Apache Kafka vs Spark: 5 Critical Differences to Simplify Understanding

In recent years, the phrase “big data” has gained popularity in a variety of industries throughout the world. Regardless of what industry you work in or the size of your firm, the growing volume and complexity of big data necessitate data collection, data analytics, and data comprehension. When you have the right big data processing tools at your disposal, transforming raw data into a form that helps companies make better decisions becomes seamless. This is why it’s so important to have effective data processing tools. There are a variety of big data technologies on the market, including Apache Kafka, Apache Spark, Flink, Apache Storm, and others.

This article will focus on comparing two prominent tools viz., Kafka vs Spark.

What is Apache Kafka?

Kafka is a distributed, partitioned, and replicated log service that is available as an open-source streaming platform. Created by LinkedIn and later acquired by the Apache Foundation, Kafka has its messaging system. It is a platform that manages real-time data streams with low latency and high throughput.

Apache Kafka, which is written in Scala, facilitates pulling in data from a wide range of sources and storing it in the form of “topics” by processing the data stream. These topic messages can be stored for long periods by apps that can reprocess them to give them useful insights.

Advantages of Apache Kafka

Compared to traditional Message Brokers, Kafka has several advantages.

• It provides for load de-bulking because no indexes are required for the message.
• It improves stream efficiency and eliminates buffering for end-users.
• Without deleting any data, all data logs are maintained with a punched time. As a result, the danger of data loss is reduced.

What is Kafka Workflow?

How Kafka Functions

Kafka is a robust publish-subscribe messaging system that handles large data volumes for both online and offline communication. Publishers send messages to a topic, while subscribers consume them. Kafka supports message broadcasting, allowing multiple subscribers to receive copies of the same message.Messages are stored on disk and replicated across brokers for fault tolerance. Kafka uses a distributed messaging model, enabling asynchronous communication between systems. Producers send messages to topics, which are stored in partitions by Kafka brokers to balance the load. If multiple consumers subscribe, Kafka shares data across them based on the number of available partitions.

Role of Zookeeper in Kafka

Zookeeper is essential for managing Kafka’s brokers. It stores metadata, tracks broker activity, detects failures, and ensures smooth integration of new brokers into the cluster. Zookeeper coordinates key operations like partition leader elections, ensuring Kafka’s distributed system runs efficiently.

In Kafka’s queue-based system, consumers with the same group ID share messages. Kafka evenly distributes data until the number of consumers matches the partitions. If consumers exceed the partitions, new ones must wait, ensuring high efficiency and fault tolerance in message handling.

What is Apache Spark?

Apache Spark is a cluster computing framework that is free and open-source. It’s a data processing solution for dealing with large data workloads and data collections. It can swiftly handle massive data volumes and divide jobs across several systems to reduce workload. With the assistance of its DAG scheduler, query optimizer, and engine, Spark is recognized for its high-performance quality for batch and streaming data processing.

Matei Zaharia, who is regarded as the inventor of Apache Spark, began developing it as an open-source research project at UC Berkeley’s AMPLab. It was later designated as an Apache Software Foundation incubated project in 2013.

Advantages of Apache Spark

Advantages of Spark include:

• Spark can leverage Hadoop’s cluster management (YARN) and underlying storage to run as a single-engine (HDFS, HBase, etc.). It can, moreover, work independently of Hadoop, collaborating with other cluster administrators and storage solutions (the likes of Cassandra and Amazon S3). 
• Spark can help with advanced analytics like machine learning and graph processing. It comes with amazing libraries, like SQL & DataFrames and MLlib (for machine learning), GraphX, and Spark Streaming, that aid enterprises solve complex data issues with ease. Spark further improves analytics performance by storing data in the RAM of the servers, which is quickly accessible.

What is Spark Workflow?

Spark Architecture: RDDs and DAGs

Spark’s architecture is built on RDDs (Resilient Distributed Datasets) and DAGs (Directed Acyclic Graphs). RDDs are partitioned collections of data that can be stored in memory across worker nodes in a Spark cluster. Spark supports two types of RDDs: Hadoop Datasets, derived from HDFS files, and parallelized collections, based on existing Scala collections.A DAG represents a series of data-processing operations where each node corresponds to a partition of RDDs and each edge represents a transformation. The DAG abstraction enhances performance by eliminating Hadoop’s multi-stage MapReduce model, enabling more efficient task execution.

Spark Execution Workflow: Driver, Workers, and Schedulers

Spark follows a master-slave architecture, where a driver (central coordinator) manages distributed worker nodes. When a Spark job is submitted, the driver program interacts with the cluster manager to request resources and starts the user-defined processing logic.

The execution logic is built in parallel, with transformations being stored as a DAG within the Spark context until an action is triggered. Once an action is called, a job is created, which is broken down into tasks by the DAG scheduler. These tasks are then assigned to the worker nodes by the task scheduler.The cluster manager handles resource allocation and monitors task execution across worker nodes, launching tasks on the executors within the nodes. When a Spark request is submitted, the program and configurations are distributed to all available nodes, enabling local execution without the need for network routing, thereby ensuring efficient parallel processing.

Apache Kafka vs Apache Spark: Quick Comparison

Feature	Apache Kafka	Apache Spark
Core Function	Message Broker (data streaming)	Data Processing Engine
Data Flow	Distributes data streams from producers to consumers	Processes and analyzes large datasets
Latency	Low latency, optimized for real-time streaming	Slightly higher latency than Kafka
Data Processing	Primarily focuses on data movement and delivery	Handles batch processing, stream processing, and complex analytics
Use Cases	Real-time event processing, log aggregation, data pipelines	Data warehousing, machine learning, ETL processes
Key Strengths	High throughput, low latency, fault tolerance	Powerful processing capabilities, handles large datasets, supports various data formats

Apache Spark vs Apache Kafka: When to Use Which

Both Apache Spark and Apache Kafka are powerful open-source platforms that play vital roles in modern data processing pipelines. While they share the common ground of distributed computing, they serve distinct purposes and excel in different scenarios.

