Software Alternatives, Accelerators & Startups
Register | Login

Google BigQuery VS JSONLint

Compare Google BigQuery VS JSONLint and see what are their differences

ASocks
Clear, Fast & Unlimited. Residential & Mobile Proxies For Best Price. 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

Google BigQuery logo Google BigQuery

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

JSONLint logo JSONLint

JSON Lint is a web based validator and reformatter for JSON, a lightweight data-interchange format.
  • Google BigQuery Landing page
    Landing page //
    2023-10-03
  • JSONLint Landing page
    Landing page //
    2023-06-18

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.

JSONLint features and specs

  • User-Friendly Interface
    JSONLint offers a simple and intuitive web interface that makes it easy to validate JSON data without the need for advanced technical skills.
  • Error Highlighting
    The tool highlights exactly where the errors are in the JSON data, making it easier to identify and correct mistakes quickly.
  • Free to Use
    JSONLint is freely accessible to anyone with an internet connection, making it a cost-effective solution for validating JSON data.
  • JSON Formatter
    In addition to validating JSON, JSONLint also offers functionality to format and beautify JSON data, improving readability.
  • Quick Processing
    The tool processes JSON data quickly, providing almost instant feedback which is useful during development and debugging.

Possible disadvantages of JSONLint

  • Internet Connection Required
    JSONLint is a web-based tool, so it requires an active internet connection to function, which can be a limitation in offline environments.
  • Basic Features
    While JSONLint is excellent for simple validation and formatting, it lacks more advanced features like schema validation or integration with development environments.
  • No API
    JSONLint does not offer an API for programmatic access, limiting its use in automated workflows and larger development pipelines.
  • Ads on the Website
    The website includes advertisements, which can be distracting for users and might affect the user experience.
  • Limited Customization
    The tool does not offer much in terms of customization options for how errors are displayed or how JSON is formatted, which might not meet all user needs.

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 JSONLint

Overall verdict

  • Yes, JSONLint is a good tool for validating and formatting JSON. It is reliable, easy to use, and widely recommended by developers for ensuring the correctness and readability of JSON data.

Why this product is good

  • JSONLint is considered good because it provides a simple and effective way to validate and format JSON data, helping developers quickly identify and correct errors in their JSON structures. Its user-friendly interface and straightforward functionality make it accessible to both beginners and experienced developers.

Recommended for

  • Developers working with JSON
  • Web developers
  • API developers
  • Anyone needing to validate JSON data

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

JSONLint videos

No JSONLint videos yet. You could help us improve this page by suggesting one.

Add video

Category Popularity

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

Google BigQuery

JSONLint

Data Dashboard
100 100%
Data Dashboard
0% 0
Development
0 0%
Development
100% 100
Big Data
100 100%
Big Data
0% 0
Image Optimisation
0 0%
Image Optimisation
100% 100

User comments

Share your experience with using Google BigQuery and JSONLint. 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 Google BigQuery and JSONLint

Google BigQuery Reviews

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 BigQuery’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 “heavy” queries that operate using a large set of data. This means it’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
16 Top Big Data Analytics Tools You Should Know About
Google BigQuery is a fully-managed, serverless data warehouse that enables scalable analysis over petabytes of data. It is a Platform as a Service that supports querying using ANSI SQL. It also has built-in machine learning capabilities.
Source: www.analytixlabs.co.in

JSONLint Reviews

We have no reviews of JSONLint yet.
Be the first one to post

Social recommendations and mentions

Based on our record, JSONLint should be more popular than Google BigQuery. It has been mentiond 135 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 (42)

  • Every Database Will Support Iceberg — Here's Why
    This isn’t hypothetical. It’s already happening. Snowflake supports reading and writing Iceberg. Databricks added Iceberg interoperability via Unity Catalog. Redshift and BigQuery are working toward it. - Source: dev.to / about 2 months ago
  • RisingWave Turns Four: Our Journey Beyond Democratizing Stream Processing
    Many of these companies first tried achieving real-time results with batch systems like Snowflake or BigQuery. But they quickly found that even five-minute batch intervals weren't fast enough for today's event-driven needs. They turn to RisingWave for its simplicity, low operational burden, and easy integration with their existing PostgreSQL-based infrastructure. - Source: dev.to / about 2 months ago
  • How to Pitch Your Boss to Adopt Apache Iceberg?
    If your team is managing large volumes of historical data using platforms like Snowflake, Amazon Redshift, or Google BigQuery, you’ve probably noticed a shift happening in the data engineering world. A new generation of data infrastructure is forming — one that prioritizes openness, interoperability, and cost-efficiency. At the center of that shift is Apache Iceberg. - Source: dev.to / 2 months ago
  • Study Notes 2.2.7: Managing Schedules and Backfills with BigQuery in Kestra
    BigQuery Documentation: Google Cloud BigQuery. - Source: dev.to / 4 months ago
  • Docker vs. Kubernetes: Which Is Right for Your DevOps Pipeline?
    Pro Tip: Use Kubernetes operators to extend its functionality for specific cloud services like AWS RDS or GCP BigQuery. - Source: dev.to / 7 months ago
View more

JSONLint mentions (135)

  • How to Store Multi-Line Strings in JSON
    Or paste your JSON into JSONLint. Both tools immediately identify stray control characters. - Source: dev.to / about 2 months ago
  • Chapter 1: setup, CSS, version control and SASS
    Our old pal VS Code will probably throw up some wiggly red lines if we do it wrong, so look out for them. If you're struggling to see why it doesn't work, try an online JSON Validator and see if it pushes you in the right direction. - Source: dev.to / 3 months ago
  • JSON Unescape: Understanding and Using It Effectively
    Online Tools: Platforms like JSONLint and FreeFormatter allow users to paste JSON data and unescape it with a click. - Source: dev.to / 5 months ago
  • Mastering JSON: How to Parse JSON Like a Pro
    Most APIs love JSON; it's their go-to language. Getting the hang of its structure can help keep your boat afloat in this sea of code. JSON mistakes can have you drifting off course, so it's good practice to validate your JSON using tools like this handy validator. It's like having a spell-check for your syntax, ensuring your JSON is shipshape before you set sail with tests. - Source: dev.to / 6 months ago
  • A little help with some server side work please
    You could, but just as easy to put it here - https://jsonlint.com/. Source: over 1 year ago
View more

What are some alternatives?

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

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

JSONFormatter.org - Online JSON Formatter and JSON Validator will format JSON data, and helps to validate, convert JSON to XML, JSON to CSV. Save and Share JSON

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

JSON Editor Online - View, edit and format JSON online

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.

JSON Formatter & Validator - The JSON Formatter was created to help with debugging.

Loading...