Kafka Streams Vs. Spark Streaming: Real-Time Data Showdown in 2025

Kafka Stream or Spark Streaming: which one to choose? It is one of the questions that comes up most often in big data processing. And that’s because the applications built today are inherently dependent on the never-ending stream of data from various sources. Where to store it and how to transact that store now comes under strategic decision.

Of course, it means that there are numerous approaches to accomplish the tasks in question as well. While intentionally designed to address different issues, both Kafka and Spark provide a solution to processing data in close to real-time. For Kafka, that is Kafka Streams and in Spark, it is Spark Structured Streaming. 

Let’s talk about Kafka Streams vs. Spark Streaming and help your organization choose the one that best matches your requirements and preferences. 

What Is Kafka Stream?

Apache Kafka acts as an open-source distributed event streaming platform that is used by more than 80% of the companies in the Fortune 500. LinkedIn created Kafka and later donated it to Apache where it is useful for firms that need powerful data conveyance pipelines, stream processing, data melding, and dependable backing on the dominant part of trials.

By design, Kafka was a very scalable fault-tolerant system that could handle millions of messages per second or trillions of messages per day. In particular, it is usually installed on a minimum of three nodes as a cluster. That append-only log is then split and copied for the Kafka nodes to distribute across the said system.

Kafka Streams is one of the five fundamental APIs of Apache Kafka. It stemmed from the desire to have a Kafka-native library that can turn Kafka real-time data input streams into output topics without relying on an outside stream processing cluster.

Key Components In Kafka Streams

• Producers – The producers must send the data from various generating events, applications, and other sources into the Kafka topics. For instance, concerning data streams oriented on credit card fraud identification, the producers could be the payment processing systems, the point of sales systems, and payment gateways. 
• Topics – Producers put out events into topics. Topics are ways of organizing data access, and consumers select these ways. In credit card fraud detection stream examples, information could be published into “in store” subjects, “online” subjects, or “ATM withdrawals” subjects. 

Overview of Apache Spark Streaming and Its Architecture

Spark Streaming is a real-time data processing framework built on Apache Spark that enables scalable and fault-tolerant stream processing of live data streams. It ingests real-time data, processes it in micro-batches, and provides near real-time analytics.

Key Features of Spark Streaming

Micro-batch Processing – Unlike true event-by-event streaming, Spark Streaming processes data in small intervals (micro-batches).

Scalability – Can handle large-scale data streams using distributed computing.

Fault Tolerance – Uses Spark’s checkpointing and recovery mechanisms to ensure reliability.

Multiple Data Source Support – Works with Kafka, Flume, HDFS, S3, TCP sockets, and more.

Integration with Spark Ecosystem – Supports MLlib (Machine Learning), SQL, and GraphX for advanced analytics.

Spark Streaming Architecture

Spark Streaming follows a DStream (Discretized Stream) model, where real-time data is broken into small micro-batches and processed using Spark’s computation engine.

Key Components:

Data Sources (Ingestion Layer)

Real-time data is ingested from sources like Kafka, Flume, HDFS, S3, or TCP sockets.

Receiver Layer

The Receiver takes incoming data and stores it as small micro-batches in Spark’s memory.

Discretized Streams (DStreams)

Spark Streaming represents continuous data streams as DStreams, which are internal sequences of Resilient Distributed Datasets (RDDs). These RDDs are processed using Spark’s transformations and actions.

Processing Engine

Micro-batches are processed using Spark’s DAG Scheduler and Executors, which perform transformations like map, filter, reduce, join, etc. Supports both stateful and stateless processing.

Output Sink

Once processed, the data is delivered to multiple endpoints, including HDFS, databases, dashboards, or Kafka, for storage, analysis, or further processing.

Similarities Between Apache Kafka Streams vs. Spark Streaming

When discussing the processing of real-time data, Kafka Streams and Spark Streaming are the two major frameworks. Let’s take a closer look at both.

Real-Time Stream Processing

Both Kafka Streams and Spark Streaming have been designed for working in real-time data processing tools.

Both are created for processing streaming data which makes both useful for a broad range of applications including event tracking, real-time analysis, and monitoring among others. Used in the monitoring of user activities or the analysis of social media streams, these frameworks offer the means to make sense of data that will be received and processed.

