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Postman VS Apache Spark

Compare Postman VS Apache Spark and see what are their differences

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Postman logo Postman

The Collaboration Platform for API Development

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.
  • Postman Landing page
    Landing page //
    2021-07-23
  • Apache Spark Landing page
    Landing page //
    2021-12-31

Postman features and specs

  • User-Friendly Interface
    Postman features an intuitive and user-friendly interface that simplifies the process of constructing API requests and visualizing responses. This makes it accessible for both beginners and advanced users.
  • Collaboration
    Postman offers robust collaboration features, such as shared workspaces, collections, and real-time editing, enabling teams to work together more efficiently on API development.
  • Comprehensive Testing Tools
    Postman provides a suite of testing tools to create, automate, and manage test cases. It supports automated testing through its scripting environments, which ensure APIs perform as expected.
  • Extensive API Documentation
    Postman can automatically generate comprehensive API documentation, making it easier to maintain and share API specifications with stakeholders and other developers.
  • Mock Servers
    Postman allows users to create mock servers to simulate API responses. This is particularly useful for testing and development purposes when the actual API is not yet available.
  • Integration Capabilities
    Postman offers integrations with various CI/CD tools, version control systems, and other services like Jenkins, GitHub, and Slack, facilitating seamless integration into development workflows.

Possible disadvantages of Postman

  • Resource Intensive
    Postman can sometimes be resource-intensive, consuming substantial memory and CPU, which can impact the performance of your system, especially when dealing with large collections.
  • Steep Learning Curve for Advanced Features
    While Postman is generally user-friendly, some of its advanced features, like scripting and automation, can have a steep learning curve and might require additional effort to master.
  • Pricing
    Although Postman offers a free tier, many of its advanced features, such as enhanced collaboration tools and extended integrations, are locked behind paid plans, which may not be cost-effective for smaller teams or individual developers.
  • Dependency on Internet
    Some of Postman's features, particularly those related to collaboration and synchronization, require a stable internet connection, which can be a limitation in environments with poor connectivity.
  • Limited Native Support for Certain Protocols
    Postman primarily focuses on HTTP/HTTPS protocols and may offer limited or no native support for other protocols, which can be restricting for developers working with diverse sets of technologies.

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.

Analysis of Postman

Overall verdict

  • Yes, Postman is widely regarded as a good tool for API development and testing. Its combination of powerful features and ease of use makes it a popular choice among developers.

Why this product is good

  • Postman is considered a top choice for API development due to its user-friendly interface, extensive features for testing, automation, and collaboration, and strong community support. It simplifies the process of creating, managing, and testing APIs, making it accessible for both beginners and experienced developers.

Recommended for

  • Developers working on API integration
  • QA engineers involved in testing APIs
  • Teams in need of collaborative API development
  • Developers looking to automate API testing
  • Individuals looking for a comprehensive API testing tool

Analysis of Apache Spark

Overall verdict

  • Yes, Apache Spark is generally considered good, especially for organizations and individuals that require efficient and fast data processing capabilities. It is well-supported, frequently updated, and widely adopted in the industry, making it a reliable choice for big data solutions.

Why this product is good

  • Apache Spark is highly valued because it provides a fast and general-purpose cluster-computing framework for big data processing. It offers extensive libraries for SQL, streaming, machine learning, and graph processing, making it versatile for various data processing needs. Its in-memory computing capability boosts the processing speed significantly compared to traditional disk-based processing. Additionally, Spark integrates well with Hadoop and other big data tools, providing a seamless ecosystem for large-scale data analysis.

Recommended for

  • Data scientists and engineers working with large datasets.
  • Organizations leveraging machine learning and analytics for decision-making.
  • Businesses needing real-time data processing capabilities.
  • Developers looking to integrate with Hadoop ecosystems.
  • Teams requiring robust support for multiple data sources and formats.

