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Google BigQuery VS Postman

Compare Google BigQuery VS Postman and see what are their differences

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Contents:
  1. ยป Base Details
  2. ยป Videos
  3. ยป Reviews
  4. ยป Alternatives

Google BigQuery logo Google BigQuery

A fully managed data warehouse for large-scale data analytics.

Postman logo Postman

The Collaboration Platform for API Development
  • Google BigQuery Landing page
    Landing page //
    2023-10-03
  • Postman Landing page
    Landing page //
    2021-07-23

Google BigQuery features and specs

  • Scalability
    BigQuery can effortlessly scale to handle large volumes of data due to its serverless architecture, thereby reducing the operational overhead of managing infrastructure.
  • Speed
    It leverages Google's infrastructure to provide high-speed data processing, making it possible to run complex queries on massive datasets in a matter of seconds.
  • Integrations
    BigQuery easily integrates with various Google Cloud Platform services, as well as other popular data tools like Looker, Tableau, and Power BI.
  • Automatic Optimization
    Features like automatic data partitioning and clustering help to optimize query performance without requiring manual tuning.
  • Security
    BigQuery provides robust security features including IAM roles, customer-managed encryption keys, and detailed audit logging.
  • Cost Efficiency
    The pricing model is based on the amount of data processed, which can be cost-effective for many use cases when compared to traditional data warehouses.
  • Managed Service
    Being fully managed, BigQuery takes care of database administration tasks such as scaling, backups, and patch management, allowing users to focus on their data and queries.

Possible disadvantages of Google BigQuery

  • Cost Predictability
    While the pay-per-use model can be cost-efficient, it can also make cost forecasting difficult. Unexpected large queries could lead to higher-than-anticipated costs.
  • Complexity
    The learning curve can be steep for those who are not already familiar with SQL or Google Cloud Platform, potentially requiring training and education.
  • Limited Updates
    BigQuery is optimized for read-heavy operations, and it can be less efficient for scenarios that require frequent updates or deletions of data.
  • Query Pricing
    Costs are based on the amount of data processed by each query, which may not be suitable for use cases that require frequent analysis of large datasets.
  • Data Transfer Costs
    While internal data movement within Google Cloud can be cost-effective, transferring data to or from other services or on-premises systems can incur additional costs.
  • Dependency on Google Cloud
    Organizations heavily invested in multi-cloud or hybrid-cloud strategies may find the dependency on Google Cloud limiting.
  • Cold Data Performance
    Query performance might be slower for so-called 'cold data,' or data that has not been queried recently, affecting the responsiveness for some workloads.

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.

Analysis of Google BigQuery

Overall verdict

  • Google BigQuery is a powerful and flexible data warehouse solution that suits a wide range of data analytics needs. Its ability to handle large volumes of data quickly makes it a preferred choice for organizations looking to leverage their data effectively.

Why this product is good

  • Google BigQuery is a fully-managed data warehouse that simplifies the analysis of large datasets. It is known for its scalability, speed, and integration with other Google Cloud services. It supports standard SQL, has built-in machine learning capabilities, and allows for seamless data integration from various sources. The serverless architecture means that users don't need to worry about infrastructure management, and its pay-as-you-go model provides cost efficiency.

Recommended for

  • Businesses requiring fast processing of large datasets
  • Organizations that already utilize Google Cloud services
  • Companies looking for a cost-effective, scalable analytics solution
  • Teams interested in using SQL for data analysis
  • Data scientists integrating machine learning with their data workflows

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

Google BigQuery videos

+ Add

Cloud Dataprep Tutorial - Getting Started 101

More videos:

  • Review - Advanced Data Cleanup Techniques using Cloud Dataprep (Cloud Next '19)
  • Demo - Google Cloud Dataprep Premium product demo

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

Category Popularity

0-100% (relative to Google BigQuery and Postman)

Google BigQuery

Postman

Data Dashboard
100 100%
Data Dashboard
0% 0
API Tools
0 0%
API Tools
100% 100
Big Data
100 100%
Big Data
0% 0
APIs
0 0%
APIs
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 Google BigQuery and Postman

