Apache Kafka distributed the event store platform to process data directly from Kafka, which makes integrating with other data sources difficult. Spark Streaming is a separate Spark library, that supports the implementation of both iterative algorithms, which visit their data set several times in a loop, and interactive/exploratory data analysis, that is, repetitive database-style querying of data.
What is Apache Kafka?
Apache Kafka is an open-source distributed streaming system for stream processing, real-time data pipelines, and scalable data integration. Kafka swiftly progressed from a messaging queue to a full-fledged event streaming infrastructure capable of processing over 1 million messages per second, or billions of messages per day. Kafka uses a binary TCP-based protocol designed for efficiency and depends on a "message set" concept that automatically groups messages to reduce network roundtrip time. This leads to larger network packets, larger sequential disk operations, and contiguous memory blocks, allowing Kafka to convert a bursty stream of random message writes into linear writes.
What is Apache Spark?
Apache Spark is used mainly for distributed processing systems for big data applications. It uses in-memory caching and improved query execution to perform rapid analytic queries on data of any size. Spark offers an interface for programming clusters that includes implicit data parallelism and fault tolerance. In the UC Berkeley R&D Lab, they discovered that was inefficient for iterative and interactive computing tasks.
Similarities between Apache Kafka and Spark
- Scalability: Kafka is a highly scalable data streaming engine that can expand vertically and horizontally and also you can increase Spark's processing capacity by adding nodes to a cluster.
- Data diversity: Kafka and Spark enable you to build data pipelines from enterprise applications, databases, and other streaming sources.
- Big data processing: Kafka and Spark both may adopt distributed data pipelines across numerous servers to analyze huge amounts of data in real time.
Difference between Apache Kafka and Spark
Apache Kafka | Apache Spark |
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Apache Kafka provides an open-source distributed streaming system. | Apache Spark is also an open-source distributed processing system and provides high speed. |
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It has ETL functions that require the Kafka Connect API as well as the Kafka Streams API. | It has native support for ETL. |
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Kafka's memory usage is lower than Spark's since it does not retain intermediate processing results in memory. | Spark's memory usage is generally higher than Kafka's since it retains intermediate processing results in memory. |
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Apache Kafka supports hopping, tumbling, session, and sliding modes for Windows. | Apache Spark supports only sliding for Windows. |
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Apache Kafka has an ultra-low latency and each incoming event is processed in real time. | Apache Spark provides low latency and performs read and write operations on RAM. |
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Backup data is partitioned on different servers. Request backups when an active partition fails. | Maintains persistent data across several nodes. Recalculates the result if a node fails. |
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It enables data transformation functions that require additional libraries. | It supports Java, Python, Scala, and R for data transformation and machine learning workloads. |
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Conclusion
In this article, we have learned about Apache Kafka and Spark. Apache Kafka offers ultra-low latency and processes each incoming real-time, whereas Spark stores persistent data across multiple nodes and recalculates the outcome if a node fails.
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