Apache Spark vs Mule

Overview

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

Mule runtime engine

Stacks127

Followers129

Votes8

Apache Spark vs Mule: What are the differences?

Data Processing: Apache Spark is a distributed processing framework that allows for parallel processing of large datasets, while Mule is an integration platform that focuses on connecting different systems and integrating data from various sources.
Technology Stack: Apache Spark is built using Scala, a programming language that runs on the Java Virtual Machine (JVM), and it provides high-level APIs for programming in Scala, Java, Python, and R. On the other hand, Mule is built using Java and provides a Java-based API for integration purposes.
Parallelism: One of the key differences between Apache Spark and Mule is their approach to parallelism. Apache Spark utilizes a distributed computing model called Resilient Distributed Datasets (RDDs) to achieve parallel processing, while Mule leverages parallel flows and message processing to handle multiple tasks simultaneously.
Data Transformation: Apache Spark provides a wide range of libraries and functions for data transformation and manipulation, such as Spark SQL, DataFrame API, and Spark Streaming. Mule, on the other hand, focuses more on data transformation using its Anypoint Data Mapper and DataWeave languages.
Real-time Processing: While both Apache Spark and Mule can handle real-time data processing, Apache Spark is specifically designed for large-scale real-time analytics and streaming processing with its Spark Streaming and Structured Streaming capabilities. Mule, on the other hand, focuses more on real-time integration and event-driven architectures.
Scalability: Apache Spark can easily scale horizontally by adding more nodes to the cluster, allowing it to handle large volumes of data and support high-concurrency workloads. Mule also offers scalability but focuses more on vertical scalability, allowing organizations to scale up their integration infrastructure by deploying more powerful hardware resources.

In Summary, Apache Spark is a distributed processing framework with a focus on data processing and analytics, leveraging parallelism and real-time capabilities, while Mule is an integration platform that emphasizes connecting systems, data transformation, and real-time integration.

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Advice on Apache Spark, Mule runtime engine

Nilesh

Technical Architect at Self Employed

Jul 8, 2020

Needs adviceon

Elasticsearch

Kafka

We have a Kafka topic having events of type A and type B. We need to perform an inner join on both type of events using some common field (primary-key). The joined events to be inserted in Elasticsearch.

In usual cases, type A and type B events (with same key) observed to be close upto 15 minutes. But in some cases they may be far from each other, lets say 6 hours. Sometimes event of either of the types never come.

In all cases, we should be able to find joined events instantly after they are joined and not-joined events within 15 minutes.

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Detailed Comparison

Apache Spark	Mule runtime engine
Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.	Its mission is to connect the world’s applications, data and devices. It makes connecting anything easy with Anypoint Platform™, the only complete integration platform for SaaS, SOA and APIs. Thousands of organizations in 60 countries, from emerging brands to Global 500 enterprises, use it to innovate faster and gain competitive advantage.
Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk;Write applications quickly in Java, Scala or Python;Combine SQL, streaming, and complex analytics;Spark runs on Hadoop, Mesos, standalone, or in the cloud. It can access diverse data sources including HDFS, Cassandra, HBase, S3	Connects data;Connects applications;Integration platform;Fast
Statistics
GitHub Stars 42.2K	GitHub Stars -
GitHub Forks 28.9K	GitHub Forks -
Stacks 3.1K	Stacks 127
Followers 3.5K	Followers 129
Votes 140	Votes 8
Pros & Cons
Pros 61 Open-source 48 Fast and Flexible 8 One platform for every big data problem 8 Great for distributed SQL like applications 6 Easy to install and to use Cons 4 Speed	Pros 4 Open Source 2 Microservices 2 Integration
Integrations
No integrations available	CloudApp API Umbrella Zapier

What are some alternatives to Apache Spark, Mule runtime engine?

Presto

Distributed SQL Query Engine for Big Data

Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

Apache Flink

Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Druid

Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.

Apache Kylin

Apache Kylin™ is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets, originally contributed from eBay Inc.

Splunk

It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.

Apache Impala

Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.

Vertica

It provides a best-in-class, unified analytics platform that will forever be independent from underlying infrastructure.

Azure Synapse

It is an analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources—at scale. It brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.

Related Comparisons

Apache Spark vs Mule: What are the differences?

Data Processing: Apache Spark is a distributed processing framework that allows for parallel processing of large datasets, while Mule is an integration platform that focuses on connecting different systems and integrating data from various sources.
Technology Stack: Apache Spark is built using Scala, a programming language that runs on the Java Virtual Machine (JVM), and it provides high-level APIs for programming in Scala, Java, Python, and R. On the other hand, Mule is built using Java and provides a Java-based API for integration purposes.
Parallelism: One of the key differences between Apache Spark and Mule is their approach to parallelism. Apache Spark utilizes a distributed computing model called Resilient Distributed Datasets (RDDs) to achieve parallel processing, while Mule leverages parallel flows and message processing to handle multiple tasks simultaneously.
Data Transformation: Apache Spark provides a wide range of libraries and functions for data transformation and manipulation, such as Spark SQL, DataFrame API, and Spark Streaming. Mule, on the other hand, focuses more on data transformation using its Anypoint Data Mapper and DataWeave languages.
Real-time Processing: While both Apache Spark and Mule can handle real-time data processing, Apache Spark is specifically designed for large-scale real-time analytics and streaming processing with its Spark Streaming and Structured Streaming capabilities. Mule, on the other hand, focuses more on real-time integration and event-driven architectures.
Scalability: Apache Spark can easily scale horizontally by adding more nodes to the cluster, allowing it to handle large volumes of data and support high-concurrency workloads. Mule also offers scalability but focuses more on vertical scalability, allowing organizations to scale up their integration infrastructure by deploying more powerful hardware resources.

Apache Spark vs Mule

Overview