Apache Kylin VS Postman

• High Query Performance
  Apache Kylin is designed for high-performance, low-latency analytics on large datasets. Its OLAP engine pre-computes and stores aggregated queries, which speeds up query responses significantly.
• Scalability
  Kylin can handle massive volumes of data, making it suitable for large scale data warehousing needs. It is designed to scale out by distributing the workload across a cluster of servers.
• Integration with Hadoop Ecosystem
  Kylin integrates seamlessly with the Hadoop ecosystem, leveraging tools like Hive, HBase, and Spark to facilitate data processing and storage, thereby enhancing its functionality and compatibility.
• Support for Multi-dimensional Analysis
  It provides strong multidimensional analysis capabilities, allowing for complex queries using well-known BI tools like Tableau and Power BI.

Possible disadvantages

• Complex Setup
  Setting up and configuring Apache Kylin can be complex and time-consuming, requiring a deep understanding of the Hadoop ecosystem and its components.
• Resource Intensity
  The pre-computation of data cubes and their storage can be resource-intensive, consuming significant memory and storage capacity.
• Limited Flexibility in Querying
  Pre-aggregated cube-based analysis may not cover all ad-hoc queries. Kylin's strength lies in pre-aggregated queries but may fall short in handling highly dynamic, on-the-fly queries.
• Maintenance Overhead
  Maintaining Kylin’s precomputed cubes can become cumbersome, particularly as data evolves or changes frequently, requiring updates or recalculations of cubes.

• 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

• 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.

No analysis of Apache Kylin yet.

Extreme OLAP Analytics with Apache Kylin - Big Data Application Meetup

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POST/CON 2018 workshop in review: Running Postman Collections

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