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Fivetran VS Scikit-learn

Compare Fivetran VS Scikit-learn and see what are their differences

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Fivetran logo Fivetran

Fivetran offers companies a data connector for extracting data from many different cloud and database sources.

Scikit-learn logo Scikit-learn

scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
  • Fivetran Landing page
    Landing page //
    2023-09-19
  • Scikit-learn Landing page
    Landing page //
    2022-05-06

Fivetran

Website
fivetran.com
Pricing URL
Official Fivetran Pricing
$ Details
-
Release Date
2012 January
Startup details
Country
United States
State
California
City
Oakland
Founder(s)
George Fraser
Employees
250 - 499
Edit details

Scikit-learn

Website
scikit-learn.org
Pricing URL
-
$ Details
Release Date
-
Edit details

Fivetran features and specs

  • Automation
    Fivetran automates data integration, eliminating the need for manual coding and reducing maintenance overhead.
  • Scalability
    Fivetran can easily scale its services to handle growing data loads, making it suitable for businesses of various sizes.
  • Wide Range of Connectors
    It supports a broad array of data sources and destinations, allowing for diverse data pipelines.
  • Data Transformation
    Fivetran provides built-in data transformation capabilities, ensuring that data is in the correct format when it reaches the destination.
  • Real-Time Data Syncing
    Fivetran allows for near real-time data syncing, which is crucial for businesses that rely on up-to-date data for decision-making.
  • Reliability
    The service ensures data integrity and reliability, minimizing data loss during transfers.

Possible disadvantages of Fivetran

  • Cost
    Fivetran can be expensive, especially for small businesses or startups with limited budgets.
  • Limited Customization
    The platform offers limited options for customization, which might be a drawback for businesses with unique data integration needs.
  • Complex Setup for Non-Technical Users
    Despite its automation features, the initial setup can be complex for users without technical expertise.
  • Dependency on Third-Party Services
    Reliance on Fivetran means depending on a third party for crucial data integration tasks, which could be risky if the service faces downtime.
  • Data Latency for Some Sources
    While Fivetran supports near real-time syncing for many sources, some data sources might experience latency, affecting the freshness of the data.

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.

Analysis of Fivetran

Overall verdict

  • Fivetran is generally regarded as a good solution for businesses looking for an automated, reliable, and easy-to-use data integration tool. It is particularly beneficial for companies that wish to reduce time and effort spent on managing data pipelines and ensuring accurate data transfer.

Why this product is good

  • Fivetran is considered good due to its ability to automate data integration processes, providing a seamless and efficient way to connect various data sources to your data warehouse. It offers pre-built connectors, automated schema management, and reliable data syncing, which reduces the need for manual coding and maintenance. Its robust security measures and scalability also contribute to its positive reputation.

Recommended for

    Fivetran is recommended for small to large businesses that require efficient data integration from multiple sources into their data warehouse. It is ideal for organizations looking for a fully managed service to simplify their ETL/ELT processes, especially those using cloud-based data warehousing solutions such as Snowflake, BigQuery, or Redshift.

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.

Fivetran videos

+ Add

Cloud Data Warehouse Benchmark Redshift vs Snowflake vs BigQuery | Fivetran

More videos:

  • Review - Looker + Fivetran: Data Source to Dashboard in an Afternoon
  • Review - The Modern Data Stack: Fivetran + Looker + Snowflake

Scikit-learn videos

+ Add

Learning Scikit-Learn (AI Adventures)

More videos:

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

Category Popularity

0-100% (relative to Fivetran and Scikit-learn)

Fivetran

Scikit-learn

Data Integration
100 100%
Data Integration
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
ETL
100 100%
ETL
0% 0
Data Science Tools
0 0%
Data Science 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 Fivetran and Scikit-learn

Fivetran Reviews

Best ETL Tools: A Curated List
High costs: Fivetran’s pricing model, based on Monthly Active Rows (MAR), is one of the most expensive modern ELT vendors, often 5-10x the alternatives. Fivetran measures MARs based on its internal representation of data. Costs are especially high with connectors that need to download all source data each time or that have nonrelational data because Fivetran converts it into...
Source: estuary.dev
Top 11 Fivetran Alternatives for 2024
Fivetran's pricing is determined by monthly active rows (MAR), which can be unpredictable because of the way Fivetran internally represents data and manages non-relational sources. Additionally, reducing latency significantly increases costs. While a small deployment (2M MARs/month) can cost $700-$2667, 10M MARs/month get you into $10K a month. It is not unheard of for...
Source: estuary.dev
10 Best ETL Tools (October 2023)
It is a cloud-based ETL solution that supports data integration with data warehouses like Redshift, BigQuery, Azure, and Snowflake. One of the top selling points of Fivetran is its array of data sources, with nearly 90 possible SaaS sources and the ability to add custom integrations.
Source: www.unite.ai
15+ Best Cloud ETL Tools
Fivetran is a cloud-based automated ETL tool that simplifies the process of transporting data from various sources to a database or data warehouse. It offers an array of more than 200 connectors to help you to collect data seamlessly from multiple sources at the same time.
Source: estuary.dev
Top 14 ETL Tools for 2023
Overall, Fivetran is a great ETL solution for businesses looking to streamline their data integration process. The platform makes it easy for organizations of any size to move and transform data from multiple sources into an analytics-ready form quickly and cost-effectively. While there have been some issues reported with Fivetran’s customer service and pricing model, the...
Source: www.integrate.io

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

Social recommendations and mentions

Based on our record, Scikit-learn should be more popular than Fivetran. 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.

Fivetran mentions (12)

  • Sync Snowflake and Google Sheets
    Even looking past these limitations, internal scripts invariably require development and maintenance time, and as any developer knows, can break at the worst of times :) Method #2: Use Zapier? (https://zapier.com) Want to use Zapier to do this? You can’t. Not only because it doesn’t track deletes, updates to existing records, and only does one way syncs. But because Snowflake isn’t supported. Method #3:... - Source: Hacker News / over 2 years ago
  • Big problem with companies now is they hire data scientist for task that don't require data science practices.
    Disclaimer: I work for Fivetran, a data integration company. Source: almost 3 years ago
  • I love data science but hate data engineering
    Disclaimer: I'm a product evangelist for a data integration company called Fivetran, so I'm shamelessly shilling here. Source: almost 3 years ago
  • Which webflow theme is this?
    I really like the theme they’re using on https://fivetran.com. Source: about 3 years ago
  • A modern data stack for startups
    From experience then, believe me when I say you don't want to build these. Thankfully, ETL products like Fivetran and Stitch run and maintain these extraction processes for you. - Source: dev.to / about 3 years ago
View more

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 / about 1 year 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
View more

What are some alternatives?

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

Stitch - Consolidate your customer and product data in minutes

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

Hevo Data - Hevo Data is a no-code, bi-directional data pipeline platform specially built for modern ETL, ELT, and Reverse ETL Needs. Get near real-time data pipelines for reporting and analytics up and running in just a few minutes. Try Hevo for Free today!

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

Talend Data Integration - Talend offers open source middleware solutions that address big data integration, data management and application integration needs for businesses of all sizes.

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

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