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VS Code VS Apache Parquet

Compare VS Code VS Apache Parquet and see what are their differences

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

VS Code logo VS Code

Build and debug modern web and cloud applications, by Microsoft

Apache Parquet logo Apache Parquet

Apache Parquet is a columnar storage format available to any project in the Hadoop ecosystem.
  • VS Code Landing page
    Landing page //
    2024-10-09
  • Apache Parquet Landing page
    Landing page //
    2022-06-17

VS Code features and specs

  • Cross-platform
    VS Code works on Windows, macOS, and Linux, providing a consistent development experience across different operating systems.
  • Extensibility
    A vast library of extensions allows users to add functionalities like debuggers, linters, and themes, making it highly customizable.
  • Integrated Git
    Built-in Git integration makes it easy to manage version control tasks directly within the editor.
  • Performance
    Lightweight compared to full-fledged IDEs, ensuring good performance even on systems with limited resources.
  • IntelliSense
    Advanced code completion and refactoring tools help improve coding efficiency and reduce errors.
  • Community Support
    A strong and active community provides extensive support, tutorials, and third-party extensions.
  • Debugging
    Robust debugging tools for various languages and frameworks are available out of the box.
  • Free and Open-Source
    VS Code is completely free to use and open-source, which is beneficial for both individual developers and organizations.

Possible disadvantages of VS Code

  • Limited IDE Features
    While extensible, it may lack some advanced features found in dedicated IDEs out of the box.
  • Extension Management
    Managing and configuring a large number of extensions can become cumbersome and sometimes lead to performance issues.
  • Learning Curve
    Although user-friendly, it has a steeper learning curve for beginners due to its numerous features and customization options.
  • Memory Usage
    Despite being lightweight, it can consume a significant amount of memory when multiple extensions are installed.
  • Update Frequency
    Frequent updates may sometimes introduce bugs or require users to adapt to new changes quickly.
  • Internet Dependency
    Some features and extensions may require an internet connection to function optimally.
  • Telemetry
    By default, VS Code collects usage data, which might be a concern for users sensitive about data privacy. However, this can be disabled.

Apache Parquet features and specs

  • Columnar Storage
    Apache Parquet uses columnar storage, which allows for efficient retrieval of only the data you need, reducing I/O and improving query performance on large datasets.
  • Compression
    Parquet files support efficient compression and encoding schemes, resulting in significant storage savings and less data to transfer over the network.
  • Compatibility
    It is compatible with the Hadoop ecosystem, including tools like Apache Spark, Hive, and Impala, making it versatile for big data processing.
  • Schema Evolution
    Parquet supports schema evolution, allowing changes to the schema without breaking existing data, which helps in maintaining long-lived data pipelines.
  • Efficient Read Performance for Aggregations
    Due to its columnar layout, Parquet is highly efficient for processing queries that aggregate data across columns, such as SUM and AVERAGE.

Possible disadvantages of Apache Parquet

  • Write Performance
    Writing data to Parquet can be slower compared to row-based formats, particularly for small inserts or updates, due to the overhead of encoding and compression.
  • Complexity in File Management
    Managing and partitioning Parquet files to optimize performance can become complex, particularly as datasets grow in size and complexity.
  • Not Ideal for All Workloads
    Workloads that require frequent row-level updates or involve small queries might be less efficient with Parquet due to its columnar nature.
  • Learning Curve
    The need to understand the nuances of columnar storage, encoding, and compression can pose a learning curve for teams new to Parquet.

Analysis of VS Code

Overall verdict

  • Yes, VS Code is generally considered a good choice for developers due to its flexibility, efficiency, and strong community support. It is lightweight, fast, and user-friendly, catering to both novice and experienced developers.

Why this product is good

  • VS Code, developed by Microsoft, is a widely popular and versatile code editor. It offers a robust extension ecosystem, which allows developers to customize their workflow and coding environment extensively. Additionally, VS Code supports numerous programming languages right out of the box and provides features like IntelliSense, debugging, Git integration, and a built-in terminal, making it a powerful tool for developers.

Recommended for

  • Web developers looking for a comprehensive yet lightweight coding environment.
  • Software developers who need an editor with extensive language support and customization options.
  • Beginner programmers who would benefit from a feature-rich editor that can grow with their skills.
  • Developers interested in an open-source tool with continuous updates and community-driven enhancements.

VS Code videos

+ Add

My New Favorite Text Editor - Visual Studio Code

More videos:

  • Review - 7 reasons why I switched to Visual Studio Code from Sublime Text

Apache Parquet videos

No Apache Parquet videos yet. You could help us improve this page by suggesting one.

Add video

Category Popularity

0-100% (relative to VS Code and Apache Parquet)

VS Code

Apache Parquet

Text Editors
100 100%
Text Editors
0% 0
Databases
0 0%
Databases
100% 100
IDE
100 100%
IDE
0% 0
Big Data
0 0%
Big Data
100% 100

User comments

Share your experience with using VS Code and Apache Parquet. 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 VS Code and Apache Parquet

