Stream Processing Tools Compared: Flink, Kafka Streams, Spark, and More

Picking a stream processing tool used to mean choosing between Spark and Kafka Streams. The field is wider now. Apache Flink has become the default for stateful workloads. SQL-native engines like RisingWave and Materialize target teams that don’t want to write Java. And managed platforms bundle stream processing into broader data pipeline products.

This guide compares eight stream processing tools across the dimensions that matter most: latency characteristics, state management, SQL support, operational burden, and cost. If you’re evaluating options for a new project or considering a migration, this should save you a few weeks of proof-of-concept work.

Quick comparison table

Tool	Latency	State Management	SQL Support	Deployment Model	Best For
Apache Flink	Low ms	RocksDB, incremental checkpoints	Flink SQL (full)	Self-managed or managed (AWS, Confluent)	Stateful event processing at scale
Kafka Streams	Low ms	RocksDB, changelog topics	None (Java DSL only)	Embedded in JVM apps	Kafka-native microservices
Spark Structured Streaming	100ms+ micro-batch, ~10ms continuous (experimental)	In-memory + checkpoint to HDFS/S3	Spark SQL (full)	Self-managed, Databricks, EMR	Teams already running Spark batch jobs
Apache Beam	Varies by runner	Runner-dependent	Beam SQL (limited)	Dataflow, Flink, Spark runners	Multi-cloud portability
ksqlDB	Low ms	RocksDB (Kafka-backed)	ksql (Kafka-specific)	Confluent Cloud or self-managed	SQL queries over Kafka topics
Materialize	Low ms	Differential dataflow (memory)	PostgreSQL-compatible	Managed SaaS	Incremental view maintenance
RisingWave	Low ms	S3-backed shared storage	PostgreSQL-compatible	Self-managed or cloud	SQL-first streaming with PG compatibility
Streamkap	Sub-250ms end-to-end	Managed (Flink-based)	SQL, Python, TypeScript	Fully managed SaaS	CDC pipelines with built-in transforms

1. Apache Flink

Apache Flink is a distributed stream processing framework designed around continuous data flows. Unlike batch-first systems that bolt on streaming, Flink treats streams as the primary abstraction. Batch is just a bounded stream.

Architecture

Flink runs as a cluster with a JobManager (coordinator) and one or more TaskManagers (workers). State is stored in RocksDB on local disk and periodically checkpointed to a distributed filesystem like S3 or HDFS. Checkpoints are incremental, meaning only changed state gets written. This keeps checkpoint overhead low even for multi-terabyte state.

Latency and throughput

Flink processes events one at a time (not micro-batched), which gives it single-digit millisecond latency in most configurations. Throughput scales horizontally by adding TaskManagers. Production deployments regularly handle millions of events per second per job.

State management

This is where Flink separates itself. It offers exactly-once state consistency through aligned and unaligned checkpoints, supports keyed state and operator state, and can manage terabytes of state per job. Savepoints let you stop a job, modify the code, and resume from the same state.

SQL support

Flink SQL is a full SQL layer that compiles down to the same dataflow runtime. You can create tables, define watermarks for event time, run joins (including temporal joins), and write results to sinks—all in SQL. It’s mature enough for production use, though complex windowing logic sometimes requires dropping into the Java/Scala DataStream API.

Pricing

Open source. Self-managed clusters cost whatever your compute runs on. Managed options include AWS Managed Flink (formerly Kinesis Data Analytics), Confluent Cloud for Flink, and Ververica Platform. Managed pricing varies from $0.11/hour per compute unit (AWS) to consumption-based models.

Pros

• True stream-native architecture with low latency
• Battle-tested state management at terabyte scale
• Strong SQL support and active open-source community
• Exactly-once processing guarantees

Cons

• Steep operational learning curve for self-managed clusters
• JVM tuning required for large state workloads
• Job upgrades with state compatibility need careful planning
• Managed offerings can get expensive at scale

Best for

Teams building stateful event-driven applications: fraud detection, real-time aggregations, complex event processing, or any workload where correctness of state matters more than simplicity of setup.

2. Kafka Streams

Kafka Streams is a Java library for building stream processing applications that read from and write to Apache Kafka. It’s not a cluster or a service—it’s a dependency you add to your JVM application.

Architecture

Each Kafka Streams instance runs inside your application process. Parallelism comes from running multiple instances of your app, each assigned a subset of Kafka partitions. There’s no separate cluster to manage. State is stored in local RocksDB instances and backed up to Kafka changelog topics for fault tolerance.

Latency and throughput

Kafka Streams processes records one at a time with millisecond-level latency, comparable to Flink. Throughput scales with the number of Kafka partitions and application instances. It won’t match Flink on raw throughput for large-scale jobs, but for partition-level workloads it’s fast.

