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Scikit-learn VS Apache Calcite

Compare Scikit-learn VS Apache Calcite and see what are their differences

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Scikit-learn logo Scikit-learn

scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

Apache Calcite logo Apache Calcite

Relational Databases
  • Scikit-learn Landing page
    Landing page //
    2022-05-06
  • Apache Calcite Landing page
    Landing page //
    2022-04-30

Scikit-learn features and specs

  • Ease of Use
    Scikit-learn provides a high-level interface for common machine learning algorithms, making it easy for beginners and professionals to implement complex models with minimal coding.
  • Extensive Documentation and Community Support
    The library has comprehensive documentation and a large, active community. This makes it easy to find tutorials, examples, and solutions to common problems.
  • Integration with Other Libraries
    Scikit-learn integrates well with other scientific computing libraries such as NumPy, SciPy, and pandas, allowing for seamless data manipulation and analysis.
  • Variety of Algorithms
    It offers a wide array of machine learning algorithms for tasks such as classification, regression, clustering, and dimensionality reduction.
  • Performance
    Designed with performance in mind, many of the algorithms are optimized and some even support multicore processing.

Possible disadvantages of Scikit-learn

  • Limited Deep Learning Support
    Scikit-learn is primarily focused on traditional machine learning algorithms and does not offer support for deep learning models, unlike libraries like TensorFlow or PyTorch.
  • Not Ideal for Large-Scale Data
    While Scikit-learn performs well for moderate-sized datasets, it may not be the best choice for extremely large datasets or big data applications.
  • Lack of Online Learning Algorithms
    The library has limited support for online learning algorithms, which are useful for scenarios where data arrives in a stream and model needs to be updated incrementally.
  • Less Flexibility in Customization
    It can be less flexible compared to lower-level libraries when highly customized or specific implementations are needed.
  • Dependency Overhead
    Scikit-learn relies on several other Python libraries like NumPy and SciPy, which might require users to manage multiple dependencies.

Apache Calcite features and specs

  • Query Optimization
    Calcite provides advanced query planning and optimization features, allowing for efficient execution of SQL queries across different data sources.
  • Extensibility
    The framework is highly extensible, allowing users to add custom rules and support for additional languages and data stores.
  • Support for Multiple Data Sources
    Apache Calcite can integrate with a wide range of data sources, providing a unified query interface where users can query from different systems using standard SQL.
  • Community and Open Source
    As part of the Apache Software Foundation, Calcite benefits from a vibrant open-source community that continuously improves and expands its capabilities.

Possible disadvantages of Apache Calcite

  • Complexity
    The system can be complex to set up and configure, especially for users who are not familiar with query processing infrastructure.
  • Limited Direct Data Storage
    Calcite itself does not store or manage data; it acts as an intermediary layer, which may limit its use for those looking for a standalone database solution.
  • Learning Curve
    The rich set of features and customizations can lead to a steep learning curve, requiring users to invest time to fully understand and utilize its capabilities.
  • Performance Overhead
    Given that Calcite introduces an additional layer between the application and data storage, there might be performance overheads in certain use cases.

Analysis of Scikit-learn

Overall verdict

  • Yes, Scikit-learn is generally regarded as a good library for machine learning, especially for beginners and intermediate users who need reliable tools with efficient implementation of numerous algorithms.

Why this product is good

  • Scikit-learn is considered a good machine learning library because it provides a wide range of state-of-the-art algorithms for supervised and unsupervised learning. It is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy. The library is well-documented, easy to use, and has a consistent API that simplifies the integration of different algorithms. Furthermore, there's a strong community and continuous development, which means it is well-maintained and updated regularly with new features and improvements.

Recommended for

  • Beginners learning machine learning concepts and application.
  • Data scientists and engineers looking for a robust and efficient toolkit to build and deploy machine learning models.
  • Researchers who need an easy-to-use library that facilitates the experimentation of various algorithms.
  • Developers who require a seamless, Python-based machine learning library that integrates well with other data analysis tools and environments.

Scikit-learn videos

+ Add

Learning Scikit-Learn (AI Adventures)

More videos:

  • Review - Python Machine Learning Review | Learn python for machine learning. Learn Scikit-learn.

