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Google BigQuery VS graphql.js

Compare Google BigQuery VS graphql.js 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.

graphql.js logo graphql.js

A reference implementation of GraphQL for JavaScript - graphql/graphql-js
  • Google BigQuery Landing page
    Landing page //
    2023-10-03
  • graphql.js Landing page
    Landing page //
    2023-08-27

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.

graphql.js features and specs

  • Strongly Typed
    GraphQL.js allows for strongly typed schemas, making it easier to perform validation and introspection on your data, ensuring that queries conform to a specific structure before execution.
  • Efficient Data Fetching
    GraphQL.js enables clients to request exactly the data they need which can reduce over-fetching and under-fetching compared to REST APIs.
  • Rich Developer Tooling
    The introspection capabilities in GraphQL.js allow for rich tooling, enabling better development workflows including robust IDE support and tools like GraphiQL.
  • Evolving APIs
    GraphQL.js facilitates evolving APIs without the need for versioning, providing backward compatibility by introducing non-breaking changes.
  • Community Support
    GraphQL.js has a large and active community, providing numerous resources, plugins, and tools that support smooth development processes.

Possible disadvantages of graphql.js

  • Complexity
    Implementing GraphQL.js can add complexity to projects as developers may need to learn new concepts such as schemas, resolvers, and query languages.
  • Overhead
    The flexibility of GraphQL.js can introduce performance overhead, as the server may need to parse and execute more complex and dynamic queries.
  • Cache Invalidation
    Caching strategies for GraphQL.js can be more complex compared to REST, as caching needs to account for the structure and specifics of the queries requested.
  • Over-fetching Risks
    While GraphQL.js mitigates data over-fetching, it can also expose sensitive data if developers are not meticulous in specifying and controlling the schema and access permissions.
  • Debugging Complexity
    Debugging runtime errors in GraphQL.js can sometimes be more difficult, especially with deeply nested queries and complex resolvers.

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

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

graphql.js videos

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

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Category Popularity

0-100% (relative to Google BigQuery and graphql.js)

Google BigQuery

graphql.js

Data Dashboard
100 100%
Data Dashboard
0% 0
Project Management
0 0%
Project Management
100% 100
Big Data
100 100%
Big Data
0% 0
Development
0 0%
Development
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 graphql.js

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

graphql.js Reviews

We have no reviews of graphql.js yet.
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Social recommendations and mentions

Based on our record, Google BigQuery should be more popular than graphql.js. 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 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 / 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

graphql.js mentions (8)

  • Diving into Open-Source Development
    To begin, I'm going to start with GraphQL. This repo is a JS-specific implementation for GraphQL, for which projects written in JS/TS can utilize to build an API for their web app. The reason why I chose this project is because I've always been intrigued by how GraphQl challenges the standard way of building an API, a.k.a REST APIs. I have very little knowledge about this project since I've never used it before at... - Source: dev.to / almost 2 years ago
  • How to define schema once and have server code and client code typed? [Typescript]
    When I asked this in StackOverflow over a year ago I reached the solution of using graphql + graphql-zeus. Source: almost 2 years ago
  • Apollo federated graph is not presenting its schema to graphiql with fields sorted lexicographically
    GraphiQL (and many other tools) relies on introspection query which AFAIK is not guaranteed to have any specific order (and many libs don't support it). Apollo Server is built on top of graphql-js and it relies on it for this functionality. Source: over 2 years ago
  • How (Not) To Build Your Own GraphQL Server
    Defining your schema and the resolvers simultaneously led to some issues for developers, as it was hard to decouple the schema from the (business) logic in your resolvers. The SDL-first approach introduced this separation of concerns by defining the complete schema before connecting them to the resolvers and making this schema executable. A version of the SDL-first approach was introduced together with GraphQL... - Source: dev.to / over 3 years ago
  • three ways to deploy a serverless graphQL API
    Graphql-yoga is built on other packages that provide functionality required for building a GraphQL server such as web server frameworks like express and apollo-server, GraphQL subscriptions with graphql-subscriptions and subscriptions-transport-ws, GraphQL engine & schema helpers including graphql.js and graphql-tools, and an interactive GraphQL IDE with graphql-playground. - Source: dev.to / over 3 years ago
View more

What are some alternatives?

When comparing Google BigQuery and graphql.js, 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?

JsonAPI - Application and Data, Languages & Frameworks, and Query Languages

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.

Apollo - Apollo is a full project management and contact tracking application.

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.

Graphene - Query Languages

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