State management

Local RocksDB stores with automatic changelog-based recovery. When an instance fails, another instance restores state from the changelog topic. State size is limited by local disk. Interactive queries let you read state from running instances, which is useful for building queryable microservices.

SQL support

None. Kafka Streams is a Java DSL (with a Scala wrapper). You write topology code using operations like map, filter, groupByKey, and aggregate. If you want SQL over Kafka, look at ksqlDB (covered below).

Pricing

Open source, included with Apache Kafka. You pay for the Kafka cluster and whatever compute runs your application instances.

Pros

• No separate cluster—runs inside your app
• Tight Kafka integration with exactly-once support
• Simple deployment model (it’s just a JVM app)
• Good for event sourcing and CQRS patterns

Cons

• Tied to Kafka as both source and sink
• Java/Scala only
• State recovery from changelogs can be slow for large state
• No SQL interface

Best for

Teams building Kafka-native microservices in Java or Scala. If your architecture already runs on Kafka and you want to process events without adding another system, Kafka Streams is the lowest-friction option.

3. Apache Spark Structured Streaming

Spark Structured Streaming extends the Spark SQL engine to handle streaming data. It uses a micro-batch execution model by default, processing small batches of data at regular intervals.

Architecture

Runs on the Spark runtime (driver + executors). A streaming query is conceptually an unbounded DataFrame that gets appended to as new data arrives. Under the hood, the engine divides the stream into micro-batches, processes each batch using the standard Spark SQL optimizer, and checkpoints progress to reliable storage.

Latency and throughput

Default micro-batch mode gives latency in the hundreds of milliseconds to seconds range—good enough for dashboards and analytics, but not for sub-second alerting. Spark 3.x introduced a continuous processing mode that targets ~1ms latency, but it’s still experimental and doesn’t support all operators. Throughput is strong, especially for complex analytical queries, thanks to Spark’s optimizer and code generation.

State management

State lives in memory on executors and checkpoints to HDFS, S3, or other distributed storage. Supports arbitrary stateful operations via mapGroupsWithState and flatMapGroupsWithState. State management is less mature than Flink’s—checkpoint sizes can grow large, and recovery means replaying from the last checkpoint.

SQL support

Full Spark SQL support. You can define streaming sources and sinks in SQL, run aggregations, joins, and window functions. The unified batch/streaming API means the same SQL works on both bounded and unbounded data.

Pricing

Open source. Databricks charges per DBU (Databricks Unit). AWS EMR, Google Dataproc, and Azure HDInsight offer managed Spark with per-instance pricing. Databricks streaming workloads typically run $0.22–$0.40/DBU depending on the tier.

Pros

• Unified batch and streaming API
• Strong SQL support and query optimization
• Large ecosystem (MLlib, GraphX, Delta Lake integration)
• Familiar to anyone who knows Spark

Cons

• Micro-batch latency is too high for some use cases
• Continuous mode is experimental with limited operator support
• Checkpoint-based recovery can be slow
• Heavier resource footprint than stream-native tools

Best for

Organizations already running Spark for batch ETL or analytics that want to add streaming without introducing a new framework. Also a good fit when you need to combine streaming with ML model scoring or complex analytical queries.

4. Apache Beam

Apache Beam is a unified programming model for batch and stream processing. You write your pipeline once, then execute it on a “runner”—Google Cloud Dataflow, Flink, Spark, or others.

Architecture

Beam defines a portable pipeline abstraction (PCollections, PTransforms) that gets translated into runner-specific execution plans. The Beam SDK handles windowing, triggering, and watermark semantics. The runner handles distribution, state, and fault tolerance.

Latency and throughput

Entirely dependent on the runner. On Dataflow or Flink, you get millisecond-level latency. On Spark, you get micro-batch latency. Beam itself adds minimal overhead—it’s a translation layer, not a runtime.

State management

Beam defines a state and timer API, but the implementation quality varies by runner. Dataflow and Flink runners have strong state support. Other runners may have gaps. Cross-runner state portability is not guaranteed.

SQL support

Beam SQL exists but has limited adoption. It supports basic queries and some streaming extensions. Most production Beam pipelines are written in Java, Python, or Go using the SDK directly.

Pricing

Open source SDK. You pay for the runner. Google Cloud Dataflow charges per vCPU-hour and GB-hour. Running Beam on self-managed Flink or Spark clusters costs whatever those clusters cost.

Pros

• Write once, run on multiple engines
• Strong windowing and trigger semantics
• Google Cloud Dataflow is a battle-tested managed runner
• Python, Java, and Go SDKs

Cons

• Abstraction adds complexity—debugging goes through multiple layers
• Runner-specific behavior differences in practice
• Smaller community than Flink or Spark
• Locked into Beam’s programming model

Best for

Teams committed to multi-cloud or multi-runner portability, or organizations standardized on Google Cloud Dataflow.