Fault Tolerance

It is important for any real-time processing system, and both Kafka Streams and Spark Streaming do it perfectly. Kafka Streams uses partitioning to make data resilient to faults where a copy of data is stored in multiple brokers to avoid a broker from failure. Similarly, Spark Streaming, which has a completely different architecture, has a very good integration of fault tolerance.

Kafka Streams vs. Spark Streaming: Feature Comparison

Now, we’ll be talking about comparing Kafka Streams and Spark Streaming in detail for key points.

Big Data Processing

Kafka Streams is used for real-time data consumption and it comes with a low-latency mechanism, which is good when it comes to serving microservices and event-driving approaches. Streaming, more popular by its alias, spark streaming, is capable of handling event-driven and batch processing of very large-scale distributed data. Use Kafka when you want speed while Spark is used when you need to process huge amounts of data.

Data Diversity

Kafka Streams is just a lib and a framework, it can work only on Kafka topics which restricts its input variety. Spark streaming supports several sources like HDFS, S3, databases, and so on providing flexibility for complex ecosystems. Choose Kafka Streams if it meets your needs or use Apache Spark Streaming if you need to process data of different formats.

Scalability

The horizontal scalability in Kafka Streams is one area that can be scaled easily because it is based on Kafka partitioning. Spark Streaming uses Spark clusters which are good for scalability for large-scale works but consume more resources. If you need a lightweight, dynamic system, Kafka Streams is a clear winner whereas when we are dealing with complex workloads, Spark Streaming is a better choice.

Workflow

Currently, Kafka Streams is integrated into application code by using a lightweight API located in the Kafka area. To run Spark Streaming a Spark cluster needs to be set up and resources scheduled, even though it is part of the large Spark framework. Kafka Streams is better suited to fit simple applications; Spark Streaming is better integrated with complex, multi-stage processing data workflows.

ETL

Kafka Streams allows for delta processing, as well as very easy, and quick, transformations for basic ETL procedures. Spark Streaming has richer APIs and is more suitable for complex ETL which contain aggregations, joins, and enrich data. If you require marginal ETL processing, Kafka Streams is the best option, while complex ETL processing requires Spark Streaming.

Latency

Kafka Streams has less latency because it is built natively into the Apache Kafka environment and the overall end-to-end latency is in milliseconds. On the other hand, Spark Streaming is characterized by higher latency due to switching to the micro-batch processing model which may lead to delays in real-time data pipelines.

Programming Languages

Other than Java, Kafka Streams supports Scala, making it easier to integrate with Kafka’s related applications. Whereas Spark Streaming works best with Java, Scala, Python, and R, which enable developers to choose the language of their preference for stream processing.

Availability

Kafka Streams ensures high availability and tolerance since Kafka is also a fault-tolerant system. As for the issues related to node failure, it redistributes data across brokers, which solves the problem. 

Compared to this, Spark Streaming involves a more complex implementation strategy to make it highly available and mostly depends on other frameworks such as Hadoop Distributed File System (HDFS) or Yet Another Resource Negotiator (YARN) for providing fault tolerance.

 Multiple Data Sources

Kafka Streams is primarily built to complement Kafka hence it fits well in applications that are built around Kafka. Hence, Spark streaming supports multiple sources like Kafka, HDFS, Flume and so on which provides more flexibility when it comes to integration of various data systems with Streaming.

Processing Model

Kafka Streams uses a straightforward event-driven model to process streams in real time. Hence, Spark Streaming works based on the concept of micro-batch processing where data is processed in time-bounded intervals and provides a better trade-off between real-time operation and batch processing capabilities.

Data Storage

When we talk about Kafka Streams vs. Spark Streaming performance in data storage, Kafka Streams is seamlessly built on Kafka for reading and writing data. It reads data in real time from Kafka’s topics while providing low latency for the entire process. However, Spark Streaming has a variety of storage options such as HDFS, Cassandra, or S3 where the processed data can be stored.

Language Support

Kafka Streams is Java and Scala based and this restricts language flexibility to JVM-born languages only. Spark Streaming integrates with various programming languages, which enables organizations to have developers with different programming language backgrounds like Java, Scala, Python, and R.

Analysis And ML Libraries Support

Spark Streaming is integrated with Spark’s rich libraries for machine learning and graph processing. Kafka Streams does support some third-party libraries but there are no libraries available in this streaming platform to implement machine learning algorithms. But it can be interfaced with other systems where required for any ML requirements.

