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  • Writer's pictureTimeplus Team

Batch ETL vs Streaming ETL: 8 Differences You Need To Know

You have large amounts of data waiting to be processed – user interactions, transactions, system logs – the list goes on. All this data can be great for insights but it also means dealing with the challenge of managing it in the most efficient and timely manner. To handle this, it is important to understand the difference between 2 main methods: batch ETL vs streaming ETL.


But how do you figure out which way to go? Is it about data volume or the immediacy of insights? Or maybe it is about doing things right away in real-time versus the comprehensive analysis of big batches of information at once. When faced with such decisions, having a clear picture is key – and that is exactly what this guide will provide.


We will explore 8 major differences between batch ETL and streaming ETL for a clear, concise understanding of each method so you can choose the right approach for your data processing challenges. 


What Is Batch ETL?


Batch ETL vs Streaming ETL - Batch ETL

Batch ETL is a traditional method in data processing with 3 crucial steps: extract, transform, load i.e. extract data from various sources, transform this data, and then load data into a system for bulk processing at scheduled intervals. 


The process is characterized by structured and predictable cycles. It is a dependable option when real-time data processing isn't a critical requirement. Batch ETL fits well with traditional data warehousing setups as a reliable means of extracting data for established data systems.


What Is Streaming ETL?


Batch ETL vs Streaming ETL - Streaming ETL

Streaming ETL is a modern approach to data processing that is designed to handle data in real-time as it is generated or received. Unlike traditional batch processing, which deals with data in intervals, streaming ETL continuously collects, processes, and moves data to let organizations gain insights and respond to events almost immediately. 


It is an essential component of contemporary data strategies and integrates seamlessly with advanced data architectures, stream processing platforms, and streaming ETL tools. Streaming ETL also plays a crucial role in data migration where it ensures that the data remains current and actionable.


Batch ETL vs Streaming ETL: Understanding 8 Key Differences To Revolutionize Your Data Workflow


Batch ETL vs Streaming ETL - Batch ETL vs Streaming ETL

Batch and streaming ETL cater to different data processing needs and scenarios. Let’s discuss the major differences between them to help you choose the most suitable ETL strategy for your data management objectives.


1. Data Processing Method


Batch ETL processes data in comprehensive, aggregated sets at predetermined intervals. It focuses on gathering data over a specific period and then executing the ETL process in a consolidated manner. You can use this traditional method when data is voluminous and not time-sensitive. 


On the other hand, streaming ETL operates continuously to process raw data in real-time as it is generated or received. It is particularly effective in scenarios where timely processing and analysis are crucial, like in financial trading or real-time monitoring systems.


2. Latency


By design, batch processing experiences higher data latency. The nature of batch processing inherently introduces a delay between the occurrence of a data event and the complete processing of that data for downstream consumption


This makes batch processing less suitable for applications requiring immediate data analysis and action. Essentially, it prioritizes processing large amounts of data efficiently over getting quick insights from the data.


On the other hand, streaming ETL offers low data latency and processes data immediately as it arrives. This makes it highly suitable for applications that require real-time data analysis and immediate action. In streaming ETL, the data pipeline is designed to minimize delays so you can quickly respond to emerging trends, anomalies, or critical events as they happen.


3. Data Volume Handling


Batch ETL handles large volumes of data that accumulate over time. This approach is used in situations where the overall dataset is massive and requires extensive computation, like in monthly financial reporting or large-scale data analytics.


Streaming ETL is tailored for managing data incrementally as it arrives. It can handle high-velocity data streams which makes it suitable for scenarios where data is generated continuously and needs immediate processing like IoT devices, social media feeds, or online transaction systems.


4. Scalability


Batch processing’s scalability is more rigid as it is designed to handle predetermined data volumes and intervals. It involves scaling vertically by adding more power to existing machines or horizontally by adding more machines, depending on the volume and complexity of the data. 


However, this scalability is limited by the nature of batch processing since it processes large data sets at once but less frequently.


Streaming ETL provides superior scalability as it can adapt to different data velocities and volumes in real-time. It can be scaled horizontally to handle increased data volumes and processing requirements without sacrificing performance. 


5. Complexity


Batch ETL processing is simpler in setup and operation as it deals with finite, predictable data sets. Its processes and infrastructure are designed for less frequent but large-scale data handling, which simplifies many aspects of data management. However, this simplicity also means less flexibility and slower adaptation to changing data requirements.


Streaming ETL presents higher complexity because of its nature of continuous data flow. Challenges of the streaming ETL process include real-time data ingestion, continuous transformations, and maintaining performance and data consistency. This complexity requires a higher level of technical expertise to effectively manage and operate the streaming ETL pipeline.


6. Infrastructure Needs


Batch ETL can operate with less demanding infrastructure compared to streaming ETL. It is compatible with traditional data warehouses and does not need the advanced computing resources needed for real-time processing. 