5. ksqlDB

ksqlDB is a streaming database built on top of Kafka Streams. It provides a SQL interface for creating stream processing applications over Kafka topics.

Architecture

ksqlDB servers form a cluster that runs Kafka Streams topologies generated from SQL statements. Each SQL query becomes a Kafka Streams application under the hood. Push queries provide continuous results, while pull queries provide point lookups against materialized state.

Latency and throughput

Same as Kafka Streams—millisecond-level latency for record processing. Pull queries against materialized views return in single-digit milliseconds. Throughput scales by adding ksqlDB servers, though it’s generally lower than raw Kafka Streams because of the SQL compilation layer.

State management

Backed by Kafka Streams’ RocksDB + changelog pattern. ksqlDB materializes query results into tables that you can query with pull queries. State management is automatic—you define the query, ksqlDB manages the state.

SQL support

Purpose-built SQL dialect for Kafka. Supports CREATE STREAM, CREATE TABLE, SELECT ... EMIT CHANGES, window functions, joins (stream-stream, stream-table, table-table), and user-defined functions. Not ANSI SQL—it has Kafka-specific extensions and limitations.

Pricing

Open source (Confluent Community License). Confluent Cloud ksqlDB pricing is consumption-based, charged per CSU (Confluent Streaming Unit) at roughly $0.12/hour. Self-managed is free but requires a Kafka cluster.

Pros

• SQL interface lowers the barrier to Kafka stream processing
• Push and pull query models
• Tight integration with Kafka and Schema Registry
• Managed option on Confluent Cloud

Cons

• Only works with Kafka topics as source and sink
• SQL dialect is non-standard and has limitations
• Confluent Community License restricts some use cases
• Not suitable for complex stateful logic beyond SQL

Best for

Teams that want SQL-based stream processing directly on Kafka topics without writing Java. Good for building materialized views, filtering and routing events, and simple enrichments.

6. Materialize

Materialize is a streaming database that maintains SQL views incrementally. When source data changes, Materialize updates view results without recomputing from scratch.

Architecture

Built on Timely Dataflow and Differential Dataflow (Rust-based research systems from Frank McSherry). Sources connect to Kafka, PostgreSQL CDC, or webhook inputs. The engine maintains an internal representation of each view and updates it incrementally as new data arrives. Results are queryable via a PostgreSQL-compatible wire protocol.

Latency and throughput

Sub-second view maintenance for most workloads. Materialize targets single-digit millisecond updates for simple views and low hundreds of milliseconds for complex multi-way joins. Throughput is competitive for analytical queries but not designed for ultra-high-volume event processing like Flink.

State management

All state lives in the Differential Dataflow engine, backed by durable storage. The incremental computation model means state is always consistent with the latest inputs. You don’t manage checkpoints or configure state backends—it’s handled by the engine.

SQL support

PostgreSQL-compatible SQL. You can use psql, standard SQL drivers, and ORMs to connect. Supports views, joins, aggregations, window functions, and CTEs. The PostgreSQL compatibility makes adoption straightforward for teams familiar with relational databases.

Pricing

Managed SaaS only (the open-source version was deprecated). Pricing starts at roughly $0.35/hour for the smallest configuration, scaling based on compute and storage. Free trial available.

Pros

• Incremental view maintenance is a different (and powerful) paradigm
• PostgreSQL-compatible SQL and wire protocol
• No JVM, no Kafka required for basic use
• Elegant model for maintaining real-time dashboards and caches

Cons

• No self-managed option since open-source deprecation
• Memory-intensive for large state
• Fewer integrations than Flink or Spark
• Relatively early in production adoption compared to Flink

Best for

Teams that need to maintain real-time materialized views with standard SQL. Good for powering dashboards, application caches, and operational analytics where the primary pattern is “keep this query result up to date.”

7. RisingWave

RisingWave is an open-source streaming database, similar in concept to Materialize but with a cloud-native architecture and PostgreSQL wire compatibility.

Architecture

Disaggregated storage and compute. Compute nodes run the streaming engine (Rust-based), meta nodes manage cluster state, and compactor nodes handle storage compaction. State is stored in S3 or compatible object storage rather than local disk, which simplifies scaling and recovery. Sources include Kafka, Pulsar, Kinesis, PostgreSQL CDC, MySQL CDC, and more.

Latency and throughput

Sub-second for most streaming queries. RisingWave targets similar latency profiles to Materialize—single-digit to low hundreds of milliseconds depending on query complexity. The S3-backed storage adds some latency overhead compared to memory-only systems but makes large state workloads more cost-effective.

State management

S3-backed shared storage eliminates the need for local state management. Scaling up or down doesn’t require state migration—new nodes read from S3. This is a significant operational advantage over tools that store state on local disk.

