Batch vs Stream Processing: Bounded Data Dumps vs Continuous Event Flow

Sun, 06 Sep 2026 10:02:40 +0900

Overview

Batch processing collects data over a period and runs computation on the whole bounded set at once, while stream processing handles each event as it arrives, continuously. The choice determines whether your system optimizes for throughput and simplicity or low latency on fresh results.

Comparison Diagram

Comparison Table

Aspect	Batch Processing	Stream Processing
Data ingestion	Data collected and stored until job triggers	Events consumed individually as they arrive
Data scope per run	Bounded, finite dataset (a file, a partition, a day’s data)	Unbounded, continuous sequence of events
Processing trigger	Scheduled interval or manual kickoff (hourly, nightly)	Continuous, triggered by each event or micro-window
Latency to result	Minutes to hours, depending on schedule	Milliseconds to seconds
State management	Recomputed fresh from full dataset each run	Maintained incrementally across the event stream
Fault recovery	Rerun the failed job against the same input	Checkpointing and replay from an offset in the log
Ordering guarantees	Whole dataset available, so ordering enforced within the job	Ordering must be explicitly handled (per-key, watermarks)
Resource usage pattern	Spiky: idle, then a burst of compute at run time	Steady, sustained compute and memory footprint

Key Differences

Batch operates on a bounded dataset, stream operates on an unbounded sequence of events
Batch trades latency for simplicity and throughput; stream trades complexity for freshness
Stream systems need watermarks to handle late or out-of-order events, which batch avoids entirely
Failure recovery in batch means rerunning the job; stream relies on checkpoint and replay semantics
Batch pipelines are easier to reason about and test since input is fixed and reproducible

When to Use Each

Batch Processing

Lambda Architecture vs Kappa Architecture: Batch-Plus-Speed vs Single-Stream Pipelines

Tue, 04 Aug 2026 05:21:28 +0900

Overview

Lambda Architecture and Kappa Architecture are two patterns for building big-data pipelines that need both real-time and historical results. Lambda runs parallel batch and speed layers that get merged at query time, while Kappa pushes everything through a single, replayable stream pipeline. The choice determines how much duplicate logic you maintain and how reprocessing actually works.

Comparison Diagram

Comparison Table

Aspect	Lambda Architecture	Kappa Architecture
Ingestion path	Raw events are forked to both a batch store and a stream processor at once	Raw events are written once to an immutable, replayable log (e.g. Kafka)
Processing model	Two independent codebases — a batch job and a stream job — implement the same logic twice	One stream-processing codebase handles both real-time and historical computation
Historical reprocessing	The batch layer periodically recomputes results over the entire raw dataset	Reprocessing means replaying the log from an earlier offset through the same stream job
State and storage	Separate batch views and speed views are maintained independently, often in different stores	A single serving store is continuously updated by the stream processor
Result merging	The query layer merges or reconciles batch and speed views at read time	No merge step — the stream processor’s output is the only view
Consistency behavior	Speed layer results are approximate until the batch layer overwrites them, so the two can disagree temporarily	One computation path avoids batch/speed drift, but correctness depends entirely on the stream engine’s guarantees
Operational overhead	Higher — two parallel pipelines to build, deploy, and monitor, with duplicated logic	Lower pipeline count, but requires a log system with long enough retention to support full replays
Best-fit scenario	Batch and speed logic genuinely differ, or the org already has mature batch infrastructure	Team wants one canonical pipeline and has a stream engine that can absorb both live and replay traffic

Key Differences

Lambda splits ingestion across a batch layer and a speed layer, while Kappa sends everything through one stream.
Reprocessing history in Lambda means rerunning a full batch job; in Kappa it means replaying the log through the same stream code.
Lambda’s query layer must reconcile two separate views; Kappa exposes a single serving store with no merge step.
Kappa’s design hinges on a durable, long-retention event log capable of full replays, which Lambda does not require.

When to Use Each

Lambda Architecture

Stream-Processing on IT Comparison

Batch vs Stream Processing: Bounded Data Dumps vs Continuous Event Flow

Overview

Comparison Diagram

Comparison Table

Key Differences

When to Use Each

Lambda Architecture vs Kappa Architecture: Batch-Plus-Speed vs Single-Stream Pipelines

Overview

Comparison Diagram

Comparison Table

Key Differences

When to Use Each