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Google BigQuery VS JSON Formatter & Validator

Compare Google BigQuery VS JSON Formatter & Validator and see what are their differences

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Google BigQuery logo Google BigQuery

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

JSON Formatter & Validator logo JSON Formatter & Validator

The JSON Formatter was created to help with debugging.
  • Google BigQuery Landing page
    Landing page //
    2023-10-03
  • JSON Formatter & Validator Landing page
    Landing page //
    2021-10-08

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.

JSON Formatter & Validator features and specs

  • User-Friendly Interface
    The website has a clean, intuitive design that makes it easy for users to paste their JSON text and quickly format or validate it.
  • Real-time Validation
    As soon as the JSON data is pasted, it automatically validates and provides errors, helping users quickly identify and fix issues.
  • Clear Error Messages
    The tool provides detailed error messages, which makes it easier for users to understand where their JSON is failing validation.
  • Formatting Options
    It provides options to pretty-print JSON, making data easier to read and analyze.
  • No Installation Required
    Being a web-based tool, it requires no download or installation, making it easily accessible from any browser.

Possible disadvantages of JSON Formatter & Validator

  • Internet Connectivity Required
    Because it is a web-based tool, it requires an internet connection to use, which can be a limitation in offline scenarios.
  • Security Concerns
    Pasting sensitive JSON data into a web-based tool can pose security risks, especially if the data contains confidential information.
  • Limited Advanced Features
    The tool does not offer advanced features such as JSON schema validation or linting capabilities that some developers might need.
  • Performance with Large Files
    The tool might experience lag or performance issues when working with very large JSON files.
  • No API Integration
    It lacks an API for programmatic access, which limits automated workflows and integration into development pipelines.

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 JSON Formatter & Validator

Overall verdict

  • Yes, JSON Formatter & Validator (jsonformatter.curiousconcept.com) is a good tool for handling JSON data. It is reliable, user-friendly, and accessible online without the need for any installations.

Why this product is good

  • JSON Formatter & Validator by Curious Concept is widely regarded as a useful tool for developers and data professionals who need to review, format, and validate JSON data. Its interface is straightforward, and it provides clear feedback on JSON syntax errors, making it a helpful resource for troubleshooting data issues. The tool also offers features like JSON prettification and minification, which are useful for making JSON data more readable or compact, depending on the user's needs.

Recommended for

    This tool is recommended for software developers, data analysts, and anyone working with JSON data. It's particularly useful for those who require a quick and easy way to validate or format JSON data online without using more complex software environments.

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

JSON Formatter & Validator videos

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Category Popularity

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Google BigQuery

JSON Formatter & Validator

Data Dashboard
100 100%
Data Dashboard
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Development
0 0%
Development
100% 100
Big Data
100 100%
Big Data
0% 0
Developer Tools
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Developer Tools
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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 JSON Formatter & Validator

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

JSON Formatter & Validator Reviews

We have no reviews of JSON Formatter & Validator yet.
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Social recommendations and mentions

Google BigQuery might be a bit more popular than JSON Formatter & Validator. We know about 42 links to it since March 2021 and only 36 links to JSON Formatter & Validator. 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

JSON Formatter & Validator mentions (36)

  • Postman Tutorial: A Beginner's Step-by-Step Guide!
    **Note:* Online Post request should have the correct format to ensure that requested data will be created. It is a good practice to use Get first to check the JSON format of the request. You can use tools like https://jsonformatter.curiousconcept.com/. - Source: dev.to / 4 months ago
  • Rest API Testing: How to test Rest APIs properly!
    This can look like this, for example. Postman shows you errors in the JSON structure directly. However, you can test it more precisely with this JSON validator. - Source: dev.to / 12 months ago
  • Homebridge failed to load Config.schema.json
    Did you already validate your json with: JSON VALIDATOR? Source: about 2 years ago
  • 5 useful JSON tools to improve your productivity
    As we've seen in this article, there are many different tools available to help us work with JSON data. From visualizing and exploring data with JSON Crack, formatting it with JSON Formatter & Validator, converting it to other formats like CSV with Konklone.io, and validating it against a schema with JSON Schema — these tools can help make working with JSON data much easier and more efficient. - Source: dev.to / about 2 years ago
  • Beginner's Thread / Easy Questions [February 2023]
    Is there a library to parse json and make an interactive windows something like https://jsonformatter.curiousconcept.com. Source: over 2 years ago
View more

What are some alternatives?

When comparing Google BigQuery and JSON Formatter & Validator, 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.

JSONLint - JSON Lint is a web based validator and reformatter for JSON, a lightweight data-interchange format.

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 Editor Online - View, edit and format JSON online

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