Apache Spark vs Apache Flink: Choosing the Right Streaming Framework

Apache Spark and Apache Flink have long been two of the most important frameworks in large-scale data processing. Both were built to handle massive workloads across distributed systems, and both have evolved into versatile engines for analytics and stream processing. Yet, in 2025, the question isn’t just which framework is faster or more scalable — it’s which one aligns with how modern data pipelines actually work.

Data teams today are moving away from periodic batch jobs toward real-time data movement, change data capture (CDC), and streaming analytics. While Spark and Flink remain foundational technologies, their roles are shifting. In many cases, data engineers no longer want to build and maintain full streaming infrastructure just to get operational data into a warehouse, lake, or downstream system.

This article compares Apache Spark, Apache Flink, and introduces Estuary, a modern alternative that simplifies real-time data movement. You’ll see how each fits into today’s data ecosystem, what has changed with Spark 4.0 and Flink 2.0, and how to choose the right solution for your team’s needs.

What Are Apache Spark and Apache Flink?

Apache Spark

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Apache Spark is a distributed data processing framework originally built for high-performance batch analytics. It provides a unified engine for processing large datasets across clusters of machines, supporting languages like Python, Java, Scala, and SQL.

Over time, Spark evolved beyond batch workloads with the introduction of Structured Streaming, which processes micro-batches of data in near real time. This approach simplifies development by letting engineers reuse the same APIs for batch and streaming tasks, but it still operates in small, time-based batches rather than event-by-event streams.

Spark is widely used for:

• Batch ETL pipelines and large-scale data transformations
• Machine learning and model training (via MLlib)
• Ad-hoc analytics and interactive SQL workloads
• Integration with data warehouses, data lakes, and cloud platforms such as Databricks and AWS EMR

Apache Flink

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Apache Flink is a stream-first distributed processing engine designed to handle unbounded, continuous data streams. It provides true event-driven processing, allowing data to be processed as it arrives rather than in micro-batches.

Flink excels at stateful stream processing, enabling complex event processing (CEP), real-time joins, and windowed aggregations with millisecond latency. It also provides APIs for batch jobs, though its core strength lies in continuous streaming pipelines.

Common use cases for Flink include:

• Real-time event analytics and alerting
• Fraud detection and monitoring pipelines
• Stateful data processing and stream enrichment
• Integration with Kafka, Pulsar, and Debezium for CDC workloads

Key Architectural Difference

Spark vs Flink: The Core Differences

While both Spark and Flink can process streaming and batch data, they differ in how they approach computation, latency, and state handling. Understanding these differences helps you choose the right tool for your pipeline.

1. Processing Model

• Spark: Uses a micro-batch model for streaming through Structured Streaming. Data is divided into small batches processed at regular intervals. This design simplifies scalability and fault tolerance but introduces slight latency between event ingestion and computation.
• Flink: Follows a true stream processing model where each event is processed as it arrives. This continuous model supports sub-second latencies and is better suited for event-driven or real-time analytical applications.

2. Latency and Throughput

• Spark’s micro-batch architecture typically operates with latencies in the range of hundreds of milliseconds to seconds, depending on the configuration.
• Flink achieves much lower latency, often in the tens of milliseconds, by processing individual events in real time.
• For high-throughput pipelines that must react to data instantly, Flink has a clear edge.

3. State Management and Checkpointing

• Spark: Maintains state through periodic checkpoints and recovery logs. It offers fault tolerance, but state updates are tied to micro-batch intervals.
• Flink: Provides native stateful stream processing. Each operator can maintain local state that is asynchronously checkpointed. This enables more complex operations such as window joins and real-time aggregations while preserving exactly-once guarantees.

4. Ecosystem and Integrations

• Spark: Has a mature ecosystem with tight integration into analytics platforms like Databricks and cloud data warehouses. It also supports SQL, machine learning (MLlib), and graph processing (GraphX).
• Flink: Integrates deeply with event streaming systems such as Kafka, Pulsar, and Debezium. It supports the Table API and SQL for declarative stream processing and is commonly used in real-time analytics and CEP systems.