This makes batch ETL a cost-effective option for organizations with existing data infrastructures that are not geared toward real-time analytics.


Streaming ETL, however, requires more advanced infrastructure capable of handling real-time data processing and analytics. This includes powerful computing resources, high-speed data storage, and advanced networking capabilities to ensure that data is processed as soon as it arrives. The infrastructure for streaming ETL must be robust, resilient, and capable of continuous operation.


7. Error Handling


In batch ETL, errors are discovered and corrected after processing the batch. This allows for detailed error checking but it also means that if any errors affect the batch, you might have to reprocess it.


In streaming ETL, you need immediate error detection and correction to maintain data integrity and accuracy in real-time. This is crucial as errors should be identified and resolved as data flows through the system and prevent inaccurate data from affecting real-time decisions. 


Streaming ETL systems incorporate sophisticated error-handling mechanisms, including real-time monitoring and automated correction processes, to ensure the reliability and accuracy of the data processing pipeline.


8. Flexibility


Batch ETL is less adaptable to sudden changes in data or requirements. Modifications to batch ETL processes can be challenging and time-consuming as you typically have to change the scheduling and processing logic. This lack of flexibility can hold you back in dynamic environments where data requirements frequently change.


Streaming ETL offers greater flexibility and can efficiently adapt to dynamic data sources and real-time analytics needs. Its continuous nature allows for quick adjustments to the data processing pipeline. This means you can incorporate changes in data formats, sources, or processing logic with minimal disruption.


Batch ETL

Streaming ETL

Data Processing Method

Processes large sets of accumulated data at scheduled intervals.

Processes data continuously and in real-time as it arrives.

Latency

Higher latency because of intermittent processing.

Minimal latency, enabling immediate data processing and insights.

Data Volume Handling

Ideal for handling large volumes of accumulated data.

Efficient in handling high-velocity, continuously arriving data.

Scalability

Limited scalability, designed for predetermined volumes and intervals.

Superior scalability, adapts efficiently to different data volumes.

Complexity

Simpler setup and operation, dealing with finite, predictable datasets.

More complex because of continuous data flow and real-time processing.

Infrastructure Needs

Operates with traditional data warehouse infrastructure.

Requires advanced infrastructure for real-time processing.

Error Handling

Rectifies errors post-processing, with delayed correction.

Detects and corrects errors instantly, maintaining data flow.

Flexibility

Less adaptable to sudden changes.

Greater adaptability to dynamic data and real-time needs.


Diverse Applications Of Batch ETL: Exploring 5 Use Cases


Batch ETL effectively manages data in structured, time-defined periods which makes it ideal for different applications. Let’s explore its use cases with examples:


A. Daily Sales Reports


Batch ETL helps consolidate day-long sales data from multiple channels. Retail businesses use this to get a detailed view of daily operations. For example, a retail chain collects sales figures from hundreds of stores. Then, batch processing can generate a complete picture of daily sales, customer trends, and inventory requirements.


B. Monthly Financial Reconciliation & Reporting


Batch processing is pivotal in compiling and reconciling monthly financial transactions. A multinational corporation, for instance, uses batch processing to compile transactions from various global divisions. It can then create consolidated financial reports that are critical for both internal assessments and regulatory compliance.


C. End-Of-Day Stock Market Data Analysis


Batch ETL is used for processing vast amounts of stock market data after the market closes. Financial institutions utilize this for detailed analysis and strategic planning. An investment bank, for example, analyzes this data to understand daily market trends, forecast future market movements, and advise clients on investment strategies.


D. Scheduled Data Backups & Archival


Regular data backups often use batch ETL. This is especially important in industries where data integrity is critical. For instance, a large healthcare institution might perform nightly backups of patient data to ensure that information is preserved and recoverable in the event of system failures.


E. Bulk Email Processing


Batch ETL plays a major role in handling large-scale email operations like marketing campaigns. It allows for efficient, timed sending of large numbers of emails or reminders. A digital marketing agency could use this to schedule and send thousands of promotional emails while optimizing delivery times for maximum engagement.


Diverse Applications Of Streaming ETL: Exploring 5 Use Cases


Batch ETL vs Streaming ETL - Streaming ETL Use Cases

Let’s now discuss how streaming ETL is used in different industries for real-time data analysis and immediate action.


I. Real-Time Fraud Detection In Financial Transactions


In the finance sector, streaming ETL is used for detecting and addressing fraud in real-time. Banks and financial services utilize it for safeguarding customer transactions. For instance, a bank employs stream processing to identify and react to unusual transaction patterns, like unexpected large transfers, thereby protecting customers from fraudulent activities.


II. Live Monitoring & Analytics Of IoT Devices


Industries using IoT devices, like logistics or manufacturing, benefit greatly from stream processing. This real-time analysis is crucial for operational efficiency and proactive maintenance. A manufacturing company could use stream processing to continuously monitor machinery, detect anomalies instantly, and prevent potentia