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Mule ESB VS Spark Streaming

Compare Mule ESB VS Spark Streaming and see what are their differences

Mule ESB logo Mule ESB

Connect with our lightweight powerful ESB. Build integrations for use cases ranging from legacy services with lightweight APIs to SOA re-platforming connectivity across the entire enterprise.

Spark Streaming logo Spark Streaming

Spark Streaming makes it easy to build scalable and fault-tolerant streaming applications.
  • Mule ESB Landing page
    Landing page //
    2023-09-18
  • Spark Streaming Landing page
    Landing page //
    2022-01-10

Mule ESB videos

MuleSoft Interview Questions and Answers |Mule ESB | MuleSoft|

More videos:

  • Review - MuleSoft | Mule ESB 4 | Session 3 | Microservices | Monolithic vs Microservices

Spark Streaming videos

Spark Streaming Vs Kafka Streams || Which is The Best for Stream Processing?

More videos:

  • Tutorial - Spark Streaming Vs Structured Streaming Comparison | Big Data Hadoop Tutorial

Category Popularity

0-100% (relative to Mule ESB and Spark Streaming)
Web Service Automation
100 100%
0% 0
Stream Processing
0 0%
100% 100
Data Integration
100 100%
0% 0
Data Management
0 0%
100% 100

User comments

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Social recommendations and mentions

Based on our record, Spark Streaming seems to be more popular. It has been mentiond 3 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Mule ESB mentions (0)

We have not tracked any mentions of Mule ESB yet. Tracking of Mule ESB recommendations started around Mar 2021.

Spark Streaming mentions (3)

  • Choosing Between a Streaming Database and a Stream Processing Framework in Python
    Other stream processing engines (such as Flink and Spark Streaming) provide SQL interfaces too, but the key difference is a streaming database has its storage. Stream processing engines require a dedicated database to store input and output data. On the other hand, streaming databases utilize cloud-native storage to maintain materialized views and states, allowing data replication and independent storage scaling. - Source: dev.to / 4 months ago
  • Machine Learning Pipelines with Spark: Introductory Guide (Part 1)
    Spark Streaming: The component for real-time data processing and analytics. - Source: dev.to / over 1 year ago
  • Spark for beginners - and you
    Is a big data framework and currently one of the most popular tools for big data analytics. It contains libraries for data analysis, machine learning, graph analysis and streaming live data. In general Spark is faster than Hadoop, as it does not write intermediate results to disk. It is not a data storage system. We can use Spark on top of HDFS or read data from other sources like Amazon S3. It is the designed... - Source: dev.to / over 2 years ago

What are some alternatives?

When comparing Mule ESB and Spark Streaming, you can also consider the following products

Apache Camel - Apache Camel is a versatile open-source integration framework based on known enterprise integration patterns.

Confluent - Confluent offers a real-time data platform built around Apache Kafka.

Apache Kafka - Apache Kafka is an open-source message broker project developed by the Apache Software Foundation written in Scala.

Amazon Kinesis - Amazon Kinesis services make it easy to work with real-time streaming data in the AWS cloud.

elastic.io - elastic.io connects your SaaS to other cloud apps in seconds.

Google Cloud Dataflow - Google Cloud Dataflow is a fully-managed cloud service and programming model for batch and streaming big data processing.