5. Fault Tolerance and Exactly-Once Guarantees

Both frameworks support fault tolerance and exactly-once semantics but achieve it differently:

• Spark relies on micro-batch recovery and checkpointing for consistency.
• Flink implements exactly-once delivery at the operator level, giving it finer control and stronger real-time guarantees.

6. Operational Complexity

Running either Spark or Flink at production scale requires significant operational expertise. Clusters must be tuned for job scheduling, memory, checkpoint intervals, and recovery mechanisms.

• Spark is easier to operate for batch or hybrid workloads due to its unified engine.
• Flink demands deeper understanding of streaming semantics and checkpoint tuning but rewards that effort with lower latency and greater control.

Summary Table

Spark 4.0 and Flink 2.0: What’s New in 2025

Both Apache Spark and Apache Flink have seen major upgrades in the last year. These releases mark an effort to modernize each framework for real-time, cloud-native workloads and to close long-standing gaps in usability, performance, and developer experience.

Apache Spark 4.0 Highlights

Spark 4.0 continues the trend toward unifying batch and streaming workloads while improving flexibility for interactive and cloud-based deployments.

• Adaptive Query Execution (AQE) enhancements: Smarter dynamic partition pruning and automatic join optimization for faster query execution.
• Expanded Spark Connect API: Decouples clients from the Spark driver, improving flexibility for interactive notebooks and remote execution.
• Streaming join improvements: Better handling of late data and watermarks in Structured Streaming.
• Python API parity: Major improvements in PySpark performance and feature alignment with Scala APIs.
• Enhanced Kubernetes integration: More efficient resource scaling and containerized execution for multi-tenant environments.

Impact:
Spark 4.0 brings the platform closer to true hybrid processing, making it more capable for continuous workloads while retaining its strength in batch analytics and machine learning.

Apache Flink 2.0 Highlights

Flink 2.0 represents a shift toward simplifying stream processing without sacrificing precision. The release focuses on developer productivity, performance, and unification of APIs.

• Unified streaming and batch APIs: Developers can now define jobs that adapt to either mode without rewriting logic.
• Declarative Stream SQL: Simplified SQL-based stream definitions with automatic parallelism and checkpointing.
• Improved state management: Lower-latency asynchronous checkpoints and more efficient state backends.
• Table Store integration: Better support for hybrid streaming and analytical queries on continuously changing datasets.
• Enhanced observability: Built-in metrics for operator backpressure, state size, and checkpoint latency.

Impact:
Flink 2.0 positions the framework as a complete event-processing ecosystem, capable of handling both real-time event streams and bounded analytical jobs under a single API surface.

Convergence and Context

With these updates, both Spark and Flink are moving toward unified data processing. Spark is adding finer-grained streaming features, while Flink is improving usability and analytical flexibility.
Despite the convergence, their philosophies remain distinct:

• Spark prioritizes simplicity and ecosystem integration.
• Flink prioritizes real-time accuracy and event-driven computation.

The next question is when each should be used, and where operational complexity or latency requirements dictate a specific choice.

Choosing Between Spark and Flink

Apache Spark and Apache Flink both offer scalable, fault-tolerant distributed data processing. The decision between them depends on the type of workload, latency requirements, and the operational model of your data platform.
Below are the main criteria to guide your choice.

1. Latency Requirements

• If your workload involves high-frequency event streams that need millisecond-level responsiveness (for example, fraud detection, monitoring, or alerting), Flink is the better fit.
• If low latency is acceptable, such as in micro-batch ETL or near-real-time dashboards, Spark Structured Streaming is typically sufficient.

2. Data Type and Processing Model

• Bounded data (historical datasets): Spark performs better for traditional ETL, aggregation, and analytics jobs.
• Unbounded data (continuous streams): Flink handles real-time ingestion, windowing, and event correlation natively.