SQL support

PostgreSQL-compatible SQL, including materialized views, joins, window functions, and UDFs. Connects via any PostgreSQL driver. The SQL experience is closer to a regular database than a stream processing framework.

Pricing

Open source (Apache 2.0 license). A managed cloud service (RisingWave Cloud) offers tiered pricing starting at approximately $0.14/hour for small configurations. Self-managed is free.

Pros

• Open source with Apache 2.0 license
• S3-backed storage keeps costs down for large state
• PostgreSQL compatibility for easy adoption
• Cloud-native architecture scales well

Cons

• Newer project with smaller community than Flink or Spark
• Not suited for arbitrary event processing logic (SQL only)
• Fewer battle-tested production deployments
• Limited ecosystem of connectors compared to Flink

Best for

Teams that want a SQL-first streaming engine with PostgreSQL compatibility and prefer cloud-native, S3-backed storage. A good alternative to Materialize with the added benefit of being open source.

8. Streamkap

Streamkap is a managed platform for real-time data pipelines that includes stream processing as a built-in feature. Unlike standalone stream processing tools, Streamkap bundles CDC connectors, transformations, and delivery into a single product.

Architecture

Built on Kafka and Apache Flink internally, but you never interact with either directly. You configure source connectors (PostgreSQL, MySQL, MongoDB, Oracle, SQL Server, DynamoDB), write optional transformations in SQL, Python, or TypeScript, and select destination connectors (Snowflake, BigQuery, Databricks, ClickHouse, Elasticsearch, and 50+ others). The platform handles provisioning, scaling, checkpointing, and schema evolution.

Latency and throughput

Sub-250ms end-to-end from source database change to destination delivery. This includes CDC capture, transformation, and sink write. Throughput scales automatically—you don’t configure parallelism or cluster sizes.

State management

Fully managed. Streamkap handles Flink checkpointing, state recovery, and scaling internally. You don’t choose state backends or tune checkpoint intervals. If a pipeline fails, it recovers from the last consistent checkpoint automatically.

SQL support

SQL transformations are first-class. You write standard SQL to filter, transform, aggregate, or join streams. Python and TypeScript are available for logic that doesn’t fit SQL. There’s no need to compile, deploy, or version-manage transformation code separately—it’s part of the pipeline configuration.

Pricing

Consumption-based pricing starting at $0.15/GB of data processed. No infrastructure fees, no per-connector charges, no minimum commitments. Free trial with no credit card required.

Pros

• Zero ops—no clusters, no JVM tuning, no checkpoint configuration
• CDC and stream processing in one product
• SQL, Python, and TypeScript transforms
• Sub-250ms end-to-end latency with automatic schema evolution

Cons

• Not a general-purpose stream processing framework
• Less flexible than running your own Flink cluster
• Focused on CDC-to-destination pipelines rather than arbitrary event processing
• Newer platform with a smaller community than Flink or Spark

Best for

Teams whose primary goal is getting database changes into warehouses, lakehouses, or operational stores in real time—with optional transformations along the way. If you don’t want to run Flink clusters but need stream processing capabilities, this is the fastest path to production.

How to choose

Here’s a decision framework based on what we’ve seen work in practice:

You need stateful event processing at scale. Use Flink. Nothing else matches its combination of state management, exactly-once guarantees, and throughput for large-scale stateful workloads.

You’re building Kafka-native microservices in Java. Use Kafka Streams. It embeds in your app, needs no separate cluster, and integrates tightly with Kafka.

You already run Spark for batch and want to add streaming. Use Spark Structured Streaming. Same API, same cluster, same team skills.

You want SQL-first streaming without managing infrastructure. Consider RisingWave (open source, self-managed option) or Materialize (managed, PostgreSQL-compatible). Both let you define streaming queries in standard SQL.

You need SQL over Kafka topics specifically. Use ksqlDB. It’s purpose-built for that use case.

You’re doing CDC and need the data in a warehouse or lakehouse. Use Streamkap. It handles the full pipeline—CDC, transforms, and delivery—without requiring a separate stream processing cluster.

You need multi-cloud portability. Use Apache Beam. It’s the only option that abstracts the runner, letting you move between Dataflow, Flink, and Spark.

The operational cost matters more than you think

Feature lists don’t capture the full picture. Running Flink in production means managing cluster sizing, checkpoint tuning, state backend configuration, job upgrades with state compatibility, and on-call for a distributed system. Kafka Streams is simpler but still requires understanding of partition assignment, state store behavior, and changelog topic management.

For teams where stream processing is a means to an end—getting data from A to B with some transformation—a managed platform eliminates weeks of operational setup and ongoing maintenance. The right tool depends not just on features, but on how much operational overhead your team can absorb.

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Want stream processing without managing Flink clusters? Streamkap offers managed stream processing with built-in CDC — write SQL transforms and let the platform handle scaling. Start a free trial or explore stream processing.