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Google BigQuery VS V (programming language)

Compare Google BigQuery VS V (programming language) 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.

V (programming language) logo V (programming language)

Simple, fast, safe, compiled language for developing maintainable software.
  • Google BigQuery Landing page
    Landing page //
    2023-10-03
  • V (programming language) Landing page
    Landing page //
    2022-11-25

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.

V (programming language) features and specs

  • Fast Compilation
    V is designed to compile extremely fast, typically within less than a second, regardless of the size of the codebase.
  • Simplicity
    The syntax is simple and easy to learn, drawing inspiration from languages like Go and Python which reduces the learning curve for new developers.
  • Performance
    V aims to offer high performance akin to C, enabling developers to write highly efficient programs.
  • Safe Programming
    It includes built-in mechanisms to avoid common bugs and aims to provide safety features like immutable data structures and option types.
  • Single Binary
    V produces a single small binary without external dependencies, making distribution straightforward and more secure.
  • Cross-Platform
    V supports cross-compilation out of the box, allowing developers to build applications for multiple operating systems from a single codebase.

Possible disadvantages of V (programming language)

  • Maturing Ecosystem
    As a relatively new language, V's ecosystem, including libraries, frameworks, and community support, is still growing and may not be as mature as more established languages.
  • Limited Tooling
    Compared to more established languages, tools like IDE support, debuggers, and other development utilities are still limited or in early stages.
  • Smaller Community
    The community around V is smaller, which can result in fewer resources, tutorials, and third-party libraries being available.
  • Learning Curve for Advanced Features
    While the syntax is simple, mastering some of V's advanced features and paradigms may pose a learning curve to developers coming from more mainstream languages.
  • Rapid Changes
    As the language is still evolving, there can be rapid changes and updates which may introduce breaking changes or require frequent revisions of codebases.

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 V (programming language)

Overall verdict

  • V is a promising language for developers looking for performance and simplicity, with a distinct focus on developer experience. However, as it is relatively new, it still has a smaller community and ecosystem compared to established languages.

Why this product is good

  • V is designed to be simple, fast, and easy to use, drawing inspiration from languages like Go and Rust. It offers fast compilation, cross-platform capabilities, safety features like option types and memory management without a garbage collector, and a strong emphasis on simplicity.

Recommended for

  • Developers interested in a language with fast compilation times.
  • Projects where compile time performance and efficiency are crucial.
  • Developers who want to try a language that combines the principles of simplicity and speed.
  • Enthusiasts looking to be part of a growing community and contribute to an emerging language.

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

V (programming language) videos

+ Add

V Programming Language (Vlang): First Impression - Mike Shah

More videos:

  • Review - Introduction to V and its features (Sydney Computing Society)
  • Review - Testing the NEW "V" programming language!
  • Demo - Presentation of Vlang at IBM
  • Review - An introduction to V (Vlang)

Category Popularity

0-100% (relative to Google BigQuery and V (programming language))

Google BigQuery

V (programming language)

Data Dashboard
100 100%
Data Dashboard
0% 0
Programming Language
0 0%
Programming Language
100% 100
Big Data
100 100%
Big Data
0% 0
OOP
0 0%
OOP
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 V (programming language)

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

V (programming language) Reviews

We have no reviews of V (programming language) yet.
Be the first one to post

Social recommendations and mentions

Based on our record, V (programming language) should be more popular than Google BigQuery. It has been mentiond 74 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

V (programming language) mentions (74)

  • Koto Programming Language
    > For me the biggest gap in programming languages is a rust like language with a garbage collector, instead of a borrow checker. https://vlang.io. - Source: Hacker News / 3 months ago
  • Go Is a Well-Designed Language
    I think V [1] is what Go should’ve been. Simple, compiles fast, integrated language tooling, in fact quite similar to Go, but without all the dumb design decisions. Unlike Go, it has sum types, enums, immutable-by-default variables, option/result types, various other goodies and the syntax for for loops actually makes sense. It’s a shame that the compiler is quite buggy, but hopefully that’s going to improve. [1]... - Source: Hacker News / 5 months ago
  • Mantis - new file log driver
    Mantis is a type-safe web framework written in V that emphasizes explicit, magic-free code. - Source: dev.to / 6 months ago
  • Mantis, a web framework written in V
    For development, V offers both an interpreter and watch mode, combining the convenience of scripting languages with the type safety and performance of compiled languages. It even includes built-in channel-compatible concurrency - truly the best of both worlds. - Source: dev.to / 6 months ago
  • Lies we tell ourselves to keep using Golang
    What is quite interesting (after looking at their documentation), is that V lang[1] has all that is mentioned: `?`[2], `or`[2], sum types[4], and can return multiple values[5]. [1]: https://vlang.io/ [2]: https://github.com/vlang/v/blob/master/doc/docs.md#optionresult-types-and-error-handling [4]: - Source: Hacker News / 7 months ago
View more

What are some alternatives?

When comparing Google BigQuery and V (programming language), 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?

Nim (programming language) - The Nim programming language is a concise, fast programming language that compiles to C, C++ and JavaScript.

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.

Go Programming Language - Go, also called golang, is a programming language initially developed at Google in 2007 by Robert...

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.

D (Programming Language) - D is a language with C-like syntax and static typing.

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