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Thinknum VS socketify.py

Compare Thinknum VS socketify.py and see what are their differences

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

Thinknum logo Thinknum

Making it easy to track and model corporate financial data

socketify.py logo socketify.py

Maybe the fastest web framework for Python and PyPy
  • Thinknum Landing page
    Landing page //
    2023-09-25
  • socketify.py Landing page
    Landing page //
    2023-09-24

Thinknum features and specs

  • Alternative Data
    Thinknum provides a wide range of alternative data collected from various online sources, which can offer unique insights into market trends and business performance that traditional data sources might not capture.
  • User-Friendly Interface
    Thinknum's platform is designed with a user-friendly interface that allows users to easily navigate and extract meaningful data without requiring deep technical expertise.
  • Variety of Use Cases
    The platform caters to a variety of industries including finance, retail, and more, allowing users from different sectors to benefit from its data analytics capabilities.
  • Real-Time Data Updates
    Thinknum offers real-time data updates, providing users with the most current information to make timely and informed decisions in fast-moving markets.
  • Customizable Dashboards
    Users can create customizable dashboards that enable them to focus on specific datasets and metrics of interest, enhancing the overall user experience and productivity.

Possible disadvantages of Thinknum

  • Pricing
    Thinknum can be costly, particularly for smaller businesses or individual users, which might limit accessibility for those with limited budgets.
  • Data Accuracy
    As with any alternative data source, there can be concerns about the accuracy and reliability of the data collected, which may impact decision-making.
  • Learning Curve
    Despite a user-friendly interface, some users might face a learning curve when first using the platform, particularly those unfamiliar with data analytics tools.
  • Limited Historical Data
    For certain datasets, there may be limited historical data available, which could affect long-term trend analysis and modeling.
  • Dependence on Online Sources
    Since Thinknum relies heavily on data from online sources, any shifts in data availability, website changes, or access restrictions could impact the platform's effectiveness.

socketify.py features and specs

  • High Performance
    Socketify.py is designed for high scalability and performance, leveraging an efficient event loop and native extensions to handle a large number of concurrent connections efficiently.
  • WebSocket Support
    The library provides built-in support for WebSockets, making it suitable for real-time applications where persistent connections between client and server are necessary.
  • Asynchronous I/O
    Socketify.py is built on top of asynchronous I/O paradigms, allowing non-blocking operations that can improve the throughput of networked applications.
  • Ease of Use
    The library offers a clean and straightforward API with examples and documentation, which lowers the barrier to entry for developers who are new to network programming in Python.
  • Python Integration
    Being a Python library, socketify.py integrates well with existing Python applications and can be included as part of larger, multi-component systems.

Possible disadvantages of socketify.py

  • Limited Adoption
    As a relatively new or niche library, socketify.py might have a smaller user base and community compared to more established frameworks like Flask or Django, which could result in fewer community resources and third-party integrations.
  • Learning Curve
    For developers who are accustomed to synchronous programming paradigms, adapting to the asynchronous programming model of socketify.py may require an initial learning investment.
  • Documentation Depth
    While there is documentation, it might not be as extensive or comprehensive as those of more mature libraries, potentially requiring more experimentation or source code reading to fully grasp advanced features.
  • Potential Stability Issues
    Being less established, there might be undiscovered bugs or stability issues in production environments compared to long-standing Python networking libraries.
  • Ecosystem Limitations
    The library might lack some of the extensive third-party plugins or tools available in more popular frameworks, which could limit its extensibility.

Analysis of socketify.py

Overall verdict

  • Socketify.py is a solid choice for developers seeking a high-performance web framework in Python, particularly for I/O-bound applications requiring speed comparable to frameworks in compiled languages, thanks to its use of uWebSockets under the hood.

Why this product is good

  • Built on uWebSockets, providing significant performance improvements over traditional Python web frameworks
  • Supports WebSockets natively, making it suitable for real-time applications
  • Lightweight and minimalistic design reduces overhead
  • Compatible with ASGI, allowing integration with existing Python async ecosystem
  • Active development and growing community support on GitHub
  • Good for building high-throughput APIs and services

Recommended for

  • Developers building real-time applications like chat apps or live notifications
  • Projects requiring high concurrency and low latency in Python
  • Teams looking to replace slower WSGI-based frameworks with something faster
  • Applications needing WebSocket support without heavy framework overhead
  • Microservices architectures where performance is critical
  • Python developers wanting an alternative to Node.js for performance-sensitive tasks

Thinknum videos

+ Add

Thinknum Webinar - Question Driven Data Science: Better Answers from Alternative Data

More videos:

  • Review - Outsmarting Competitors Using Alternative Data with Justin Zhen Co-Founder, Thinknum
  • Review - Alternative Data News Episode 2 - Thinknum Alternative Data

socketify.py videos

No socketify.py videos yet. You could help us improve this page by suggesting one.

Add video

Category Popularity

0-100% (relative to Thinknum and socketify.py)

Thinknum

socketify.py

Fintech
100 100%
Fintech
0% 0
Python
0 0%
Python
100% 100
APIs
100 100%
APIs
0% 0
Web Development
0 0%
Web Development
100% 100

User comments

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Social recommendations and mentions

Based on our record, socketify.py seems to be more popular. It has been mentiond 2 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.

Thinknum mentions (0)

We have not tracked any mentions of Thinknum yet. Tracking of Thinknum recommendations started around Mar 2021.

socketify.py mentions (2)

  • Show HN: Python framework is faster than Golang Fiber
    These "benchmarks" are useless, they're not testing anything real world except the performance of uWebsockets. There are copy errors all over the place. And then an advertisement: https://github.com/cirospaciari/socketify.py#briefcase-comme... Is this a professional framework that produces proper, real-world benchmarks and... - Source: Hacker News / over 3 years ago
  • This is how I started the development of the fastest ASGI and WSGI Server in TechEmPower Benchmarks
    After starting the project called socketify.py at https://github.com/cirospaciari/socketify.py, I got pretty good results and reviews, but many people asked if socketify.py could be used to create a WSGI and ASGI server. WSGI and ASGI have a lot of overhead, that's is why I choose not to use them in the first place, but adding an ASGI and WSGI server allows a lot of code already written to run faster! Source: over 3 years ago

What are some alternatives?

When comparing Thinknum and socketify.py, you can also consider the following products

Wisesheets - Get stock financial data instantly on your spreadsheet

Flanks - API that aggregates all financial data on a single platform.

StockData.org - Free real-time, intraday and historical stock market, crypto and forex JSON APIs.

Quandl - Quandl is a platform for financial, economic, and alternative data, serving investment professionals.

Alpaca - Open links from Slack via desktop apps: Notion, Spotify, Zoom, Miro, Telegram, Airtable, Figma and VK.

Orbiter AI - Detect business issues without staring at a dashboard

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