Batch Processing vs Real-time Streaming

When building data pipelines, two paradigms dominate: batch processing and real-time streaming.

Both aim to turn raw data into insights. But they optimize for different tradeoffs across latency, complexity, and cost.

Let’s unpack them!

Batch Processing: The Workhorse of Historical Analysis

Batch processing operates on large volumes of data collected over a defined window. Hours, days, or weeks. It’s ideal when latency isn’t critical and efficiency is the priority.

Common use cases:

• Daily business intelligence dashboards

• Periodic ETL for warehousing

• ML model training on historical data

• Backfilling gaps in production pipelines

How it works:

1. Data is pulled from sources like logs, lakes (eg; S3), or OLTP databases.

2. Split into batches by time or volume.

3. Distributed engines (eg; Spark, Hadoop, AWS Glue) process in parallel.

4. Results are stored or consumed downstream.

Strengths:

• High throughput for large datasets

• Simpler to operate and debug

• Cost-effective for non-real-time workloads

But it’s inherently retrospective. If your SLA demands real-time insight, batch won’t cut it.

Real-time Streaming: The Engine Behind Instant Feedback

Streaming pipelines minimize the delay between event generation and system response. Instead of waiting for a full dataset, you process each event as it arrives.

Common use cases:

• Real-time metrics and alerts

• Fraud detection

• IoT telemetry processing

• Personalization (eg; recommender systems)

How it works:

1. Events are emitted from sources (sensors, mobile apps, services).

2. Buffered via brokers like Kafka, Kinesis, or Pulsar.

3. Stream processors (eg; Flink, Spark Streaming, Beam) apply transformations.

4. Results trigger alerts, update live UIs, or feed downstream systems.

Strengths:

• Sub-second latency

• Reactive, always-on systems

• Scales with high-velocity, high-volume inputs

But streaming introduces operational complexity: maintaining long-running jobs, handling backpressure, ensuring fault tolerance, and managing state across failures.

Key Tradeoffs to Consider

Streaming also brings harder tradeoffs around data consistency:

• At-most-once: Fastest but may drop data

• At-least-once: Can cause duplicates

• Exactly-once: Ideal but harder to guarantee and more expensive to achieve

You’ll need to account for idempotency, checkpointing, and state recovery to get this right.

Most Real-world Systems Use Both

In production, hybrid patterns are common:

Lambda architecture: Runs both a batch layer (for completeness and correctness) and a streaming layer (for low-latency views) in parallel. Outputs from both layers are merged to serve analytics or product features.

Kappa architecture: Simplifies the pipeline by treating all data as a stream. Even reprocessing is done by replaying from a durable event log (like Kafka), eliminating the need for a separate batch system.

For example, a fintech platform may stream transaction data to detect fraud in real time, while also running nightly batch jobs to reconcile ledgers and generate compliance reports.

Final Thoughts

The choice between batch and streaming isn’t about preference.

It’s about the latency your product can tolerate, the volume your infrastructure can handle, and the correctness guarantees your domain requires.

Get those tradeoffs right, and your data stack becomes a competitive edge, not a bottleneck.

That wraps up this week’s issue of Level Up Coding’s newsletter!

Join us again next week where we’ll explore and visually distill more important engineering concepts.