3. Ecosystem and Team Skills

• If your team already uses Databricks, Delta Lake, or MLlib, Spark offers smoother integration.
• If your environment is built around Kafka, Pulsar, or Debezium, Flink integrates more naturally for event-based processing.

4. Cost and Operational Complexity

• Both frameworks can scale horizontally, but Flink jobs typically require more careful tuning and monitoring due to their long-running, stateful nature.
• Spark is easier to manage for hybrid batch and streaming workloads, especially in cloud-managed services.

5. Real-Time Guarantees

• Flink provides stronger exactly-once guarantees at the operator level, making it more reliable for mission-critical streaming pipelines.
• Spark offers near-exactly-once semantics through micro-batch recovery, which is generally sufficient for analytics but not for event-driven applications that require strict consistency.

6. Deployment Flexibility

• Spark: Integrates tightly with Kubernetes, YARN, Databricks, and EMR.
• Flink: Offers better support for native streaming clusters, stateful jobs, and auto-scaling through Flink Kubernetes Operator and StatefulSets.

Decision Summary

For many modern teams, the decision is less about which is faster and more about how much operational complexity they want to manage. In practice, most organizations use a combination of both or complement them with a managed ingestion layer that handles real-time data delivery before Spark or Flink even come into play.

That layer is where Estuary Flow enters the picture.

The Missing Piece: Real-Time Data Movement

While Spark and Flink are powerful for computation, most real-time projects fail long before processing begins. The real challenge lies in getting data from source systems into a streaming state that these engines can consume reliably and at scale.

Building ingestion pipelines for databases, APIs, and SaaS tools often requires a mix of connectors, change data capture (CDC) tools, message queues, and transformation logic. Maintaining all of that introduces significant operational burden. Teams spend more time managing ingestion than developing analytics or machine learning workflows.

Why Traditional Ingestion Is a Bottleneck

• High setup complexity: Capturing change events from systems like PostgreSQL, MongoDB, or Salesforce often involves custom connectors or Debezium integrations.
• Operational overhead: Managing checkpoints, retries, schema drift, and backfills requires ongoing tuning and monitoring.
• Latency trade-offs: Even well-tuned Flink or Spark jobs depend on upstream data freshness. Without a reliable real-time ingestion layer, true streaming performance cannot be achieved.
• Inconsistent delivery: Achieving exactly-once delivery across diverse systems is difficult without transactional guarantees throughout the pipeline.

The modern data ecosystem increasingly recognizes that data movement is as critical as computation. Instead of building and maintaining ingestion code within Spark or Flink jobs, many teams are adopting platforms designed specifically for real-time CDC, schema evolution, and exactly-once delivery.

This is the gap that Estuary fills. It abstracts away the ingestion and synchronization layers so that engineers can focus on computation and analytics rather than pipeline maintenance.

Build your first real-time data pipeline with Estuary in under 10 minutes. Get Started

Estuary: The Modern Alternative (and Complement)

Estuary Flow is a real-time data movement platform that bridges the gap between operational systems and analytical engines like Spark or Flink. Instead of managing ingestion pipelines or deploying stream processing clusters, Estuary provides a managed layer for continuous, exactly-once data synchronization across databases, SaaS tools, message queues, and cloud warehouses.

How Estuary Flow Works

Estuary captures changes from source systems using Change Data Capture (CDC) and streams them to targets in real time. Each pipeline in Flow consists of:

• Captures: Real-time connectors that extract data from sources such as PostgreSQL, MySQL, MongoDB, Kafka, or SaaS APIs.
• Collections: Logical datasets within Flow that store and validate events using JSON schemas.
• Derivations (optional): Transformations written in SQL or TypeScript that process, filter, or aggregate data on the fly.
• Materializations: Connectors that continuously deliver processed data to destinations like Snowflake, BigQuery, Databricks, Elasticsearch, or Kafka.

