Software Alternatives, Accelerators & Startups
Register | Login

Apache Spark VS IBM MQ

Compare Apache Spark VS IBM MQ and see what are their differences

Cyclr
Powerful SaaS integration toolkit for SaaS developers - create, amplify, manage and publish native integrations from within your app with Cyclr's flexible Embedded iPaaS. featured
Note: These products don't have any matching categories. If you think this is a mistake, please edit the details of one of the products and suggest appropriate categories.
Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

Apache Spark logo Apache Spark

Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.

IBM MQ logo IBM MQ

IBM MQ is messaging middleware that simplifies and accelerates the integration of diverse applications and data across multiple platforms.
  • Apache Spark Landing page
    Landing page //
    2021-12-31
  • IBM MQ Landing page
    Landing page //
    2023-07-03

Apache Spark features and specs

  • Speed
    Apache Spark processes data in-memory, significantly increasing the processing speed of data tasks compared to traditional disk-based engines.
  • Ease of Use
    Spark offers high-level APIs in Java, Scala, Python, and R, making it accessible to a broad range of developers and data scientists.
  • Advanced Analytics
    Spark supports advanced analytics, including machine learning, graph processing, and real-time streaming, which can be executed in the same application.
  • Scalability
    Spark can handle both small- and large-scale data processing tasks, scaling seamlessly from a single machine to thousands of servers.
  • Support for Various Data Sources
    Spark can integrate with a wide variety of data sources, including HDFS, Apache HBase, Apache Hive, Cassandra, and many others.
  • Active Community
    Spark has a vibrant and active community, providing a wealth of extensions, tools, and support options.

Possible disadvantages of Apache Spark

  • Memory Consumption
    Spark's in-memory processing can be resource-intensive, requiring substantial amounts of RAM, which can drive up costs for large-scale deployments.
  • Complexity in Configuration
    To optimize performance, Spark requires careful configuration and tuning, which can be complex and time-consuming.
  • Learning Curve
    Despite its ease of use, mastering the full range of Spark's features and best practices can take considerable time and effort.
  • Latency for Small Data
    For smaller datasets or low-latency requirements, Spark might not be the most efficient choice, as other technologies could offer better performance.
  • Integration Overhead
    Though Spark integrates with many systems, incorporating it into an existing data infrastructure can introduce additional overhead and complexity.
  • Community Support Variability
    While the community is active, the support and quality of third-party libraries and tools can be inconsistent, leading to potential challenges in implementation.

IBM MQ features and specs

  • Reliability
    IBM MQ is renowned for its high reliability, ensuring that your messages are delivered once and only once. This is critical for applications where message loss can result in significant operational issues.
  • Security
    It provides robust security features, including authentication, encryption, and authorization, which are essential for protecting sensitive data in transit.
  • Scalability
    IBM MQ can scale horizontally and vertically to meet the demands of growing applications and varying workloads, making it suitable for both small-scale and enterprise-level deployments.
  • Integrations
    It supports a wide range of platforms and programming languages, which makes it easier to integrate with existing systems and applications.
  • Transaction Support
    It offers comprehensive support for transactions, ensuring that multiple related messages are processed in a single unit of work, which can be rolled back if needed.
  • High Availability
    Features like queue manager clustering and multi-instance queue managers provide high availability and disaster recovery capabilities.

Possible disadvantages of IBM MQ

  • Cost
    IBM MQ is a premium product, which means it comes with a significant cost, especially for large-scale enterprise deployments.
  • Complexity
    Setting up and maintaining IBM MQ can be complex, requiring specialized knowledge and skills, which can be a barrier for smaller teams or organizations.
  • Resource Intensive
    It can be resource-intensive, requiring substantial computational resources for its full operation, which may not be ideal for lightweight or resource-constrained environments.
  • Dependency
    Using IBM MQ can create a dependency on IBM’s ecosystem, which might limit flexibility and increase the cost and complexity of switching to a different messaging solution in the future.
  • Learning Curve
    There is a steep learning curve associated with IBM MQ, particularly for new users who are not familiar with message queuing or IBM's specific implementation.
  • Licensing
    The licensing model can be complex and sometimes difficult to navigate, potentially leading to unexpected costs if not carefully managed.

Apache Spark videos

+ Add

Weekly Apache Spark live Code Review -- look at StringIndexer multi-col (Scala) & Python testing

More videos:

  • Review - What's New in Apache Spark 3.0.0
  • Review - Apache Spark for Data Engineering and Analysis - Overview

IBM MQ videos

+ Add

IBM Watson Virtual Agent _ (Part 01)

More videos:

  • Review - IBM MQ Clustering - Tom Dunlap
  • Review - IBM Db2 Analytics Accelerator for z/OS
  • Review - IBM Blockchain Platform - 2019 Review - All You Need to Know
  • Review - IBM Db2 Analytics Accelerator – IDAA Afternoon Show 2019 08 28
  • Review - IBM Blockchain Platform Community Call – Next Generation Platform Tour + Q&A
  • Review - IBM MQ V9 Open Source Monitoring
  • Review - The next generation of the IBM Blockchain Platform

Category Popularity

0-100% (relative to Apache Spark and IBM MQ)

Apache Spark

IBM MQ

Databases
100 100%
Databases
0% 0
Data Integration
0 0%
Data Integration
100% 100
Big Data
100 100%
Big Data
0% 0
Cloud Computing
0 0%
Cloud Computing
100% 100

