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D (Programming Language) VS Google BigQuery

Compare D (Programming Language) VS Google BigQuery and see what are their differences

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Contents:
  1. ยป Base Details
  2. ยป Videos
  3. ยป Reviews
  4. ยป Alternatives

D (Programming Language) logo D (Programming Language)

D is a language with C-like syntax and static typing.

Google BigQuery logo Google BigQuery

A fully managed data warehouse for large-scale data analytics.
  • D (Programming Language) Landing page
    Landing page //
    2023-05-09
  • Google BigQuery Landing page
    Landing page //
    2023-10-03

D (Programming Language) features and specs

  • Performance
    D is designed to be a high-performance systems programming language, offering performance comparable to C and C++ through native machine code compilation.
  • Expressiveness
    D features a rich standard library and modern language constructs, such as garbage collection, first-class arrays, and advanced templating, making it easier to write expressive and maintainable code.
  • Memory Safety
    D offers optional garbage collection along with manual memory management. This hybrid approach can help in developing safer applications by reducing memory-related errors.
  • Interoperability
    D can easily interoperate with C API, enabling seamless integration with existing C libraries and systems. It also supports better C++ interoperability compared to other languages.
  • Built-in Unit Testing
    D has built-in support for unit tests, allowing developers to write and run tests as part of the language itself, facilitating test-driven development.
  • Concurrency
    D offers built-in concurrency support with message passing, similar to the actor model found in languages like Erlang, making it easier to write concurrent and parallel programs.

Possible disadvantages of D (Programming Language)

  • Adoption
    D is not as widely adopted as other languages like C, C++, or Java. This limited adoption means fewer libraries, frameworks, and community support.
  • Toolchain Maturity
    While D's compilers and tools have improved over the years, they may still lack the maturity and feature set of more established languages, which can affect developer productivity.
  • Learning Curve
    D's richness and combination of paradigms (such as imperative, object-oriented, and functional programming) can present a steep learning curve for new developers.
  • Garbage Collection
    Although D offers optional garbage collection, its reliance on it for memory safety might be seen as a drawback for real-time system development where deterministic memory management is crucial.
  • Ecosystem
    The ecosystem for D is less vibrant compared to more popular languages, leading to potentially fewer third-party libraries, tools, and resources.
  • Standard Library Documentation
    The standard library documentation can be inconsistent or less comprehensive compared to other languages, making it difficult for developers to fully utilize all features of the language.

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.

Analysis of D (Programming Language)

Overall verdict

  • Overall, D is a solid programming language choice that balances performance with productivity. It may not be as widely adopted as some other languages, but it has a dedicated community and continues to evolve, making it a viable option for various programming tasks.

Why this product is good

  • The D programming language is considered good by many developers for various reasons. It combines the performance and low-level control of C/C++ with the expressive power and ease of use found in modern languages. D offers features like garbage collection, first-class functions, and compile-time function execution, providing both speed and flexibility. Its interoperability with C, the convenience of a powerful standard library (Phobos), and the availability of packages via the DUB package manager make it a practical choice for systems programming, application development, and rapid prototyping.

Recommended for

  • System programming enthusiasts looking for an alternative to C/C++
  • Developers interested in writing high-performance applications
  • Programmers who appreciate modern language features and strong community support
  • Projects requiring seamless C integration
  • Individuals looking for a language that supports easy code maintenance and scalability

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

D (Programming Language) videos

+ Add

D Language Tutorial

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

Category Popularity

0-100% (relative to D (Programming Language) and Google BigQuery)

D (Programming Language)

Google BigQuery

Programming Language
100 100%
Programming Language
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Data Dashboard
0 0%
Data Dashboard
100% 100
OOP
100 100%
OOP
0% 0
Big Data
0 0%
Big Data
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 D (Programming Language) and Google BigQuery

D (Programming Language) Reviews

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Google BigQuery Reviews

Database for Data Analytics
Processing typeDescriptionUse casesCommon databasesProcessing typesProcesses data in scheduled intervals (hours, days). High-latency but cost-efficient for large datasets.Financial reporting, trend analysis, historical analyticsSnowflake, Amazon Redshift, Google BigQueryContinuously ingests and processes data with minimal latency for real-time decision-making.Fraud...
Source: blog.devart.com
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 BigQueryโ€™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 โ€œheavyโ€ queries that operate using a large set of data. This means itโ€™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

Social recommendations and mentions

D (Programming Language) might be a bit more popular than Google BigQuery. We know about 60 links to it since March 2021 and only 47 links to Google BigQuery. 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.

