Stream Processing vs. Batch Processing

What do we mean by "Batch" vs "Stream" processing?

The difference between Batch and Stream processing

Batch processing

• You process a bounded dataset, i.e., a finite collection of data that is known (or assumed) to be complete, e.g., a file, a database snapshot, or a table of logs for the day.
• The job runs, processes the data, produces results, and finishes. There’s no ongoing “listening to new data forever”.
• Latency (time from data arrival to result) is typically higher - we don’t expect instant results; rather we process large volumes, often for analytics, ETL, and reporting.
• Because the input is static/known, you can use certain optimisations (e.g., global sorts, blocking operations) because you know the whole dataset ahead of time.

Stream processing

• Here you deal with unbounded or semi‐unbounded data: data arrives continuously (e.g., sensor readings, Kafka topics, user events) and the job may never “finish”.
• The goal is to process data as it arrives, often with low latency: near-real-time reactions. Examples: fraud detection, alerting, click-stream analytics.
• Because data keeps coming, you must handle things like time (event-time, processing-time), windows, late data, out‐of‐order events.
• You cannot assume you know the full dataset ahead of time, so you have to design for ongoing computations, incremental updates, stateful streaming.

Quick comparison table

In short: If you care about processing large volumes of historical data and you don’t need results immediately, batch is fine. If you need to react to events as they happen, stream is required.

How Apache Flink® approaches both - unified architecture

One of the big advantages of Apache Flink is that it supports both paradigms in a unified way - you don’t need two completely separate engines. Let’s dive into how.

Flink’s "stream‐native" mindset

• Flink was originally built as a stream‐native engine: meaning its core architectural model treats data as streams even when the streams are bounded.
• In fact, one of the core design philosophies: batch is just a special case of streaming.
• Because of that, even “batch” jobs in Flink often use the same fundamental runtime and APIs (for example, the DataStream API) rather than a completely separate batch engine.

Bounded vs Unbounded & Execution Mode

• In Flink you can specify an execution mode: STREAMING or BATCH. For example, the documentation: “Apache Flink’s unified approach to stream and batch processing means that a DataStream application executed over bounded input will produce the same final results regardless of the configured execution mode.”
• The boundedness of the input matters: if all sources are bounded, then a job is bounded; if at least one is unbounded, the job is unbounded. The BATCH execution mode only makes sense for bounded jobs.
• When you choose BATCH mode, Flink can apply additional optimisations (e.g., more efficient joins/aggregations, shuffle strategies) because it knows whole input is finite.

Unified API & reuse

• Because of this unification, you can often write (or reuse) code that runs either as streaming or batch with minimal changes if the logic itself is compatible. The Table API/SQL in Flink is unified for both batch and stream.
• This helps in scenarios where you want to run the same logic over historical data (batch) and then continue with live incremental data (stream). For example: initial back‐fill + ongoing real-time processing.

Optimisations when batch mode is used

Some specifics of how Flink optimises when working in BATCH mode:

• Because the input is finite, Flink can use blocking operators (operators that wait for all input before proceeding) and global sorting of keys, which you might not want in an unbounded stream scenario.
• The shuffle and task scheduling can be more efficient: tasks can materialise intermediate results differently (even spilling less, materialising more aggressively) because termination is guaranteed.
• State management can be simplified: for bounded jobs you might not need extensive checkpointing/back‐pressure in the same sense as streaming continuous jobs.

Key implication: one engine, two modes

So practically:

• You use Flink’s DataStream (or Table/SQL) API.
• You choose your sources (bounded vs unbounded).
• You set execution mode accordingly (STREAMING vs BATCH).
• Flink’s runtime adapts optimisations under the hood.Therefore you avoid the “two completely separate code‐bases” problem.

Use-cases: when to pick batch vs streaming (in Flink)

Let’s talk about when you might choose one vs the other, especially in a Flink context.

Use-cases favouring batch mode

• You have a large historical dataset you want to process (say logs for last month) where latency isn't critical; you just want a final result (report, ETL load).
• You want to perform heavy aggregations, full joins, sorting across the whole dataset. Because input is bounded, optimisations can be applied.
• You’re doing a “back-fill” job: fill up data until now, then you might switch to streaming. Flink supports that transition nicely.
• Example: nightly job processing overnight logs, generating daily summary reports.