Because Flow enforces schema consistency and transactional guarantees across all stages, each document is delivered exactly once without duplication or loss, even during backfills or connector restarts.

Key Advantages for Data Engineers

• Real-Time CDC Without Infrastructure Management: Capture and stream updates from production databases and APIs without managing Kafka clusters, Debezium, or custom ingestion jobs.
• Exactly-Once Guarantees Across Sources and Sinks: Flow ensures consistency through transactional materializations, eliminating the need to build complex deduplication or checkpoint logic.
• Built-In Schema Evolution: Automatic detection and handling of schema changes prevent downstream breakage when columns are added or modified.
• Integrated Transformations: Engineers can write lightweight transformations directly in SQL or TypeScript within Estuary, reducing the need for separate compute stages.

Seamless Integration With Existing Systems

Flow integrates easily with Spark or Flink through:

• Kafka compatibility viaDekaf: Allows consuming Flow collections as Kafka topics.
• Cloud storage outputs: Materialize collections as Parquet or JSON files for Spark ingestion.
• dbt Cloud triggers: Automatically start dbt jobs when new data arrives.
• Deployment Flexibility: Flow supports fully managed SaaS, private deployment, or Bring Your Own Cloud (BYOC) options for enterprises that require network isolation or compliance control.

Example Pipelines Using Estuary Flow

• PostgreSQL to Snowflake: Capture database changes and stream them into Snowflake using Snowpipe Streaming for real-time analytics.
• MongoDB to Elasticsearch: Keep search indexes up to date as documents change in MongoDB.
• DynamoDB to Databricks: Deliver operational data into Databricks Delta tables for machine learning or analytics.

Estuary Flow does not replace Spark or Flink; it simplifies everything that happens before and after computation. Data engineers can use Estuary for reliable ingestion, transformations, and delivery, while Spark or Flink handle heavy analytical processing or event-driven applications.

Coexistence: Using Estuary with Spark or Flink

Estuary Flow is not a replacement for Spark or Flink. Instead, it complements them by handling the hardest parts of data ingestion, schema evolution, and delivery, allowing Spark and Flink to focus on computation and analysis. In most production environments, combining these systems results in a simpler, more reliable, and scalable architecture.

1. Estuary + Spark

Spark is excellent for analytical workloads, machine learning, and large-scale ETL transformations. However, it performs best when data is already available in a clean, structured format.

Estuary Flow can continuously stream data into Spark-compatible environments such as:

• Delta Lake or Databricks: Estuary materializes collections directly to cloud object storage in Parquet format, enabling near real-time updates in Delta tables.
• BigQuery or Snowflake feeding into Spark jobs: Estuary keeps these analytical stores up to date, and Spark can then query or process that data on demand.
• dbt Cloud-triggered Spark pipelines: Estuary can trigger dbt Cloud jobs automatically when new data arrives, ensuring that transformations always operate on current data.

Benefit:
Data engineers avoid building and maintaining ingestion scripts or CDC connectors, reducing latency and improving data freshness for Spark jobs.

2. Estuary + Flink

For teams using Flink for real-time stream processing or event correlation, Estuary provides a ready-to-use ingestion layer that simplifies event delivery and ensures transactional consistency.

Integrations include:

• Kafka interoperability via Dekaf: Estuary collections can appear as Kafka topics, allowing Flink jobs to consume real-time updates without running a separate Kafka cluster.
• Enrichment and routing: Estuary can preprocess data before it reaches Flink, such as normalizing schemas or merging multiple sources.
• Downstream delivery: Flink results can be written back into Flow for distribution to multiple destinations such as databases, warehouses, or APIs.

Benefit:
Flink focuses purely on stream computation, while Flow manages ingestion, consistency, and output synchronization. Estuary provides exactly-once semantics for compatible destinations and at-least-once delivery with idempotent apply for others, significantly reducing operational overhead.