D (Programming Language) mentions (60)

  • Ask HN: What is your (AI) dev tech stack / workflow? (June 2026)
    I've spent 2 weeks (2-4h per day) to make D language[1] version of Sciter SDK [2] Choice of AI "tooling" was by accident - typed something like "how to define copy constructor in D for custom structure" in Microsoft's Copilot in Edge browser that gives context for AI. The answer was good enough for me and so I went with it further. [1] D language HQ : https://dlang.org/. - Source: Hacker News / 2 months ago
  • Rue: Higher level than Rust, lower level than Go
    > Mostly, I am not really trying to compete with C/C++/Rust on speed, but I'm not going to add a GC either. So I'm somewhere in there. Out of curiosity, how would you compare the goals of Rue with something like D[0] or one of the ML-based languages such as OCaml[1]? 0 - https://dlang.org/ 1 - https://ocaml.org/. - Source: Hacker News / 8 months ago
  • Pony: An actor-model, capabilities-secure, high-performance programming language
    The D language home page has something similar with a drop down with code examples https://dlang.org/. - Source: Hacker News / about 1 year ago
  • Show HN: D++lang โ€“ A new systems programming language with Python-like syntax
    What is this? There's a lot of red flags here. * The name "D" for a programming language was taken in 1999: https://dlang.org/. - Source: Hacker News / about 1 year ago
  • 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. I cannot agree more that's the much needed sweet spot/Goldilock/etc. Personally I have been advocating this approach for some times. Apparently the language is already widely available and currently has stable and wide compiler support including the venerable GNU compiler suite (GDC). It... - Source: Hacker News / over 1 year ago
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Google BigQuery mentions (47)

  • Ruby on Rails Performance: 7 Lessons from Scaling FirstPromoter
    We migrated the analytics layer to Google BigQuery. Same queries that timed out in PostgreSQL now run in under 2 seconds. But not everything belongs in BigQuery โ€” we initially moved too aggressively and actually reverted some queries back when the added complexity wasn't justified. Our rule of thumb: if a query scans hundreds of thousands of rows or involves complex time-series aggregations, BigQuery. Everything... - Source: dev.to / 4 months ago
  • How to Analyze 47 Million Hacker News Posts: A Data Scientist's Dream Dataset Just Got Better
    Google BigQuery - For large-scale data processing and SQL-based analysis. - Source: dev.to / 5 months ago
  • What if ML pipelines had a lock file?
    Data Pipelines usually read from tables that change over time. Most of these tables are stored in a data warehouse like Amazon Redshift or Google BigQuery. Rows are added or removed. Backfills happen. A column gets renamed or its meaning changes. Even when teams snapshot data, those snapshots are often implicit, not recorded as part of the pipeline run itself. - Source: dev.to / 6 months ago
  • Best SQL Courses with Certificates for 2026
    SQL endures because it's the non-negotiable interface for relational data. Enterprise data storage still relies heavily on relational databases despite new alternatives. What makes SQL valuable for learners is transferabilityโ€”while dialects differ across PostgreSQL, SQL Server, and BigQuery, the fundamentals stay consistent. - Source: dev.to / 8 months ago
  • Why Your Snowflake Bill is High and How to Fix It with a Hybrid Approach
    Within classic cloud data warehouses, Google BigQuery presents a different pricing model. Its on-demand, per-terabyte-scanned pricing can be cost-effective for sporadic forensic queries. But it carries the risk of a runaway query where a single mistake leads to a massive bill. - Source: dev.to / 9 months ago
View more

What are some alternatives?

When comparing D (Programming Language) and Google BigQuery, you can also consider the following products

C++ - Has imperative, object-oriented and generic programming features, while also providing the facilities for low level memory manipulation

Databricks - Databricks provides a Unified Analytics Platform that accelerates innovation by unifying data science, engineering and business.โ€ŽWhat 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 experiencesโ€”so everyone in the business can explore the data that matters to them, in the context that makes it truly meaningful.

V (programming language) - Simple, fast, safe, compiled language for developing maintainable software.

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.

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