Batch vs. Streaming Data Pipelines: A Clear Guide

Rakesh singhania
3 min readFeb 8, 2024

In This article, I will help you to understand the key differences between batch and streaming data pipelines, and when to use each one. We will go through these learnings one by one.

  1. The key differences between batch and streaming data pipelines.
  2. How micro-batch and hybrid Lambda architectures work.
  3. When to use each type of pipeline based on your specific needs.

Batch Processing:

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Batch data pipelines play a crucial role when datasets demand extraction and operation as a single unit. Operating periodically on a fixed schedule, these processes ensure accuracy but might sacrifice real-time updates.

  • Works with entire datasets as a single unit.
  • Runs periodically (hours, days, weeks) or based on triggers (data size).

Ideal for situations where data freshness isn’t crucial and accuracy is paramount.

  • Use cases for batch pipelines include periodic data backups, transaction history loading, customer order processing, and even mid- to long-range sales and weather forecasting.

Streaming Processing:

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  • Processes data packets (transactions, social media activity) individually and quickly.

Used for real-time results with minimal latency.

  • Events are processed as they occur and can be stored for later analysis.
  • Examples: Social media feeds, fraud detection, user behaviour analysis, stock trading, real-time pricing.

Micro-Batch Processing:

Concept: Imagine splitting your large batch into smaller, more manageable “mini-batches”.

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Useful for near-real-time processing while balancing accuracy and latency.


  1. Data arrives continuously.
  2. Instead of waiting for a giant batch, smaller chunks are collected for a fixed period (e.g., every 5 minutes).
  3. Each mini-batch is processed as a separate batch task with the same steps as full batch processing (extraction, transformation, loading).
  4. Results are typically accumulated and made available after each mini-batch completes.


  • Lower latency than traditional batch processing by providing quicker updates.
  • Simpler setup compared to full-fledged streaming pipelines.
  • Maintains the advantages of batch processing, like data cleaning and accurate results.


  • Still not truly real-time, there’s latency within each mini-batch processing time.
  • Might not be suitable for high-velocity data streams where millisecond updates are crucial.

Hybrid Approach : Lambda Architecture


Combines a batch layer for historical data processing with a streaming layer for real-time updates.


  1. Data flows into both layers simultaneously.
  2. Batch layer processes entire datasets periodically (e.g., daily), ensuring complete and accurate historical data.
  3. Streaming layer processes data in real-time, providing near-instantaneous insights.
  4. A serving layer integrates results from both layers, offering a unified view of historical and real-time data.


  • Provides both historical accuracy and real-time insights.
  • Flexible approach to handle diverse data requirements.


  • More complex to design, implement, and maintain compared to simpler architectures.
  • Requires managing two separate data processing pipelines.
  • Might be overkill for simpler use cases where high accuracy or real-time updates aren’t essential.

Choosing the Right Pipeline:

  • Consider your latency requirements: Need real-time updates? Choose streaming.
  • How important is accuracy? Batch processing allows for data cleaning and higher quality output.
  • What is your data size and processing needs? Batch excels with large datasets, while streaming handles continuous data streams.


There’s no one-size-fits-all solution. Choose the pipeline that best aligns with your specific use case and data needs.



Rakesh singhania

As a student of technology, each day I take a single step forward on the path of learning.