3. Hybrid Architecture Example

A modern real-time data stack might look like this:

1. Estuary Flow captures and standardizes data from multiple operational systems in real time.
2. Flink consumes these standardized streams for low-latency event processing, anomaly detection, or CEP workloads.
3. Spark periodically processes enriched or aggregated data for analytical reporting, ML training, or large-scale transformations.
4. Estuary continuously materializes the outputs to cloud warehouses, dashboards, or search systems.

This architecture minimizes manual data movement and provides consistent, up-to-date data across all systems without complex orchestration.

Cost and Operational Comparison

Spark, Flink, and Estuary Flow all enable large-scale data processing, but they differ significantly in infrastructure requirements, maintenance effort, and total cost of ownership.

1. Infrastructure and Resource Costs

• Apache Spark: Requires compute clusters for batch and streaming jobs. Costs grow with job complexity and data volume, though managed services like Databricks or EMR simplify scaling.
• Apache Flink: Stream-first architecture means long-running jobs are always active. This results in steady infrastructure costs even during idle periods, especially when maintaining checkpoints and state backends.
• Estuary Flow: Operates as a managed streaming platform. You pay based on data usage and active connectors rather than cluster size or idle capacity.

Result:
Estuary typically offers lower total cost of ownership for continuous ingestion, while Spark and Flink costs scale with compute load.

2. Engineering and Maintenance Overhead

• Spark and Flink: Both require cluster management, monitoring, version upgrades, and tuning for long-running jobs. Achieving stable real-time performance often demands deep expertise in serialization, state tuning, and recovery mechanisms.
• Estuary Flow: Eliminates most operational complexity. Pipelines are configured through a UI or CLI and automatically maintain consistency, retries, and schema evolution. Engineers focus on data modeling and analytics instead of infrastructure.

Result:
Teams using Flow can build and maintain pipelines in hours rather than weeks, freeing engineering time for higher-value work.

3. Deployment and Scaling

• Spark: Scales through Kubernetes or managed services, but scaling must be planned around job cycles or batch intervals.
• Flink: Scales dynamically but requires manual configuration for parallelism, state snapshots, and checkpoints.
• Estuary Flow: Abstracts compute and backpressure handling, so users do not manage or tune clusters directly.

Result:
Estuary provides elastic scaling behind the scenes, while Spark and Flink require explicit capacity planning and monitoring.

4. Cost Predictability

Traditional compute frameworks often produce unpredictable billing when jobs overlap or data spikes occur.
Estuary Flow uses usage-based pricing tied to data volume and active connectors, with pre-pay options for predictable budgeting.

Result:
Estuary makes it easier to forecast spend and align data operations cost with actual business activity.

Spark and Flink are powerful but infrastructure-heavy, while Estuary Flow offers a managed alternative that minimizes operational cost and risk.
For most teams, combining Estuary’s ingestion layer with Spark or Flink for computation provides the best balance between cost efficiency, reliability, and scalability.

Final Decision Matrix

Conclusion

Apache Spark and Apache Flink remain foundational technologies for distributed data processing. Spark excels in large-scale batch analytics and machine learning, while Flink is built for continuous, low-latency stream processing and event-driven workloads. Both are powerful, but they require significant infrastructure and operational management to achieve true real-time performance.

Estuary addresses a different but equally critical layer of the modern data stack — continuous data movement. It captures changes from operational systems in real time, enforces schema integrity, and delivers updates exactly once to downstream destinations. In optimized configurations and supported connectors, Flow achieves sub-second latency, enabling near-instant data availability across systems.

For most data teams, the ideal approach is to combine these strengths:

• Use Estuary to capture and distribute real-time data reliably.
• Use Flink for low-latency stream processing and complex event logic.
• Use Spark for analytical, ML, and large-scale transformation workloads.

Together, these technologies form a complete, future-ready data architecture: ingestion and movement handled by Estuary, stream computation powered by Flink, and heavy analytics processed by Spark.

This layered strategy delivers real-time performance with far less operational complexity, allowing data engineers to focus on building insight-driven applications instead of managing infrastructure.

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