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Streamlit VS Data Analytic System

Compare Streamlit VS Data Analytic System and see what are their differences

Streamlit

Turn python scripts into beautiful ML tools

Rating
0 reviews
Pricing
Open source
Alternatives Is it good?
Data Analytic System

Sourced crypto market data with indicator monitoring, market regime context and research. BTC, ETH, XRP, SOL + major indices monitored 24/7. Educational content — not investment advice.

Rating
0 reviews
Alternatives Is it good?
Gruvi.ai
Unified support inbox blending AI automations with genuine human empathy. Set up in 5 mins. Smarter than chatbots, our context-aware AI handles emails & chats with a human touch. sponsored
Note: These products don't have any matching categories. If you think this is a mistake, please edit the details of one of the products and suggest appropriate categories.

Which is more popular?

Based on our record, Streamlit seems to be more popular. It has been mentioned 222 times since March 2021.

social mentions
222 vs 0
Developer Tools popularity
100% vs 0%
alternatives listed
236 vs 6

Base details

Website, pricing, platforms and company facts side by side.

Streamlit
Data Analytic System
Website streamlit.io dataanalyticsystem.com
Pricing
Open source Official pricing
Official pricing
Platforms —
Web
Company — Startup from Hungary
Listed in
Developer Tools AI Machine Learning Cloud Computing +1
Finance Cryptocurrencies Data Analysis Data Dashboard Crypto Analysis Cryptocurrencies Dashboard +3

About Streamlit and Data Analytic System

In their own words, as submitted to SaaSHub.

Streamlit
Data Analytic System

No description of Streamlit yet.

Data Analytic System (DAS) is an educational crypto-market platform run by Data Analytic Investments Kft. (Budapest, EU). What it offers: - Live, sourced market-data dashboards for BTC, ETH, XRP, SOL and other major assets, plus equity indices, FX and commodities - A MiCA / CASP authorisation...

Read more about Data Analytic System

Features and specs

What each product offers, as listed by its team.

Streamlit 5 features
Data Analytic System 6 features
  • Ease of Use
    Streamlit's API is extremely intuitive and easy to learn, which makes it accessible for developers of varying experience levels. The simplicity allows for rapid development and less time spent on complex front-end coding.
  • Interactive Widgets
    It provides a set of interactive widgets that make it simple to add complex functionalities like sliders, buttons, and file uploaders to your application with minimal code.
  • Real-time Feedback
    Streamlit supports real-time data updates, allowing users to see changes instantly. This is particularly useful for data analysis and machine learning applications where live data visualization is crucial.
  • Integration with Machine Learning Libraries
    Streamlit integrates seamlessly with popular machine learning libraries like TensorFlow, PyTorch, and scikit-learn, making it a great tool for showcasing machine learning models and results.
  • Open Source
    Being an open-source project, Streamlit is free to use and comes with the support and contributions of an active community. This means continuous improvements and a wealth of shared resources.

Possible disadvantages

  • Limited Customization
    Streamlit offers limited customization options compared to traditional web frameworks. This can be a hindrance if you need a highly customized UI/UX for your application.
  • Performance Issues
    For more complex or resource-intensive applications, Streamlit may suffer from performance drawbacks. It is not designed for high-performance computing out of the box.
  • Scalability
    Streamlit is not well-suited for large-scale applications requiring major backend architecture or for scenarios demanding high scalability and concurrency.
  • Limited Widget Style Options
    The styling and customization options for widgets are somewhat limited, meaning your application's look and feel might be more constrained compared to using other front-end frameworks.
  • Deployment Complexity
    While Streamlit provides some deployment options, deploying Streamlit apps in a production environment can sometimes require additional effort and knowledge, especially for those unfamiliar with web deployment practices.
  • Live market data
    Crypto, equity indices, FX and commodities — every panel names its source and date
  • MiCA / CASP tracker
    EU exchange authorisation status based on ESMA registers
  • Market Observation Pro
    20 custom price levels, CSV and JSON export (free tier: same data, no delay)
  • Learning modules and glossary
    8 free modules, free glossary, Kripto Akademia (50 assets, 12 categories)
  • Documentary studies and e-books
    RIPPLE study with sourced claims; PDF + audiobook; EN, HU, ES
  • Languages
    Site in English and Hungarian; books in EN, HU, ES

Analysis

An editorial look at what each product does well and who it suits.

Streamlit
Data Analytic System

Overall verdict

  • Overall, Streamlit is well-regarded for its ease of use, speed of development, and ability to create clean and professional-looking applications without in-depth web development knowledge. It provides a seamless bridge between complex data analysis and user-friendly presentation, which can be highly beneficial for a wide range of use cases.

Why this product is good

  • Streamlit is a popular choice for quickly building and deploying data applications and interactive dashboards with minimal code. It is designed to be user-friendly, allowing data scientists and engineers to transform their scripts into shareable web apps. It supports real-time updates, is highly customizable, and integrates well with Python libraries like NumPy, Pandas, and Matplotlib, making it an attractive option for many developers working within the Python ecosystem.