Use-cases favouring streaming mode

• Data arrives continuously and you need results quickly (near real-time), e.g., fraud detection, monitoring, anomaly alerts, live dashboards.
• You need to maintain state over time, use event‐time semantics, handle windows, out‐of‐order events, and you cannot wait for all data to arrive.
• Example: online click-stream processing, sensor data pipelines, alerting on live transactions.

Hybrid / mixed scenarios

• Many realistic architectures involve both: back‐fill large historical data (batch) then switch to live processing (stream). Flink’s unified model supports this.
• You might also want incremental full + incremental updates: process backlog (batch), then process new events as stream.
• It’s worth noting that having a pipeline that mixes batch and streaming within the same job is possible but has constraints. For example, the input sources must support bounded/unbounded appropriately, and you must consider execution semantics accordingly.

Rule-of-thumb in Flink

• If your input is unbounded and you care about low latency → streaming mode.
• If your input is bounded (finite) and latency is less critical than throughput/complete result → batch mode (or bounded streaming).
• If you want “both” (initial historical + ongoing real-time) → consider unified pipeline with Flink, perhaps start bounded, then switch to streaming.

Practical considerations & differences in Flink implementation

When you’re working with Flink (or designing pipelines) you’ll want to keep in mind some of the implementation details and trade-offs.

Time semantics, state, windows

• In streaming mode, you’ll often work with event time (when the event occurred) vs processing time (when Flink sees it). Flink supports both.
• For windows (e.g., tumbling, sliding windows) you need to consider late events, watermarking, state size. This is a streaming concern.
• In batch mode, because the data is bounded, these concerns simplify: you might not need complex watermarking or window‐handling of late data (assuming you ingest all data).

Fault tolerance, checkpointing, resource usage

• Streaming mode needs to run indefinitely, so you care about fault tolerance (checkpoints, state backends) and resource usage over time.
• In batch mode, since job terminates, you may rely less on constant checkpointing; Flink’s BATCH mode may reduce overhead for those parts. For example, one user said: “On the docs it’s said that it does not use checkpointing, back-pressure, nor even a RocksDB, and that ‘keys are sorted and processed sequentially’.”
• Resource usage: streaming jobs hold resources long-term; batch jobs can release when done.

Performance & optimisations

• Because Flink uses the same engine, you get benefits of pipelining and parallel processing in both modes.
• In batch mode, Flink can apply more aggressive optimisations: for example global sort, blocking operators, more efficient shuffles.
• However: streaming latency demands may limit some optimisations (you can’t wait for whole dataset).
• One article claims Flink can provide “up to 100× better throughput and latency for streaming workloads” compared to older batch-native systems when used appropriately.

Code and API reuse

• Because the DataStream API and Table/SQL API are unified, you can write code that works in both modes (with minimal changes) if your logic doesn’t rely on e.g., the job terminating vs being infinite.
• That’s really valuable: less duplication between batch & streaming code‐bases.

Things to watch out for

• Just because you’re using bounded input doesn’t necessarily mean you should auto‐choose batch mode: if you still need near‐real-time updates you might choose streaming even on bounded input.
• If your sources don’t support bounded/unbounded semantics properly (old connectors vs new), you may face issues. For example: only some sources (e.g., KafkaSource) support both bounded and unbounded mode.
• Be careful about mixing bounded/unbounded sources in the same job: unbounded means you’re effectively streaming.
• Ensure you pick the correct execution mode (BATCH vs STREAMING) so Flink can apply the correct optimisations.

