Software Alternatives, Accelerators & Startups
Register | Login

Google BigQuery VS JSON

Compare Google BigQuery VS JSON and see what are their differences

Draxlr
Turn SQL Data into Decisions. Build professional dashboards and data visualizations without technical expertise. Easily embed analytics anywhere, receive automated alerts, and discover AI-powered insights all through a straightforward interface. 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.

JSON logo JSON

(JavaScript Object Notation) is a lightweight data-interchange format
  • Google BigQuery Landing page
    Landing page //
    2023-10-03
  • JSON Landing page
    Landing page //
    2021-09-28

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 features and specs

  • Simplicity
    JSON is easy to read and write due to its straightforward syntax, making it a convenient data format for both humans and machines.
  • Language Independence
    JSON is supported by many programming languages, making it a versatile choice for data interchange across different environments.
  • Lightweight
    JSON's compact format allows for efficient data transfer, which is particularly beneficial in web applications where bandwidth is a concern.
  • Integration
    JSON easily integrates with modern web technologies and APIs, making it a preferred choice for RESTful services and web applications.
  • Data Structure
    JSON supports complex data structures, including objects and arrays, providing flexibility in representing various data forms.

Possible disadvantages of JSON

  • Limited Data Types
    JSON supports a limited set of data types, which may require additional handling when working with more complex data structures found in other formats.
  • No Comments
    JSON lacks a native mechanism for including comments within the data, which can be a limitation for documentation and readability purposes.
  • Security Concerns
    Parsing JSON can introduce security vulnerabilities if not properly handled, such as malicious data execution through insecure deserialization.
  • Verbosity
    Although lightweight, JSON can become verbose for highly nested structures, which can impact readability and processing performance.
  • Error Handling
    JSON's lack of detailed error handling mechanisms can make debugging more difficult when dealing with malformed data or parsing errors.

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

Overall verdict

  • Yes, JSON is generally considered a good choice for data interchange, especially in web applications, due to its simplicity, wide support across programming languages, and ease of use.

Why this product is good

  • JSON is a lightweight data interchange format that is easy for humans to read and write and easy for machines to parse and generate. Due to its simplicity and flexibility, it has become a widely adopted standard for data exchange on the web.

Recommended for

  • Web APIs and services
  • Applications needing a lightweight data format
  • Communication between server and client
  • Configuration files
  • Data interchange between diverse systems

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 videos

+ Add

Parsing JSON Review - Part 1

More videos:

  • Review - Parsing JSON Review - Part 2
  • Review - JSon Foreign Vol.1 Review

Category Popularity

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

Google BigQuery

JSON

Data Dashboard
100 100%
Data Dashboard
0% 0
Developer Tools
0 0%
Developer Tools
100% 100
Big Data
100 100%
Big Data
0% 0
Software Development
0 0%
Software Development
100% 100

User comments

Share your experience with using Google BigQuery and JSON. 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 JSON

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 Reviews

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

Social recommendations and mentions

Based on our record, Google BigQuery should be more popular than JSON. It has been mentiond 42 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 1 month 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 1 month 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 / about 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 mentions (13)

  • The Last Breaking Change | JSON Schema Blog
    The YAML 0.1 spec was sent to a public user group in May 2001. JSON was named in a State Software internal discussion. State Software was founded in March 2001. json.org was launched in 2002. Therefore you’re just wrong: YAML came out before JSON. Source: about 2 years ago
  • Why does wine give warnings about using 64bit prefixes, or has 32bit packages? Hasn't the world moved on from 32 bit a century ago?
    How come that doesn't apply to other libraries? For example, when I write Java or Node.js programs, I don't need to make sure packages like json.org or express.js have a 32bit or 64bit environment. What makes windows libs different than NPM libs? Source: almost 3 years ago
  • “Ignore the f'ing haters ” And other lessons learned from creating a popular
    The first two sentences of the text on http://json.org are "JSON (JavaScript Object Notation) is a lightweight data-interchange format. It is easy for humans to read and write." It's a primary goal of JSON, it's fair to question whether it's successful at it. Personally, I'd much rather write TOML or S expressions. I don't like YAML at all, the whitespace sensitivity drives me nuts. - Source: Hacker News / almost 3 years ago
  • Recording your JSON data to MCAP, a file format that support multiple serialization formats
    To help you make the transition, we’ve written a tutorial on how to write an MCAP writer in Python to record JSON data to an MCAP file. Source: almost 3 years ago
  • replace \" with "
    What you need to probably do is to step back and learn the format for JSON, and the core data structures that you will find in most languages:. Source: almost 3 years ago
View more

What are some alternatives?

When comparing Google BigQuery and JSON, 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?

LibreOffice - Base - Base, database, database frontend, LibreOffice, ODF, Open Standards, SQL, ODBC

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.

Brilliant Database - Create a personal or business desktop database fast and easily using this simple all-in-one database software. Free 30 day trial.

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.

Microsoft Office Access - Access is now much more than a way to create desktop databases. It’s an easy-to-use tool for quickly creating browser-based database applications.

Loading...