Integration With Other Technologies

Kafka Streams runs well integrated with Kafka and works best on streaming data in Kafka-oriented environments. On the other hand, Kafka Streams integration use cases cover working with all types of technologies such as HDFS, Cassandra, Hive, and so on, which in turn allows the technology to be easily deployed in all types of environments.

SQL Support

Kafka Streams possess limited SQL support at this moment but rather a stream processing library with its DSL (Domain-Specific Language). The integration of Spark Streaming with Spark SQL supports advanced SQL queries for processing the incoming streaming data and can be easier for those developers who are comfortable with SQL.

 Window Support

The talk on Kafka Streams vs. Spark Streaming is incomplete without talking about Windows support. Each of Kafka Streams and Spark Streaming supports one or more window definitions necessary to operate on time-based windows.

However, Kafka Streams allow setting a more precise window size as it is considered with the fixed micro-batch intervals used by Spark Streaming, which allow less precise control over the windowing logic.

Developer Experience And Learning Curve 

Kafka Streams is easier to set up, and developers who already understand Kafka shouldn’t have a difficult time learning how Kafka Streams works. It is more complex to learn than the simple Spark Direct API, primarily because Spark Streaming is more versatile and has more configuration options which may be necessary when connecting it to other systems, such as Hadoop and Hive.

Memory Management 

Memory management is always efficient in Kafka Streams because it is built on Kafka’s native processing rather than a separate engine requiring additional resources. In general, the memory management used in Spark Streaming is slightly more complicated compared to other systems and it may consume more memory depending on the quantity of data processed and the distributed clusters in use.

Recovery  

Kafka Streams is built with Kafka’s fault tolerance in mind, which allows for the application’s failures to be recovered through message reprocessing when a node fails. Spark Streaming it is fault-tolerant but needs checkpointing and a relatively more complicated recovery setup in terms of distributed failures.

Pros And Cons Of Kafka Stream & Spark Streaming 

Kafka Streams Pros:

• Lightweight & Easy to Deploy – It runs as a simple Java application without requiring a dedicated cluster.
• Low Latency – Processes data in real-time with millisecond-level latencies.
• Tight Integration with Kafka – Works natively with Kafka, making it highly efficient for Kafka-based applications.
• Fault Tolerance & Scalability – Uses Kafka’s replication for fault tolerance and can scale horizontally.
• Exactly-Once Processing – Supports exactly-once semantics, ensuring reliable data processing.

Kafka Streams Cons:

• Limited Beyond Kafka – Best suited for Kafka-based workflows and lacks built-in support for other data sources.
• No Batch Processing – Designed for streaming only, not ideal for batch-based workloads.
• Limited Ecosystem – Fewer third-party integrations and fewer built-in machine learning capabilities compared to Spark.

Spark Streaming Pros:

• Powerful Distributed Processing – It can handle large-scale data processing across multiple nodes.
• Flexible Data Source Integration – Supports Kafka, HDFS, S3, JDBC, and many other sources.
• Strong Fault Tolerance – Uses Spark’s checkpointing and recovery mechanisms for reliability.
• Batch & Streaming in One Framework – It efficiently handles real-time and batch processing.
• Machine Learning & Analytics Support – Integrates with MLlib and GraphX for advanced analytics.

Spark Streaming Cons:

• Higher Latency – It uses a micro-batch approach, leading to slightly higher latencies compared to Kafka Streams.
• Requires Cluster Setup – Needs Spark infrastructure, making deploying and managing complex.
• Higher Resource Consumption – It is more computationally expensive compared to Kafka Streams for simple stream processing.

Kafka Streams vs. Spark Streaming Use Cases 

• Kafka Streams are best for lightweight, real-time processing with a strong Kafka dependency.
• Choose Spark Streaming for large-scale, distributed streaming analytics with complex transformations.

Wrap Up 

Both Kafka Streams and Spark Streaming offer powerful capabilities for real-time data processing, but the best choice depends on your specific needs. Kafka Streams is your go-to if you seek lightweight, low-latency, and event-driven microservices. However, Spark Streaming is better if you require high-throughput, distributed processing with advanced analytics.

If you still have queries and seek more information on Apache Kafka implementation services, have a consultation call with our experts at Ksolves. We’re a leading Apache Spark development company serving across countries with our professional team of 500+ experts.