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Google BigQuery VS GitHub Copilot

Compare Google BigQuery VS GitHub Copilot 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.

GitHub Copilot logo GitHub Copilot

Your AI pair programmer. With GitHub Copilot, get suggestions for whole lines or entire functions right inside your editor.
  • Google BigQuery Landing page
    Landing page //
    2023-10-03
  • GitHub Copilot Landing page
    Landing page //
    2023-10-03

Trained on billions of lines of public code, GitHub Copilot puts the knowledge you need at your fingertips, saving you time and helping you stay focused.

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.

GitHub Copilot features and specs

  • Productivity Boost
    GitHub Copilot helps developers write code faster by providing intelligent suggestions and automating repetitive tasks. This can save significant time and reduce the cognitive load on developers.
  • Learning Tool
    For less experienced developers, Copilot can serve as a learning tool by suggesting best practices and introducing them to new coding patterns and techniques.
  • Support for Multiple Languages
    Copilot supports a wide range of programming languages, making it a versatile tool for developers working in different tech stacks.
  • Context-Aware Suggestions
    Copilot offers context-aware suggestions based on the code that has been written so far, making its recommendations relevant to the current development task.
  • Integration with GitHub
    Seamless integration with GitHub simplifies the development workflow, enabling smoother transitions from coding to version control and collaboration.

Possible disadvantages of GitHub Copilot

  • Code Quality Concerns
    The quality of the code generated by Copilot may vary, and it might introduce suboptimal code or practices that could lead to maintenance challenges.
  • Security Risks
    Copilot might suggest insecure code patterns or snippets, potentially introducing vulnerabilities into the project if not carefully reviewed by the developer.
  • Dependence on AI
    Over-reliance on Copilot's suggestions can lead to a lack of deep understanding of the code, which may hinder a developer's growth and problem-solving skills.
  • Licensing and Code Reuse Issues
    There are concerns about the legality and ethics of using AI-generated code snippets that might be derived from copyrighted sources, which can lead to licensing issues.
  • Limited Customizability
    Copilot may not always align with specific coding standards or preferences of a development team, and the ability to customize its behavior to enforce such standards is limited.

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 GitHub Copilot

Overall verdict

  • Overall, GitHub Copilot is a beneficial tool for many developers, especially those looking to increase their productivity and experiment with new coding styles. It can be seen as an intelligent coding assistant that complements a developer's workflow rather than replaces it.

Why this product is good

  • GitHub Copilot is considered good by many because it provides AI-assisted code completion and suggestions, which can significantly speed up coding tasks and improve productivity. It leverages OpenAI's advanced language models to offer context-aware snippets and solutions that can help developers write code more efficiently, reduce errors, and explore new coding approaches.

Recommended for

  • Software developers seeking to increase productivity
  • Beginner programmers looking for contextual code suggestions
  • Experienced developers interested in exploring and discovering alternative coding solutions
  • Teams aiming to standardize code quality and reduce time spent on routine coding tasks

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

GitHub Copilot videos

+ Add

Game over… GitHub Copilot X announced

More videos:

  • Review - The New GitHub Copilot X Powered by GPT-4 is Here!
  • Review - GitHub Copilot X -- AI Programming Gets Better... and Scary.
  • Review - GitHub Copilot Review 2023: I Love It, But It's Not For Everyone
  • Review - Is Github Copilot Worth Paying For??

Category Popularity

0-100% (relative to Google BigQuery and GitHub Copilot)

Google BigQuery

GitHub Copilot

Data Dashboard
100 100%
Data Dashboard
0% 0
Developer Tools
0 0%
Developer Tools
100% 100
Big Data
100 100%
Big Data
0% 0
AI
0 0%
AI
100% 100

User comments

Share your experience with using Google BigQuery and GitHub Copilot. For example, how are they different and which one is better?
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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 GitHub Copilot

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

GitHub Copilot Reviews

  1. Stan
    · Founder at SaaSHub ·
    Indispensable

    It definitely increases my productivity.