Google BigQuery Reviews

Database for Data Analytics
Processing typeDescriptionUse casesCommon databasesProcessing typesProcesses data in scheduled intervals (hours, days). High-latency but cost-efficient for large datasets.Financial reporting, trend analysis, historical analyticsSnowflake, Amazon Redshift, Google BigQueryContinuously ingests and processes data with minimal latency for real-time decision-making.Fraud...
Source: blog.devart.com
Data Warehouse Tools
Google BigQuery: Similar to Snowflake, BigQuery offers a pay-per-use model with separate charges for storage and queries. Storage costs start around $0.01 per GB per month, while on-demand queries are billed at $5 per TB processed.
Source: peliqan.io
Top 6 Cloud Data Warehouses in 2023
You can also use BigQueryโ€™s columnar and ANSI SQL databases to analyze petabytes of data at a fast speed. Its capabilities extend enough to accommodate spatial analysis using SQL and BigQuery GIS. Also, you can quickly create and run machine learning (ML) models on semi or large-scale structured data using simple SQL and BigQuery ML. Also, enjoy a real-time interactive...
Source: geekflare.com
Top 5 Cloud Data Warehouses in 2023
Google BigQuery is an incredible platform for enterprises that want to run complex analytical queries or โ€œheavyโ€ queries that operate using a large set of data. This means itโ€™s not ideal for running queries that are doing simple filtering or aggregation. So if your cloud data warehousing needs lightning-fast performance on a big set of data, Google BigQuery might be a great...
Source: www.shipyardapp.com
Top 5 BigQuery Alternatives: A Challenge of Complexity
BigQuery's emergence as an attractive analytics and data warehouse platform was a significant win, helping to drive a 45% increase in Google Cloud revenue in the last quarter. The company plans to maintain this momentum by focusing on a multi-cloud future where BigQuery advances the cause of democratized analytics.
Source: blog.panoply.io

Postman Reviews

Postman vs Apidog: Choosing the Suitable API Development Tool
Forking Existing Collections: One of Postmanโ€™s unique strengths is the ability to fork collections created by others. Developers can easily duplicate publicly available Postman collections, modifying them to fit their particular needs without starting from scratch. This feature saves time and encourages collaboration by allowing developers to build upon existing work.
Source: dev.to
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

Social recommendations and mentions

Based on our record, Google BigQuery should be more popular than Postman. It has been mentiond 47 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.

Google BigQuery mentions (47)

  • Ruby on Rails Performance: 7 Lessons from Scaling FirstPromoter
    We migrated the analytics layer to Google BigQuery. Same queries that timed out in PostgreSQL now run in under 2 seconds. But not everything belongs in BigQuery โ€” we initially moved too aggressively and actually reverted some queries back when the added complexity wasn't justified. Our rule of thumb: if a query scans hundreds of thousands of rows or involves complex time-series aggregations, BigQuery. Everything... - Source: dev.to / 3 months ago
  • How to Analyze 47 Million Hacker News Posts: A Data Scientist's Dream Dataset Just Got Better
    Google BigQuery - For large-scale data processing and SQL-based analysis. - Source: dev.to / 4 months ago
  • What if ML pipelines had a lock file?
    Data Pipelines usually read from tables that change over time. Most of these tables are stored in a data warehouse like Amazon Redshift or Google BigQuery. Rows are added or removed. Backfills happen. A column gets renamed or its meaning changes. Even when teams snapshot data, those snapshots are often implicit, not recorded as part of the pipeline run itself. - Source: dev.to / 5 months ago
  • Best SQL Courses with Certificates for 2026
    SQL endures because it's the non-negotiable interface for relational data. Enterprise data storage still relies heavily on relational databases despite new alternatives. What makes SQL valuable for learners is transferabilityโ€”while dialects differ across PostgreSQL, SQL Server, and BigQuery, the fundamentals stay consistent. - Source: dev.to / 7 months ago
  • Why Your Snowflake Bill is High and How to Fix It with a Hybrid Approach
    Within classic cloud data warehouses, Google BigQuery presents a different pricing model. Its on-demand, per-terabyte-scanned pricing can be cost-effective for sporadic forensic queries. But it carries the risk of a runaway query where a single mistake leads to a massive bill. - Source: dev.to / 8 months ago
View more

Postman mentions (30)

View more

What are some alternatives?

When comparing Google BigQuery and Postman, you can also consider the following products

Databricks - Databricks provides a Unified Analytics Platform that accelerates innovation by unifying data science, engineering and business.โ€ŽWhat is Apache Spark?

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

Looker - Looker makes it easy for analysts to create and curate custom data experiencesโ€”so everyone in the business can explore the data that matters to them, in the context that makes it truly meaningful.

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.

Jupyter - Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages. Ready to get started? Try it in your browser Install the Notebook.

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

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