Postman videos

+ Add

POST/CON 2018 workshop in review: Running Postman Collections

More videos:

  • Review - POST/CON 2018 workshop in review: Postman Collections
  • Tutorial - How to Share Postman Collections

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

Category Popularity

0-100% (relative to Postman and Apache Spark)

Postman

Apache Spark

API Tools
100 100%
API Tools
0% 0
Databases
0 0%
Databases
100% 100
APIs
100 100%
APIs
0% 0
Big Data
0 0%
Big Data
100% 100

User comments

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Reviews

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

Postman Reviews

Top 20 Open Source & Cloud Free Postman Alternatives (2024 Updated)
As the digital landscape evolves, the significance of APIs (Application Programming Interfaces) has surged, facilitating seamless communication between various software applications. Postman has been a leading tool in this space, offering a comprehensive platform for API development, testing, and documentation. However, recent shifts in its pricing model and user experience...
Source: medium.com
Best Postman Alternatives To Consider in 2025
- Focus on specific needs: Does the tool excel at SOAP APIs or cater to microservices? - Resource usage: Does it handle complex projects without impacting system performance? - Script reusability: Does it allow for efficient code sharing across projects?3. Is Postman the best API tool?Not all-encompassing. While Postman is powerful, the "best" tool depends on your specific...
Source: www.hypertest.co
Postman Alternatives for API Testing and Monitoring
Some engineers turn to Postman for API testing and monitoring needs. However, Postman is a costly and limited solution. QA, DevOps and other engineers may find it lacks capabilities that can answer their needs. In this blog post, we provide 12 Postman alternatives built for the enterprise.
Source: www.blazemeter.com
Beeceptor vs Postman
You cannot download request log. Although, you can use Postman APIs to query and retrieve.
Source: beeceptor.com
Top 15 MuleSoft Competitors and Alternatives
Postman is an API platform with the world’s largest public API hub that helps developers design, build, test, and iterate APIs. In 2022, Postman served over 20 million developers and 500,000 organizations.
Source: bstrategyhub.com

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

Social recommendations and mentions

Based on our record, Apache Spark should be more popular than Postman. 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.

Postman mentions (30)

  • Best API Mocking Platforms in 2024
    Postman (postman.com) is a comprehensive API platform that goes beyond mocking, offering a full suite for API development, testing, and monitoring. With its mock server feature, Postman enables teams to simulate responses for various endpoints, making it a popular choice for end-to-end API management. - Source: dev.to / 7 months ago
  • 10 Best API Mocking Tools (2024 Review)
    Postman is a widely used tool for API testing and interaction. Its "Mock Servers" feature lets you create a mock version of your API, returning specific responses for testing. While useful, Postman may lack advanced mock server management features compared to other tools. - Source: dev.to / 7 months ago
  • The 3 Best Tools for API Design for Software Architects
    Postman is a widely adopted tool for API design and development, offering an intuitive interface for creating, testing, and documenting APIs. It simplifies the API design process, allowing architects to quickly prototype and refine their designs. - Source: dev.to / 10 months ago
  • How to use ApyHub to Build a Serverless Function in NodeJs?
    Once deployed, thoroughly test your serverless function to confirm it behaves as expected. Invoke the function manually from the cloud platform’s console or use tools like Postman, Apidog, or Fusion ( Fusion is ApyHub’s own API Client ) to test HTTP-triggered functions. Ensure the function executes correctly and handles errors gracefully. - Source: dev.to / about 1 year ago
  • Mastering Microservices: A Hands-On Tutorial with Node.js, RabbitMQ, Nginx, and Docker
    To test the API endpoints, you can use Postman. Download and install Postman from Postman's official website. - Source: dev.to / over 1 year ago
View more

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 / 3 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 / 3 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

What are some alternatives?

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

Insomnia REST - Design, debug, test, and mock APIs locally, on Git, or cloud. Build better APIs collaboratively for the most popular protocols with a dev‑friendly UI, built-in automation, and an extensible plugin ecosystem.

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

MuleSoft Anypoint Platform - Anypoint Platform is a unified, highly productive, hybrid integration platform that creates an application network of apps, data and devices with API-led connectivity.

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

DreamFactory - DreamFactory is an API management platform used to generate, secure, document, and extend APIs.

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

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