling titles, and make informed decisions on marketing strategies and inventory management. 

Companies like Penguin Random House employ batch processing to analyze monthly sales data from bookstore chains and independent bookstores, enabling them to track bestselling titles. Analysts interpret the results of the batch processing analysis, generating reports and dashboards that provide insights into sales performance and market dynamics. 

Retail: Retailers employ batch processing to analyze sales data from various store locations and online channels at the end of each day or week.  

Walmart utilizes batch processing to analyze daily sales data from its store locations and online platforms, enabling it to track product performance, identify sales trends, and optimize inventory management strategies for each store location. 

Overall, comparing real-time vs. batch processing depends on the initial business goal, and ETL development services support businesses in finding the best approach. Both play critical roles in various industries, offering unique benefits and applications. And, as usual, there’s a middle way. 

batch processing vs. real-time processing

Hybrid Approaches: Finding the Middle Ground 

Combining batch and real-time processing to meet the needs of today’s businesses is imminent: establishing a processing chain that leverages weekly (batch) sales figures to develop predictive capabilities powered by that information, as well as the acceleration of real-time decision-making to avoid missing opportunities that occur in real time. 

Hybrid approaches offer a flexible and adaptable solution that leverages the strengths of each method. Its distinctive features include: 

  • Flexibility: The hybrid approach allows businesses to choose the most suitable processing method for each task or scenario, balancing the need for immediate insights with the depth of analysis required. 
  • Scalability: Businesses can scale processing resources up or down based on workload demands, ensuring efficient resource utilization and cost-effectiveness. 
  • Optimized performance: By combining real-time processing for time-sensitive tasks with batch processing for deeper analysis, businesses can achieve optimized performance and actionable insights. 

In a hybrid approach, real-time processing may be utilized for tasks such as fraud detection, customer support, or IoT data analysis, where immediate actions are required. Simultaneously, batch processing may be employed for tasks such as analytics, reporting, or machine learning model training, where deeper analysis and historical trend identification are essential.  

Data engineering services can design a flexible and scalable architecture that accommodates both real-time and batch processing requirements. Custom-built implementation strategies include: 

  • Selecting appropriate technologies 
  • Designing data pipelines 
  • Integrating real-time and batch processing components seamlessly 

In addition, challenges arising from processing data in different ways can be mutually compensated.  

Overall, the hybrid approach offers businesses the flexibility to adapt to changing data processing needs and balance immediacy and depth of analysis. It is well-suited for industries where real-time insights and historical analysis are crucial.  


To summarize the differences between batch processing and event data stream processing in a big data infrastructure: 

  • Batch processing collects, consolidates, and processes all data at once. 
  • Real-time data processing uses a messaging system and processes each event individually. 
  • Batch processing boasts lower costs and lower infrastructure requirements. 
  • Real-time processing wins in terms of speed and timeliness of data. 

In fact, both variants will usually find their place in today’s architectures of a data-driven company, especially when there is a high variance in use cases. Of course, many companies can also use a hybrid approach – it all depends on business goals and initial resources. The Lightpoint team can help you to build an effective system to handle your data per your requirements, so contact us for customized advice.