Choosing a stream-processing technology depends on more than whether your application processes data in real time. You also need to consider latency, state management, data sources, deployment model, operational complexity, and how closely the solution is tied to Apache Kafka.
This article compares Kafka Streams with Apache Spark Structured Streaming. The older Spark Streaming DStream API is also discussed briefly for context, but new Spark streaming applications should generally use Structured Streaming.
What is data streaming?
Data streaming is a processing pattern in which records arrive continuously and are processed as they become available. Instead of waiting for a large batch of data, a streaming application reads events, applies transformations, and writes results to a destination such as a database, data lake, dashboard, or another messaging topic.
What is Kafka Streams?
Kafka Streams is a Java library for building stream-processing applications on top of Apache Kafka. It is embedded directly into an application rather than deployed as a separate processing cluster.
Kafka Streams reads records from Kafka topics, applies transformations such as filtering, mapping, joins, and aggregations, and can write the results back to Kafka topics or external systems. It uses Kafka partitions for parallelism and supports local state stores, windowing, event-time processing, and exactly-once processing semantics when configured appropriately.
What is Spark Structured Streaming?
Spark Structured Streaming is the streaming engine built on Apache Spark SQL. It allows developers to express streaming logic using familiar DataFrame and Dataset APIs, similar to batch processing.
Structured Streaming can read from sources such as Kafka, files, cloud storage, and other supported connectors. It supports transformations, aggregations, joins, event-time windows, watermarks, checkpointing, and writing results to files, tables, databases, or custom sinks.
Spark Streaming vs Spark Structured Streaming
Apache Spark originally provided streaming through the DStream-based Spark Streaming API. DStreams represent a continuous stream as a sequence of RDDs and process incoming data in batches.
Spark Structured Streaming is the newer API and is built on the Spark SQL engine. It provides a table-oriented programming model, incremental query execution, event-time processing, watermarks, and a common API for batch and streaming workloads. For new projects, Structured Streaming is generally the preferred Spark streaming option.
Kafka Streams vs Spark Structured Streaming: key differences
| Area | Kafka Streams | Spark Structured Streaming |
|---|---|---|
| Technology | Java library embedded in an application and built around Apache Kafka. | Distributed stream-processing engine built on Apache Spark SQL. |
| Primary ecosystem | Kafka topics, partitions, brokers, schemas, and Kafka-based applications. | Spark, DataFrames, Datasets, SQL, data lakes, cloud storage, and analytics workloads. |
| Deployment | Deploy the application instances that contain the Kafka Streams library. | Run streaming queries on a Spark deployment such as Kubernetes, YARN, or a managed Spark platform. |
| Processing model | Record-at-a-time stream processing through a processor topology. | Incremental processing of streaming DataFrames; micro-batch is the default execution mode, with other modes available for supported workloads. |
| Latency profile | Designed for low-latency event processing and millisecond-level application use cases. | Designed for scalable streaming analytics; latency depends on trigger settings, workload, cluster resources, and query complexity. |
| Data sources | Kafka topics are the native source and destination. | Kafka, files, cloud storage, and other supported streaming sources. |
| State management | Local state stores support aggregations, joins, windowing, and interactive queries. | Managed streaming state with checkpointing, stateful operators, windows, joins, and watermarks. |
| Fault tolerance | Uses Kafka offsets, replication, state-store recovery, and configurable processing guarantees. | Uses checkpointing and query-progress tracking; end-to-end guarantees also depend on the source and sink. |
| Programming APIs | Streams DSL and Processor API, primarily for Java applications. | DataFrame, Dataset, and SQL APIs in Python, Scala, Java, and other supported languages. |
| Batch integration | Focused on continuous Kafka-centric application processing. | Strong integration with Spark batch processing, SQL, machine learning, and data engineering workloads. |
| Operational model | Often simpler for small, focused Kafka-centric services, but Kafka operations remain necessary. | Provides a broader processing platform, but cluster sizing, dependencies, checkpoints, and query operations add complexity. |
| Typical fit | Real-time enrichment, event-driven services, Kafka-to-Kafka processing, and low-latency stateful applications. | Streaming ETL, lakehouse ingestion, large-scale aggregations, joins, analytics, and unified batch/streaming pipelines. |
When should you use Kafka Streams?
Kafka Streams is a natural fit when Kafka is already the central event platform and the processing logic needs to run close to the event stream.
- You need low-latency processing of Kafka events.
- Your inputs and outputs are primarily Kafka topics.
- You want to embed stream processing inside a Java microservice.
- You need local state stores, windowed aggregations, joins, or event enrichment.
- You want to scale processing using Kafka partitions without operating a separate Spark cluster.
When should you use Spark Structured Streaming?
Spark Structured Streaming is a strong fit when streaming is part of a wider data engineering or analytics platform.
- You need to process data from multiple sources such as Kafka, cloud storage, and files.
- You already use Spark for batch processing, SQL, or machine learning.
- You need large-scale joins, aggregations, windowing, or streaming ETL.
- You want one DataFrame or SQL-based approach for batch and streaming pipelines.
- You are building lakehouse ingestion or analytics pipelines on a managed Spark platform.
Can Kafka Streams and Spark Structured Streaming work together?
Yes. They are not mutually exclusive. A common architecture uses Kafka as the event backbone, Kafka Streams for lightweight real-time application processing, and Spark Structured Streaming for larger-scale transformations, enrichment, or lakehouse ingestion.
For example, an application can validate and enrich events with Kafka Streams, publish the results to another Kafka topic, and use Spark Structured Streaming to load those events into a data lake for analytics. The design should account for schemas, delivery guarantees, duplicate handling, checkpoint locations, monitoring, and replay requirements.
Important considerations before choosing
- Latency: Define the actual end-to-end latency requirement instead of assuming that every streaming workload needs sub-second processing.
- Source and sink support: Confirm that the required connectors and destination semantics are supported.
- Delivery guarantees: Exactly-once processing is not automatically guaranteed across every external system. Validate the complete source-to-sink design.
- State size: Stateful joins and aggregations can require significant local or checkpointed storage.
- Late events: Define event-time and watermark policies for out-of-order data.
- Operations: Plan for monitoring, retries, back-pressure, checkpoint recovery, scaling, and schema evolution.
- Team skills: Choose APIs and deployment models that match the team’s Java, Python, SQL, Kafka, and Spark experience.
Summary
Kafka Streams and Spark Structured Streaming solve related but different problems. Kafka Streams is a lightweight, Kafka-native library suited to low-latency event-driven applications and Kafka-centric processing. Spark Structured Streaming is a broader distributed processing engine suited to streaming ETL, analytics, large-scale transformations, and unified batch and streaming pipelines.
The right choice depends on your latency target, data sources, processing complexity, deployment environment, operational model, and existing platform. In some architectures, using both technologies together is appropriate.
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Kunal Rathi
With over 15 years of experience in data engineering and analytics, I've assisted countless clients in gaining valuable insights from their data. As a dedicated supporter of Data, Cloud and DevOps, I'm excited to connect with individuals who share my passion for this field. If my work resonates with you, we can talk and collaborate.