1. Data Flow & Core Functions

• Kafka: Acts as a high-throughput message broker. It efficiently receives, stores, and distributes streams of data from various sources to multiple consumers. Think of it as a robust highway for data in motion.
• Spark: Functions as a powerful processing engine. It excels at performing complex computations on large datasets, including batch processing, stream processing, and interactive analysis.

2. Latency Considerations

• Kafka: Prioritizes low-latency data streaming. Its optimized message queuing system ensures minimal delay in data delivery, making it ideal for real-time applications.
• Spark: While capable of stream processing, it generally has slightly higher latency compared to Kafka due to the nature of its processing engine. However, it provides a comprehensive set of tools for complex data analysis and transformations.

3. Use Cases

• Kafka:
  
  • Real-time Event Processing: Handling high-velocity streams of events (e.g., sensor data, financial transactions).
  • Log Aggregation: Collecting and analyzing log data from various sources.
  • Data Pipelines: Building real-time data pipelines for fast and efficient data ingestion and delivery.
• Spark:
  
  • Data Warehousing: Loading and processing large datasets for analytical purposes.
  • Machine Learning: Training and deploying machine learning models on massive datasets.
  • Large-Scale Data Analysis: Performing complex data transformations, aggregations, and exploratory analysis.
  • ETL Processes: Extracting, transforming, and loading data from various sources.

Understanding Apache Kafka and Apache Spark Differences

Here are the key differences between Apache Kafka vs Spark:

• Apache Kafka vs Spark: ETL 
• Apache Kafka vs Spark: Latency
• Apache Kafka vs Spark: Recovery
• Apache Kafka vs Spark: Processing Type
• Apache Kafka vs Spark: Programming Languages Supported

Apache Kafka vs Spark: ETL

• Spark for ETL: Spark enables the complete ETL (Extract, Transform, Load) process by allowing users to pull data from a source, hold it in memory, process it, and then push it to a target system. It supports both batch and streaming ETL.
• Kafka and ETL: Kafka does not offer exclusive ETL services but relies on its APIs to build streaming data pipelines. Kafka is more focused on streaming data transport and less on full ETL processes.
• Kafka Connect API: Kafka enables the Extract and Load phases of ETL through its Kafka Connect API. This API helps create scalable and fault-tolerant streaming data pipelines, connecting Kafka to external systems for data ingestion and export.
• Scalability and Monitoring: The Kafka Connect API leverages Kafka’s inherent scalability and fault-tolerance features. It provides a unified approach for monitoring and managing all data connections within the system.
• Kafka Streams API: The Transform phase of ETL can be handled by the Kafka Streams API, which supports real-time stream processing and data transformations within the pipeline, offering powerful capabilities for stream-based ETL processing.

Apache Kafka vs Spark: Latency

• Spark for Source Flexibility: Spark is the better option when latency isn’t a critical concern. It offers greater source flexibility and broad compatibility with various systems, making it ideal for a wide range of data sources.
• Kafka for Low Latency: Kafka excels in scenarios where low latency and real-time processing are required. If processing times shorter than milliseconds are essential, Kafka is the best choice due to its optimized event-driven architecture.
• Kafka’s Fault Tolerance: Kafka offers superior fault tolerance thanks to its event-driven processing model, making it highly reliable for critical real-time applications.
• Compatibility Challenges with Kafka: While Kafka excels in performance, its compatibility with other systems can be more complex and might require additional configuration or integration efforts.

Apache Kafka vs Spark: Recovery

• Spark’s Fault Tolerance: Apache Spark ensures high availability and fault tolerance through RDDs (Resilient Distributed Datasets). RDDs enable recovery from worker node failures by continually saving transformations and actions, allowing all stages to be retried in case of a failure without data loss.
• Kafka’s Data Replication: Kafka ensures fault tolerance via data replication within its cluster. It duplicates and distributes data across multiple brokers or servers. If a Kafka server fails, the data remains accessible on other servers, ensuring seamless recovery and continuous operation.
• 24/7 Availability: Both Spark and Kafka are designed for real-time stream processing systems that require 24/7 availability. Each system provides mechanisms to recover from faults and maintain uninterrupted service.

Apache Kafka vs Spark: Processing Type

• Kafka’s Continuous Processing: Kafka follows an event-at-a-time processing model, analyzing events in real-time as they occur. This enables continuous stream processing, ideal for real-time data analysis.
• Spark’s Micro-Batch Processing: Spark uses a micro-batch processing model, where incoming data streams are divided into small batches. These batches are processed in short intervals, making Spark more suited for near real-time stream processing.

Apache Kafka vs Spark: Programming Languages Support

• Kafka’s Limited Programming Support: Kafka does not provide specific programming language support for data transformation, focusing primarily on data transport and streaming rather than processing.
• Spark’s Language Flexibility: In contrast, Apache Spark supports a wide range of programming languages and frameworks, including Java, Scala, Python, and R. This flexibility allows Spark to perform more than just data interpretation.
• Integration with Machine Learning: Spark can leverage existing machine learning frameworks and perform complex data processing tasks, including graph processing, making it a versatile tool for various data analytics and machine learning applications.

Conclusion

This article introduces two of Apache’s most popular big data processing tools, Apache Kafka and Apache Spark. It gives you an overview of their advantages, workflows, and fundamental distinctions to assist you in making better decisions and processing information according to varying needs before diving into Apache Kafka vs Spark.

Explore our comprehensive guide on Spark real-time streaming to master the setup and optimize real-time data workflows.

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