Summary: when to use what, and how Flink fits

Let’s wrap up the main take-home points:

• Batch processing = bounded datasets + full result + throughput prioritised over latency.
• Stream processing = continuous data + incremental results + low latency prioritised.
• Flink allows both in one engine,  you don’t need entirely separate systems.
• With Flink:
  
  • If you set execution mode = BATCH and have bounded sources, you can benefit from special optimisations.
  • If you have unbounded streams (or need low latency), use execution mode = STREAMING (or default).
• Use-case oriented: choose streaming for “as events happen” processing; choose batch for “process the heavy dataset later” analytics.
• For many real systems: you’ll do both (e.g., back‐fill batch, then continuous streaming) and Flink supports that via unified API.
• Technical considerations matter: time semantics, state management, checkpointing, resource usage, connector capabilities

The code reuse advantage: with Flink you can potentially write once and run in both modes (or switch modes) rather than maintain two separate pipelines.

Example scenario: how you might implement in Flink

Here’s how you might approach a real scenario:

• Suppose you are ingesting user click events for a website you want to: 
  • A) process historical click logs (past 30 days) to compute user behaviour metrics;
  • B) from now on process live click events to update metrics in near real‐time.

Step A)  Batch part

• Use a bounded source: e.g., read logs from S3/HDFS for the last 30 days.
• Set Flink execution mode to BATCH.
• Use DataStream API (or Table API) to compute aggregates, join with user profile data, etc. Because input is bounded you get full result (e.g., user metrics snapshot).
• After job completes, write results to a data store (e.g., a database or data warehouse).

Step B)  Streaming part

• Set up a KafkaSource (unbounded) reading live click events.
• Use Flink in STREAMING mode. Use event‐time windows (e.g., tumbling 5-minute windows) to compute sliding metrics. Maintain state for each user.
• Update results in near‐real‐time (e.g., a live dashboard, alerting, incremental enrichment).
• Possibly you join with the historical snapshot from the batch job or keep that in a store.

Optionally

How Ververica Platform supports both batch and stream processing

Here are the key ways Ververica Platform (and its ecosystem) makes life easier for unified batch + streaming with Flink:

Unified Processing and Batch Support

At Ververica, we see batch as simply bounded streaming in the world of Apache Flink. Our philosophy is clear:

“The core building block is continuous processing of unbounded data streams — if you can do that, you can also handle offline processing of bounded datasets.”

That’s why our Ververica Platform fully supports bounded streaming applications — batch-type jobs that run through the same runtime. When a batch job finishes successfully, the Deployment simply transitions to the FINISHED state.

We’ve built our entire ecosystem to bring together ingestion, processing, and storage into a cohesive, cloud-native platform for both batch and streaming.

Closing the Gap Between Batch and Streaming: The Streamhouse

We introduced the concept of the Streamhouse to unify streaming and batch analytics.

“Streamhouse complements our streaming-first architecture by providing seamless integration for both batch and streaming workloads.”

Traditionally, organizations ran separate systems for batch (data lakehouses) and for real-time streaming. With the Streamhouse, we’re eliminating that divide.
You can now achieve incremental updates, low-latency analytics, and unified storage and compute for both historical and real-time data — all in one place.

Engine Enhancements and Operations Tooling

Under the hood, our VERA (Ververica Runtime Assembly) engine combines the best of open-source Flink with our own advanced innovations.

“VERA is the ultra-high-performance, cloud-native engine that optimizes Apache Flink and powers our Streaming Data Platform.”

Operationally, we provide a rich set of capabilities for managing both long-running streaming applications and bounded (batch) jobs. With our platform, you get cluster management, lifecycle UI, bounded-job awareness, deployment states, and more — all designed to simplify operations at scale.

Our ecosystem also includes built-in support for ingestion (e.g., CDC), storage (via Flink CDC + Paimon), state backends, and more — making end-to-end batch and streaming pipelines truly turnkey.

How We Can Help You

Bringing it all together:

• You can run batch (bounded) and streaming (unbounded) workloads on the same platform and tooling, minimizing fragmentation.

• You get production-grade infrastructure, tooling, and operational support — no need to build everything yourself.

• Our unified Streamhouse architecture reduces duplication — no separate data lake and streaming stack required.

• Our optimized runtime (VERA) delivers exceptional performance, resiliency, and scalability.

• You gain enterprise-grade features like governance, lifecycle management, monitoring, and cluster operations — all in one integrated platform.