User comments

Share your experience with using Apache Spark and IBM MQ. For example, how are they different and which one is better?
Log in or Post with

Reviews

These are some of the external sources and on-site user reviews we've used to compare Apache Spark and IBM MQ

Apache Spark Reviews

15 data science tools to consider using in 2021
Apache Spark is an open source data processing and analytics engine that can handle large amounts of data -- upward of several petabytes, according to proponents. Spark's ability to rapidly process data has fueled significant growth in the use of the platform since it was created in 2009, helping to make the Spark project one of the largest open source communities among big...
Source: searchbusinessanalytics.techtarget.com
Top 15 Kafka Alternatives Popular In 2021
Apache Spark is a well-known, general-purpose, open-source analytics engine for large-scale, core data processing. It is known for its high-performance quality for data processing – batch and streaming with the help of its DAG scheduler, query optimizer, and engine. Data streams are processed in real-time and hence it is quite fast and efficient. Its machine learning...
Source: www.spec-india.com
5 Best-Performing Tools that Build Real-Time Data Pipeline
Apache Spark is an open-source and flexible in-memory framework which serves as an alternative to map-reduce for handling batch, real-time analytics and data processing workloads. It provides native bindings for the Java, Scala, Python, and R programming languages, and supports SQL, streaming data, machine learning and graph processing. From its beginning in the AMPLab at...
Source: www.analyticsinsight.net

IBM MQ Reviews

6 Best Kafka Alternatives: 2022’s Must-know List
IBM MQ is one of the best Kafka Alternatives which has an easy-to-use Interface and High Reliability and Data Security. It also facilitates the interoperability between various applications, either within or outside the organization. IBM MQ allows developers to focus on critical issues and manage any changes to transaction volumes asynchronously due to its simple structure.
Source: hevodata.com
Top 15 Alternatives to RabbitMQ In 2021
IBM MQ is an official message middleware which shortens the integration of varied applications and data spread throughout numerous platforms. It employs a message queue to share the info and offers a distinct messaging service for cloud systems, IoT gadgets, and mobile environments. By linking every element virtually from modest device to most complicated industrial...
Source: gokicker.com
Top 15 Kafka Alternatives Popular In 2021
IBM MQ is an easily usable interface with a great deal of reliability and security. Support is readily available in case needed anytime. It looks at handling the interoperability between various applications, be it within the organization or outside. It has asynchronous competencies and offers message integrity and relentless delivery. Because of its simplistic nature, it...
Source: www.spec-india.com

Social recommendations and mentions

Based on our record, Apache Spark seems to be more popular. It has been mentiond 70 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.

Apache Spark mentions (70)

  • Every Database Will Support Iceberg — Here's Why
    Apache Iceberg defines a table format that separates how data is stored from how data is queried. Any engine that implements the Iceberg integration — Spark, Flink, Trino, DuckDB, Snowflake, RisingWave — can read and/or write Iceberg data directly. - Source: dev.to / about 1 month ago
  • How to Reduce Big Data Analytics Costs by 90% with Karpenter and Spark
    Apache Spark powers large-scale data analytics and machine learning, but as workloads grow exponentially, traditional static resource allocation leads to 30–50% resource waste due to idle Executors and suboptimal instance selection. - Source: dev.to / about 1 month ago
  • Unveiling the Apache License 2.0: A Deep Dive into Open Source Freedom
    One of the key attributes of Apache License 2.0 is its flexible nature. Permitting use in both proprietary and open source environments, it has become the go-to choice for innovative projects ranging from the Apache HTTP Server to large-scale initiatives like Apache Spark and Hadoop. This flexibility is not solely legal; it is also philosophical. The license is designed to encourage transparency and maintain a... - Source: dev.to / 2 months ago
  • The Application of Java Programming In Data Analysis and Artificial Intelligence
    [1] S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach. Pearson, 2020. [2] F. Chollet, Deep Learning with Python. Manning Publications, 2018. [3] C. C. Aggarwal, Data Mining: The Textbook. Springer, 2015. [4] J. Dean and S. Ghemawat, "MapReduce: Simplified Data Processing on Large Clusters," Communications of the ACM, vol. 51, no. 1, pp. 107-113, 2008. [5] Apache Software Foundation, "Apache... - Source: dev.to / 2 months ago
  • Automating Enhanced Due Diligence in Regulated Applications
    If you're designing an event-based pipeline, you can use a data streaming tool like Kafka to process data as it's collected by the pipeline. For a setup that already has data stored, you can use tools like Apache Spark to batch process and clean it before moving ahead with the pipeline. - Source: dev.to / 3 months ago
View more

IBM MQ mentions (0)

We have not tracked any mentions of IBM MQ yet. Tracking of IBM MQ recommendations started around Mar 2021.

What are some alternatives?

When comparing Apache Spark and IBM MQ, you can also consider the following products

Apache Flink - Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.

RabbitMQ - RabbitMQ is an open source message broker software.

Hadoop - Open-source software for reliable, scalable, distributed computing

Ethereum - Ethereum is a decentralized platform for applications that run exactly as programmed without any chance of fraud, censorship or third-party interference.

Apache Storm - Apache Storm is a free and open source distributed realtime computation system.

Hyperledger - Hyperledger is a multi-project open source collaborative effort hosted by The Linux Foundation, created to advance cross-industry blockchain technologies.

Loading...