Apache Calcite videos

+ Add

The Evolution of Apache Calcite and its Community - A Discussion with Julian Hyde

More videos:

  • Review - Building modern SQL query optimizers with Apache Calcite - Vladimir Ozerov

Category Popularity

0-100% (relative to Scikit-learn and Apache Calcite)

Scikit-learn

Apache Calcite

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Databases
0 0%
Databases
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
Database Tools
0 0%
Database Tools
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 Scikit-learn and Apache Calcite

Scikit-learn Reviews

15 data science tools to consider using in 2021
Scikit-learn is an open source machine learning library for Python that's built on the SciPy and NumPy scientific computing libraries, plus Matplotlib for plotting data. It supports both supervised and unsupervised machine learning and includes numerous algorithms and models, called estimators in scikit-learn parlance. Additionally, it provides functionality for model...
Source: searchbusinessanalytics.techtarget.com

Apache Calcite Reviews

We have no reviews of Apache Calcite yet.
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Social recommendations and mentions

Based on our record, Scikit-learn should be more popular than Apache Calcite. It has been mentiond 31 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.

Scikit-learn mentions (31)

  • Must-Know 2025 Developer’s Roadmap and Key Programming Trends
    Python’s Growth in Data Work and AI: Python continues to lead because of its easy-to-read style and the huge number of libraries available for tasks from data work to artificial intelligence. Tools like TensorFlow and PyTorch make it a must-have. Whether you’re experienced or just starting, Python’s clear style makes it a good choice for diving into machine learning. Actionable Tip: If you’re new to Python,... - Source: dev.to / 4 months ago
  • 🚀 Launching a High-Performance DistilBERT-Based Sentiment Analysis Model for Steam Reviews 🎮🤖
    Scikit-learn (optional): Useful for additional training or evaluation tasks. - Source: dev.to / 6 months ago
  • Essential Deep Learning Checklist: Best Practices Unveiled
    How to Accomplish: Utilize data splitting tools in libraries like Scikit-learn to partition your dataset. Make sure the split mirrors the real-world distribution of your data to avoid biased evaluations. - Source: dev.to / 12 months ago
  • How to Build a Logistic Regression Model: A Spam-filter Tutorial
    Online Courses: Coursera: "Machine Learning" by Andrew Ng EdX: "Introduction to Machine Learning" by MIT Tutorials: Scikit-learn documentation: https://scikit-learn.org/ Kaggle Learn: https://www.kaggle.com/learn Books: "Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow" by Aurélien Géron "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman By... - Source: dev.to / over 1 year ago
  • Link Prediction With node2vec in Physics Collaboration Network
    Firstly, we need a connection to Memgraph so we can get edges, split them into two parts (train set and test set). For edge splitting, we will use scikit-learn. In order to make a connection towards Memgraph, we will use gqlalchemy. - Source: dev.to / almost 2 years ago
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Apache Calcite mentions (12)

  • Data diffs: Algorithms for explaining what changed in a dataset (2022)
    > Make diff work on more than just SQLite. Another way of doing this that I've been wanting to do for a while is to implement the DIFF operator in Apache Calcite[0]. Using Calcite, DIFF could be implemented as rewrite rules to generate the appropriate SQL to be directly executed against the database or the DIFF operator can be implemented outside of the database (which the original paper shows is more efficient).... - Source: Hacker News / almost 2 years ago
  • How to manipulate SQL string programmatically?
    Use a SQL Parser like sqlglot or Apache Calcite to compile user's query into an AST. Source: about 2 years ago
  • Parsing SQL
    One parser I think deserves a mention is the one from Apache Calcite[0]. Calcite does more than parsing, there are a number of users who pick up Calcite just for the parser. While the default parser attempts to adhere strictly to the SQL standard, of interest is also the Babel parser, which aims to be as permissive as possible in accepting different dialects of SQL. Disclaimer: I am on the PMC of Apache Calcite,... - Source: Hacker News / almost 3 years ago
  • Semantic Diff for SQL
    Apache Calcite can do this, though it's not a beginner-friendly task: https://calcite.apache.org/. - Source: Hacker News / almost 3 years ago
  • OctoSQL allows you to join data from different sources using SQL
    You should look at Apache Calcite[0]. Like OctoSQL, you can join data from different data sources. It's also relatively easy to add your own data sources ("adapters" in Calcite lingo) and rules to efficiently query those sources. Calcite already has adapters that do things like read from HTML tables over HTTP, files on your file system, running processes, etc. This is in addition to connecting to a bunch of... - Source: Hacker News / almost 3 years ago
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What are some alternatives?

When comparing Scikit-learn and Apache Calcite, you can also consider the following products

OpenCV - OpenCV is the world's biggest computer vision library

Presto DB - Distributed SQL Query Engine for Big Data (by Facebook)

Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.

SQLite - SQLite Home Page

NumPy - NumPy is the fundamental package for scientific computing with Python

Open Data Hub - OpenDataHub

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