Advantages of using Ververica Platform compared to open-source Apache Flink

Now let’s compare: if you just use open-source Apache Flink vs. adopt Ververica Platform (which builds on Flink). Here are the advantages (and trade-offs).

Key advantages

1. Operational & deployment maturity
   • With open-source Flink you get a world-class engine, but you often need to build out the operational layer (cluster management, scalability, multi-tenant, monitoring, UI, lifecycle). Ververica builds much of that out of the box.
   • Ververica provides enterprise support, structured best practices, and integrations suited for production-grade, large‐scale systems.
   • Example: Job lifecycle management for bounded jobs (batch) is explicitly supported. Open source might require you to build additional orchestration.

2. Unified architecture for both batch & streaming with less friction
   • While Flink supports both batch & streaming, Ververica emphasises this unification with features like Streamhouse, tightly integrated storage + compute + ingestion. That means less “glue code” or custom architecture.
   • This is especially helpful if you’re doing hybrid workloads (back-fill + real-time)  which many organisations are. Ververica is designed for that.

3. Performance and scalability enhancements
   • The VERA runtime claims improved performance, optimised for cloud-native, large state, etc. For example: “Flink-powered lakehouse with 5-10× faster processing…”
   • These enhancements might reduce need for tuning and deep internals knowledge (you still need good design).

4. Integrated ecosystem and tooling
   • With Ververica you don’t just get the engine you get ingestion (CDC), storage (Paimon), unified platform, UI, dashboards, deployment tools. That reduces engineering overhead.
   • For example: if you’re building a combined real-time + historical analytics pipeline, having Lakehouse + Streamhouse in one place is a big plus.

5. Support and risk mitigation
   • Enterprises often prefer vendor support, SLAs, proven track record for critical use cases (finance, IoT, etc). Ververica’s heritage (the original creators of Flink) and its enterprise offering give that.
   • If you rely purely on open-source Flink, you may have to invest more in in-house expertise, custom tooling, and risk mitigation.

What to consider / trade-offs

• Cost: The enterprise platform (Ververica) may involve licensing, support costs vs “free” open-source Flink.
• Flexibility: With open source you’re free to customise deeply; a managed platform may impose some constraints (though likely minor).
• Vendor lock-in: If you adopt platform-specific enhancements, you might tie in to that vendor’s ecosystem (though Ververica is built on open source).
• Overhead vs light use-case: If you have a small project, simple pipeline, maybe open source Flink is sufficient without full platform overhead.

Conclusion

When choosing between stream processing and batch processing, the decision always comes down to your data and your goals:

• If your data is finite and you can afford to wait for complete results, batch processing is usually more efficient.
• If your data arrives continuously and you need insights as it happens, stream processing is the way to go.

With Apache Flink, you don’t have to pick one world or the other; it's built around the idea that batch is just a special case of streaming. That means you can process both bounded and unbounded data within the same engine, APIs, and architecture. It gives you the flexibility to run large-scale historical analyses one moment and handle real-time event streams the next, all with consistent semantics and performance.

Now, where Ververica Platform really shines is in making all of this easier to manage and operate. It builds directly on Apache Flink but adds a full layer of enterprise-grade features: deployment automation, monitoring dashboards, lifecycle management for both long-running streaming apps and finite batch jobs, and built-in optimizations via the VERA runtime. It’s designed to take care of the “plumbing” so teams can focus on building data logic, not managing infrastructure.

On top of that, Ververica’s Streamhouse architecture unifies streaming and batch data under one consistent ecosystem, so historical and real-time analytics no longer live in separate systems. Whether you’re replaying data to rebuild models, running continuous ETL, or feeding real-time dashboards, Ververica Platform gives you a single, cloud-native environment for it all.

In short:

• Flink gives you the flexibility and unified model for stream and batch.
• Ververica Platform gives you the tools, automation, and reliability to run it at scale, in production, with much less operational friction.

If you’re experimenting, open-source Flink is an excellent place to start. But if you need to move from prototypes to dependable, large-scale, always-on workloads or want an environment where both batch and stream pipelines can coexist seamlessly, Ververica Platform is built exactly for that.