Recommended for

    Streamlit is ideal for data scientists, analysts, and developers looking to rapidly prototype and deploy data-driven applications. It is recommended for those who prioritize simplicity, quick deployment, and seamless integration with Python code. Individuals or teams interested in building dashboards, ML model sharing platforms, or interactive reports will find Streamlit particularly useful.

No analysis of Data Analytic System yet.

Videos

Walkthroughs and reviews on video.

Streamlit 3 videos + Add
Data Analytic System 2 videos + Add

My thoughts on web frameworks in Python and R (PyWebIO vs Streamlit vs R Shiny)

More videos

  • - 1/4: What is Streamlit
  • - How to Build a Streamlit App (Beginner level Streamlit tutorial) - Part 1

The Analyst Room — Ep. 2: RIPPLE, the book. 22 chapters, 4 languages, PDF + audiobook

More videos

  • - The Analyst Room Podcast — Episode 1: One person, four AI systems, and how the site actually works

Category popularity

How often each product is chosen within a category, 0–100% relative to the other.

Score bands 0–20 21–40 41–50 51–60 61–100
Streamlit
Data Analytic System
100% 100%
Developer Tools
0% 0%
0% 0%
Cryptocurrencies
100% 100%
100% 100%
Productivity
0% 0%
0% 0%
Finance
100% 100%

Questions & Answers

As answered by people managing Streamlit and Data Analytic System.

Why should a person choose your product over its competitors?

Data Analytic System's answer:

Price trackers like CoinGecko or CoinMarketCap show numbers; Data Analytic System shows the numbers together with their source, date and context, and adds what trackers do not: an ESMA-based MiCA/CASP status list for EU exchanges, sourced documentary studies, a free glossary and learning modules, and multilingual e-books (EN, HU, ES). The free tier has no delay and the same data as the paid tier. We do not sell trading calls or recommendations of any kind.

What makes your product unique?

Data Analytic System's answer:

Every data panel and every article names its source and date. The platform is run by a small Budapest-based publisher (Data Analytic Investments Kft.) and combines live market data for crypto, equity indices, FX and commodities with an EU MiCA/CASP authorisation tracker, a free glossary and learning modules, and documentary studies such as the RIPPLE book, in which every factual claim carries a verifiable reference. Content is produced with a documented human+AI verification workflow (Uncle Sunny method): a human editor closes every item. Educational content only, not investment advice.

How would you describe the primary audience of your product?

Data Analytic System's answer:

People who want to understand crypto and macro markets rather than be told what to do: self-directed readers, students, journalists and compliance-minded professionals in the EU, plus Hungarian- and Spanish-speaking readers who lack sourced material in their language. Typical use: checking a data point with its source, reading the MiCA status of an exchange, or working through a learning module.

What's the story behind your product?

Data Analytic System's answer:

Data Analytic Investments Kft. was founded in Budapest in 2026 by Janos Szabo. The starting point was frustration with crypto content that mixes facts, opinion and promotion without references. The answer was a publishing method (Uncle Sunny) in which nothing is published before it is challenged and sourced, and a site built around that rule. The first flagship product was the RIPPLE documentary study (ISBN 978-615-83207 series, HU/EN/ES); the market-data dashboards, the MiCA tracker and the learning sections followed.

Which are the primary technologies used for building your product?

Data Analytic System's answer:

A TypeScript/React web application with server-side rendering, a MySQL database, and Stripe for payments. Market data comes from public exchange and data-provider APIs; regulatory data from ESMA registers. The editorial workflow uses several AI systems under human review, with every published claim tied to a named source.

User comments

Share your experience with using Streamlit and Data Analytic System. For example, how are they different and which one is better?

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Social recommendations and mentions

Recommendations tracked on public social media and blogs since March 2021.

Streamlit 222 mentions
Data Analytic System 0 mentions
  • Trail Sense("From Screen to soil")
    App: Streamlit and plain Python. Plant data lives in plants.json, and your saved garden in garden_progress.json. - Source: dev.to / 8 days ago
  • I Built an AI Gardening App That Tells Me to Put the Laptop Down
    The stack is small on purpose: Streamlit, one JSON file, and gemma3:1b running locally through Ollama. I picked the 1B Gemma model to find out whether this idea works on an ordinary laptop with no cloud account. On a laptop CPU an answer... - Source: dev.to / 9 days ago
  • How I Bypassed Reddit's Unauthenticated RSS Rate Limits (Without an API Key)
    I wrapped the logic in a small, single-process Streamlit app that pulls the newest posts from Hacker News, Reddit, and Lemmy into one sortable table. A few implementation details worth mentioning:. - Source: dev.to / 2 months ago

View more

Tracking Data Analytic System since Sep 2026.

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