How to Choose Between Batch and Stream Processing?
When to Use Batch vs Stream Processing: Key Differences, Use Cases, and Tools for Data Engineers
When designing a data pipeline, we often face a series of architectural decisions that shape how data moves through the system. We ask ourselves:
How will we ingest the data? Is it via API, streaming events, or batch files?
Where will we store the data? Will it live in a data lake, a warehouse, or an operational database?
What are the downstream use cases? Are we building dashboards, feeding machine learning models, triggering alerts, or powering business reports?
Each of these decisions plays a role in defining the overall system. But in this post, we will focus on:
How should the data be processed?
Should we process data in large batches at scheduled intervals, in micro-batches that run every few minutes, or as soon as it arrives in near real time?
There are circumstances where both approaches succeed or fall short depending on how well they match the use case, the team’s maturity, and the operational environment.
In this post, we’ll explore:
What are batch processing and stream processing?
How do they differ?
How often does each approach appear in typical workflows?
Popular tools used in modern data teams.
Best practices for choosing the right method.
What Is Batch vs Stream Processing?
Understanding how and when data is processed is fundamental when designing a data pipeline. Processing strategies typically fall into two categories: batch processing and stream processing. Each has its strengths and use cases.
Batch Processing
Batch processing refers to the method of collecting data over a period of time, storing it, and then processing it all at once. This is typically done on a fixed schedule such as hourly, daily, or weekly.
For example, a daily ETL pipeline that reads from a data lake, performs joins and aggregations, and loads results into a warehouse for business reporting is a classic batch use case.
Key characteristics:
Processes large volumes of data at once
Operates on a schedule or trigger (not continuously)
Prioritises data completeness and consistency over speed
Easier to test and debug
Typical use cases:
Daily reporting dashboards
Historical data backfills
Data lake to warehouse transfers
If you want to learn more about Batch processing, check out this post as part of our Comprehensive Data Engineering Interview Preparation Guide1.
Stream Processing
Stream processing refers to handling data as it arrives, often within milliseconds to seconds. Instead of waiting for all data to be collected, the system continuously ingests and processes events in real time or near-real time.
For example, a fraud detection system that flags suspicious transactions the moment they happen is a clear case for stream processing. Another example is real-time user activity tracking for recommendation engines.
Key characteristics:
Processes data one record (or small windows) at a time.
Operates continuously with minimal delay.
Enables real-time insights and reactions.
Requires handling of out-of-order or late-arriving data.
Typical use cases:
Monitoring systems and real-time alerts
Event-driven microservices
Personalisation and user journey tracking
IoT sensor data analysis
Key Differences and Boundaries
If the use case can tolerate delay and favours complete, consistent results, batch processing is often the better starting point. If insights need to be timely or the system needs to respond immediately to events, stream processing may be a better fit.
Also, some modern systems adopt a hybrid approach, such as the Lambda architecture, where stream processing handles real-time data while batch processing ensures long-term accuracy through periodic reprocessing. Although we don’t see such architecture in practice these days, it may still be useful to keep that in mind.