VS Code Reviews

  1. dksinden
    ยท Working at SpeechKit ยท
Boost Your Productivity with These Top Text Editors and IDEs
Visual Studio Code, commonly known as VS Code, is a powerful and extensible code editor developed by Microsoft. With its rich ecosystem of extensions and features like IntelliSense, debugging, and Git integration, VS Code enhances your coding productivity.
Source: convesio.com
13 Best Text Editors to Speed up Your Workflow
Finally, the Visual Studio Code website has numerous tabs for you to learn about the software. The documentation page walks you through steps like the setup and working with different languages. Youโ€™re also able to check out some tips and tricks and learn all of the Visual Studio Code keyboard shortcuts. Along with a blog, updates page, extensions library and API...
Source: kinsta.com
Jupyter Notebook & 10 Alternatives: Data Notebook Review [2023]
Previously, VS Code was more suited to developers or engineers due to its lack of data analysis capabilities, but since 2020, the VS Code team has collaborated with the Jupyter team to create an integrated notebook within VS Code. The end result is a fantastic IDE workbook for data analysis.
Source: lakefs.io
The Best IDEs for Java Development: A Comparative Analysis
Overview: Although not a traditional IDE, VS Code has gained popularity as a lightweight code editor.
Source: dev.to
20 Best Diff Tools to Compare File Contents on Linux
Visual studio code is a code editor made by Microsoft. It supports several development operations like debugging, task running, and version control. It works on Linux, macOS and Windows operating systems.
Source: linuxopsys.com

Apache Parquet Reviews

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

Social recommendations and mentions

Based on our record, VS Code seems to be a lot more popular than Apache Parquet. While we know about 1215 links to VS Code, we've tracked only 31 mentions of Apache Parquet. 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.

VS Code mentions (1215)

  • History of JavaScript: Browser wars, ECMAScript, Node.js, TypeScript, and React
    Visual Studio Code, a code editor created by Microsoft, was first introduced on April 29, 2015, at the Build conference. - Source: dev.to / 1 day ago
  • How to Get Your First Tool Online
    The step up from there is an editor with a built-in agent like Cursor, Google Antigravity, Windsurf, or VS Code with a coding extension. These are code editors with an AI agent living inside them, and the difference is the responsible party for getting things from place to place. Instead of the software creator shuttling code between windows, the AI agent edits the project files directly and runs the GitHub and... - Source: dev.to / 16 days ago
  • Agentic Engineering: What Does AI Coding Really Cost?
    For IDE-heavy teams, BYOK (bring your own key) can be interesting, no matter whether you live in WebStorm or VS Code. On the JetBrains side, the JetBrains AI plans and Junie BYOK docs allow it, and most VS Code AI extensions offer the same idea: keep the IDE, connect provider keys, pay the provider. - Source: dev.to / about 1 month ago
  • Best Markdown Editors for Developers
    Option 1: Raw editing in IDE. You open the .md file in VS Code or whatever you use. Syntax highlighting shows you the structure. Maybe you toggle a preview pane. This works for quick edits but becomes painful for anything involving tables, diagrams, or complex formatting. - Source: dev.to / about 1 month ago
  • Document Generation for Developers: Security, Compliance, and Build-vs-Buy Decisions for the Template-Plus-Data Pipeline
    You'll need Python 3.8+ and pip for the quickstart, with venv recommended for isolation. Install the requests library for HTTP calls. VS Code with the Python extension works well as an editor, though PyCharm or Sublime Text work equally well. You'll also need a free Foxit developer account. - Source: dev.to / about 1 month ago
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Apache Parquet mentions (31)

  • Can you build observability ingestion on S3 alone โ€” no Kafka, no disks, no coordination layer?
    Apache Iceberg fits these requirements well. Iceberg stores data as immutable Apache Parquet files and adds them through atomic commits, so readers always see a consistent snapshot. A separate metadata layer prunes files by their statistics before the data itself is ever read, and those statistics can be extended to match an observability filtering profile. - Source: dev.to / 10 days ago
  • Zeroserve: A zero-config web server you can script with eBPF
    Depends on the domain. There's a bunch of sciences using large datasets served up efficiently using static file formats, e.g., https://zarr.dev/ and https://parquet.apache.org/. - Source: Hacker News / about 1 month ago
  • What Are Table Formats and Why Were They Needed?
    The data files themselves are still standard Parquet or ORC. The table format adds a metadata layer on top that gives those files the properties of a database table. - Source: dev.to / 2 months ago
  • So, you know what? I just wasted 3 months of my life
    The dataset is huge - in parquet conversion - it is total 9gb. And in raw PNG image nested folders - it is 67 gigabytes. Huge... - Source: dev.to / 4 months ago
  • Fix Slow Query: A Developer's Guide to Data Warehouse Performance
    The solution is to standardize on columnar formats like Apache Parquet. Parquet stores data in columns, not rows, which immediately enables column pruning. If a query is SELECT avg(price) FROM sales, the engine reads only the price column and ignores all others. This can reduce storage footprints by up to 75% compared to raw formats and is a cornerstone of modern analytics performance. - Source: dev.to / 8 months ago
View more

What are some alternatives?

When comparing VS Code and Apache Parquet, you can also consider the following products

Sublime Text - Sublime Text is a sophisticated text editor for code, html and prose - any kind of text file. You'll love the slick user interface and extraordinary features. Fully customizable with macros, and syntax highlighting for most major languages.

Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.

Vim - Highly configurable text editor built to enable efficient text editing

Apache Arrow - Apache Arrow is a cross-language development platform for in-memory data.

Node.js - Node.js is a platform built on Chrome's JavaScript runtime for easily building fast, scalable network applications

Amazon S3 - Amazon S3 is an object storage where users can store data from their business on a safe, cloud-based platform. Amazon S3 operates in 54 availability zones within 18 graphic regions and 1 local region.

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