Batch vs Streaming Data Processing

Two fundamentally different philosophies for moving and analysing data — and choosing the wrong one can cost your organisation millions in lost opportunity. By 2026, organisations that process data in real time achieve 3–5 times faster decision-making than those relying solely on batch processing, according to McKinsey. The global streaming analytics market reached $36 billion in 2026, growing at 28% CAGR, as enterprises across finance, retail, and manufacturing recognise that the hours-long delay inherent in batch processing is no longer acceptable. Yet batch processing still handles over 70% of all enterprise data workloads. Understanding when to use each approach — or how to combine them — is a strategic decision with direct bottom-line impact.

What It Is

Batch processing is the digital equivalent of the postal service: you collect a large volume of data over a period of time — hours, overnight, or even weekly — and then process it all at once in a single, scheduled operation. Think of payroll runs, end-of-day financial reconciliations, or monthly sales reports. The data is gathered, the programme runs, results are produced, and stakeholders receive them after the fact. It is predictable, efficient for large datasets, and well-understood by IT teams worldwide. Streaming processing, by contrast, is like a live telephone conversation. Data is processed continuously as it arrives — event by event, millisecond by millisecond. When a credit card transaction occurs, when a customer clicks 'add to basket,' or when a sensor on a factory floor detects a temperature anomaly, streaming systems process that event immediately and can trigger a response within seconds or even milliseconds. There is no waiting for a batch window. The two approaches are not mutually exclusive. Most mature organisations operate a hybrid architecture: streaming for use cases that demand immediacy (fraud detection, personalisation, operational alerts) and batch for use cases where completeness and efficiency matter more than speed (financial reporting, regulatory compliance, large-scale model training). The art lies in knowing which data flows require which treatment.

Why It Matters for Business

The financial impact of choosing the right processing model can be enormous. In fraud detection, a batch-based system that processes transactions overnight may detect fraudulent activity 8–12 hours after it occurs — by which time the funds are unrecoverable. Streaming-based fraud detection, as deployed by companies like Revolut and Capital One, can identify and block fraudulent transactions in under 100 milliseconds. Capital One reported a 60% reduction in fraud losses after migrating its real-time fraud detection to a streaming architecture. In e-commerce, Amazon's recommendation engine processes clickstream data in real time to personalise product suggestions within a single browsing session. The company attributes 35% of its revenue to recommendation-driven purchases — revenue that would be lost if recommendations were based on yesterday's batch-processed data. Similarly, Uber's dynamic pricing algorithm processes thousands of location events per second to adjust fares in real time, optimising both driver utilisation and rider experience. However, streaming is not universally superior. It is more expensive to operate, requires more sophisticated engineering talent, and introduces complexity in debugging and data quality assurance. Batch processing remains 40–60% cheaper per terabyte for large-scale analytical workloads and is better suited for scenarios where data completeness matters more than speed. For example, training a machine learning model on a year's worth of customer data is inherently a batch operation — you need the complete dataset, not a real-time trickle.

Enterprise Use Cases

Netflix provides a textbook example of hybrid batch-streaming architecture. Its real-time streaming infrastructure processes viewing events to make immediate content recommendations and manage adaptive video quality, while its batch systems run overnight to analyse long-term viewing patterns, inform content acquisition decisions worth billions of dollars, and generate financial reports. Netflix processes over 1 petabyte of data per day across both paradigms. In manufacturing, Siemens uses streaming data from IoT sensors on factory floors to detect equipment anomalies in real time, preventing costly unplanned downtime estimated at $50,000 per hour for large production lines. Batch processing then aggregates this sensor data over weeks and months to train predictive maintenance models that further improve accuracy. Financial services firm JPMorgan Chase processes over $6 trillion in daily transactions using a combination of streaming (for real-time risk monitoring and trading signals) and batch (for end-of-day regulatory reporting, trade reconciliation, and risk model calibration). This dual approach has reduced operational risk incidents by 35% while maintaining compliance with stringent regulatory deadlines.

Vendor Landscape

Apache Kafka, originally developed at LinkedIn and now managed by Confluent, dominates the streaming infrastructure market with a 35% share. Confluent's managed cloud service starts at approximately $0.11 per GB of data processed and scales to enterprise tiers exceeding $200,000 annually. Apache Flink is the leading stream processing framework, used by companies like Uber and Alibaba for stateful real-time computations. On the batch side, Apache Spark remains the de facto standard, with Databricks (valued at $43 billion) offering the leading managed platform, with pricing based on compute units typically ranging from $0.07 to $0.40 per DBU. Cloud-native options include AWS Kinesis and Google Dataflow for streaming, and AWS EMR and Google Dataproc for batch. Snowflake increasingly supports both paradigms through its Snowpipe streaming ingestion and batch workloads. Strategic considerations for organisations include the total cost of ownership, the skill set of the engineering team, and the specific use cases that will drive value.