    🏁 Competitors: Tabnine
Cursor vs Windsurf vs GitHub Copilot
GitHub Copilot Chat is similar — you can ask it to explain code or suggest improvements. It's integrated right into VS Code, so it feels pretty seamless. They've been rolling out some new features lately, like better chat history, drag and & folders and ways to attach more context. But if you're already using Cursor, you might not find anything groundbreaking here.
Source: www.builder.io
Cursor vs GitHub Copilot
GitHub Copilot Chat is similar — you can ask it to explain code or suggest improvements. It's integrated right into VS Code, so it feels pretty seamless. They've been rolling out some new features lately, like better chat history, drag and & folders and ways to attach more context. But if you're already using Cursor, you might not find anything groundbreaking here.
Source: www.builder.io
Top 10 Vercel v0 Open Source Alternatives | Medium
Next up, we have GitHub Copilot, a popular AI-powered code completion tool that’s been making waves in the developer community. Built on top of OpenAI Codex, Copilot integrates seamlessly with various code editors and IDEs to provide intelligent code suggestions as you type.
Source: medium.com
10 Best Github Copilot Alternatives in 2024
GitHub Copilot is an excellent tool for developers, allowing them to boost their workflow and project quality. Are you looking for a GitHub Copilot alternative that fits your needs in 2024? Whether you’re searching for a free GitHub Copilot alternative, an open-source alternative to GitHub Copilot, or a tool that works well with VSCode, this guide is here to help.
Source: www.geeksforgeeks.org
The Best GitHub Copilot Alternatives for Developers
Moreover, GitHub Copilot provides a wide range of functionalities, such as code explanation, answering coding questions, refactoring code, and generation of unit tests and docs. With it, developers can automate coding tasks, improve productivity and focus more on complex coding. GitHub Copilot supports various programming languages, including JavaScript, Python, Ruby, Go,...
Source: softteco.com

Social recommendations and mentions

Based on our record, GitHub Copilot should be more popular than Google BigQuery. It has been mentiond 293 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

GitHub Copilot mentions (293)

  • Semantic Code Search
    We at Ducky.ai noticed a strange thing has happened in software development, we’re no longer writing code in the traditional sense. Instead, we describe what we want and ask the machine to write the first draft. Tools like GitHub Copilot, Cursor, Replit, and Devin have changed what it means to build. The keyboard isn’t gone, but it’s quieter. Developers are prompting, guiding, reviewing. Code appears in response... - Source: dev.to / 1 day ago
  • Level Up Your Coding Game with These Free Vibe Coding Tools!
    Microsoft's AI pair programmer revolutionized code completion with context-aware suggestions drawn from entire codebases. Copilot Chat now extends beyond autocomplete to explain complex logic, generate tests, and refactor legacy code. The 2025 update introduced Copilot Extensions that integrate directly with CI/CD pipelines and cloud services. Developers report 55% faster coding speeds when using its advanced code... - Source: dev.to / 10 days ago
  • why software engineering feels like you’re losing your mind and why you’re not alone
    GitHub Copilot Pair program with AI The $10/month mental health hack we didn’t know we needed. https://github.com/features/copilot. - Source: dev.to / 10 days ago
  • Agentic AI: Your New Coding Buddy
    Unlike autocomplete tools like GitHub Copilot, Agentic AI can take broad instructions like “build me a login page with email verification” and handle it end-to-end. - Source: dev.to / 13 days ago
  • Top 10 Programming Trends and Languages to Watch in 2025
    AI-assisted coding is transforming software development by enhancing efficiency, reducing repetitive tasks, and improving code quality. Tools like GitHub Copilot, Amazon CodeWhisperer, and OpenAI's Codex provide developers with suggestions for entire functions, automate boilerplate code, and identify real-time errors. AI also supports early bug detection and automated code reviews, which are essential for... - Source: dev.to / 14 days ago
View more

What are some alternatives?

When comparing Google BigQuery and GitHub Copilot, 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?

Codeium - Free AI-powered code completion for *everyone*, *everywhere*

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.

Tabnine - TabNine is the all-language autocompleter. We use deep learning to help you write code faster.

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.

Cursor - The AI-first Code Editor. Build software faster in an editor designed for pair-programming with AI.

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