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mlsql VS Kimono

Compare mlsql VS Kimono and see what are their differences

mlsql

Infer SQL queries from plain-text questions and table headers.

Rating
0 reviews
Alternatives Is it good?
Kimono

The clove shop is well-established drapery of the gate of the tiger. Other than fabrics for kimono sale, I meet various requests concerning the sum commencing with a dressing classroom, a kimono rental, recycling.

Rating
0 reviews
Alternatives Is it good?
JustDocz
Private document search for Windows. Find files by what’s inside them — locally, without uploading your documents. 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, mlsql should be more popular than Kimono. It has been mentioned 2 times since March 2021.

social mentions
2 vs 1
Web App popularity
100% vs 0%
alternatives listed
1 vs 101

Base details

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

mlsql
Kimono
Website github.com kimonolabs.com
Listed in
Education LMS Online Learning Web App +1
Web Scraping Data Extraction Data Web Crawling +1

Features and specs

What each product offers, as listed by its team.

mlsql 0 features
Kimono 5 features

No features have been listed yet.

  • Ease of Use
    Kimono provides a user-friendly interface that allows users to easily create APIs from web data without requiring advanced programming skills.
  • Speed
    The tool allows quick extraction of web data, enabling users to generate APIs in a matter of minutes.
  • No Coding Required
    Users can generate APIs without writing any code, making it accessible to non-developers and reducing development time.
  • Cloud-Based
    Since Kimono is a cloud-based service, it eliminates the need for local installations or maintenance of software.
  • Data Scheduling
    Kimono offers data scheduling features, which allow users to set up automatic data extraction at regular intervals.

Possible disadvantages

  • Service Discontinuation
    Kimono Labs was acquired by Palantir and has ceased operations, making the service unavailable for new users.
  • Scalability
    As a no-code solution, Kimono may not be suitable for large scale or highly specialized data scraping needs which may require more advanced customization.
  • Limited Data Structure
    The tool may struggle with complex or deeply nested data structures, limiting its usefulness for certain web data extraction tasks.
  • Dependency
    Reliance on a third-party service for critical data extraction tasks can be a risk if the service experiences downtime or other issues.
  • Security Concerns
    Sending web data to an external service raises potential security and privacy concerns, particularly when dealing with sensitive information.

Analysis

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

mlsql
Kimono

Overall verdict

  • MLSQL is a solid open-source unified platform that blends SQL with machine learning, making data engineering and ML workflows accessible through a single, declarative language. It's a good choice for teams looking to streamline big data and ML pipelines without switching between multiple tools.

Why this product is good

  • It unifies data processing and machine learning under a single SQL-like syntax, lowering the learning curve for data teams.
  • Built on top of Apache Spark, it leverages a proven distributed computing engine for handling large-scale data.
  • Open-source and actively developed, allowing customization and community-driven improvements.
  • Supports the full ML lifecycle including data preprocessing, training, and deployment within one workflow.
  • Reduces the need to context-switch between separate ETL, analytics, and ML tools.

Recommended for

  • Data engineers and data scientists who prefer SQL-centric workflows
  • Teams working with big data on Apache Spark
  • Organizations wanting to unify ETL and machine learning pipelines
  • Companies seeking an open-source alternative to fragmented ML tooling
  • Analysts looking to build ML models without deep programming expertise

Overall verdict

  • Overall, Kimono Labs was considered a good choice for non-technical users and even some developers who needed a quick, reliable tool for web scraping tasks. However, it's important to note that Kimono Labs was acquired by Palantir in 2016 and subsequently shut down, so it's no longer available for use.

Why this product is good

  • Kimono Labs provided a web scraping tool that allowed users to automate data extraction from websites without any coding knowledge. It was appreciated for its ease of use, intuitive interface, and the ability to quickly create APIs from web data. This made it ideal for users who needed to pull data from the web regularly without engaging in complex programming tasks.

Recommended for

    Originally, Kimono Labs was recommended for journalists, researchers, marketers, and data enthusiasts who needed a straightforward way to gather and analyze web data without extensive technical skills. Professionals with a focus on data aggregation from multiple sources also found it beneficial before its discontinuation.

Videos

Walkthroughs and reviews on video.

mlsql 0 videos + Add
Kimono 3 videos + Add

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

What is the Japanese Kimono and How to Add it To Your Streetwear Fits

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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
mlsql
Kimono
100% 100%
Web App
0% 0%
0% 0%
Web Scraping
100% 100%
100% 100%
LMS
0% 0%
0% 0%
Data Extraction
100% 100%

User comments

Share your experience with using mlsql and Kimono. 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.

mlsql 2 mentions
Kimono 1 mention
  • Download the files from Docker Container and locally edit them
    I am working on a project that requires me to take user input (as English) and return SQL queries. We will eventually be working with multiple datasets, which is why the valuenet over at https://github.com/paulfitz/mlsql ended up being... Source: over 4 years ago
  • Clone and edit Docker Projects hosted on Github
    I found this great resource here: https://github.com/paulfitz/mlsql. Source: almost 5 years ago

Alternatives to mlsql and Kimono

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