10 Best Data Science Tools

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Data professionals rely on myriad tools and technologies to extract knowledge from the volumes of data commonplace in today’s organizations. To help make sense of that market, we compared the top 10 solutions data professionals use to uncover hidden patterns and make data-driven decisions and ranked them on how well they performed in five key categories: core features, enterprise features, integrations, support, and pricing.

Here are our top data science tool picks for 2023:

Top 10 Data Science Tools Comparison

Though data science has long since entered the business mainstream—and even more so with the rise of artificial intelligence and machine learning (AI/ML)—the discipline is mostly relegated to the enterprise. Most data science tools and solutions are priced accordingly, aside from a few open source and lower cost alternatives. Here’s a look at how the top 10 solutions scored on our rankings.

Core Features Enterprise Features Integrations Support Pricing
Databricks ☆☆☆☆☆ ☆☆☆☆☆ ☆☆☆☆☆ ☆☆☆☆☆ ☆☆☆☆
Snowflake ☆☆☆☆☆ ☆☆☆☆ ½ ☆☆☆☆ ½ ☆☆☆☆☆ ☆☆☆☆
Dataiku ☆☆☆☆☆ ☆☆☆☆ ½ ☆☆☆☆ ☆☆☆☆☆ ☆☆☆ ½
Cloudera ☆☆☆☆☆ ☆☆☆☆ ½ ☆☆☆☆☆ ☆☆☆☆☆ ☆☆☆ ½
RapidMiner ☆☆☆☆☆ ☆☆☆½ ☆☆☆ ☆☆☆☆ ½ ☆☆☆☆ ½
Matlab ☆☆ ½ ☆☆☆ ½ ☆☆☆☆ ☆☆☆ ½ ☆☆☆ ½
Alteryx ☆☆☆☆☆ ☆☆☆ ½ ☆☆☆☆ ½ ☆☆☆ ½ ☆☆☆ ½
Saturn Cloud ☆☆☆☆ ☆☆ ½ ☆☆ ½ ☆☆☆ ☆☆☆ ½
Azure Synapse ☆☆☆☆☆ ☆☆☆☆ ☆☆☆☆☆ ☆☆☆☆☆ ☆☆☆☆
Knime Analytics Platform ☆☆☆☆ ☆☆☆☆ ☆☆☆☆ ☆☆☆ ☆☆☆☆

Databricks icon.

Databricks Platform

Best Overall for Data Science, Documentation, and Learning 

Databricks is a renowned data analytics and machine learning platform that simplifies and accelerates data processing, analysis, and model development. Founded by the creators of Apache Spark, it offers a collaborative environment for data professionals to work together efficiently.

With its powerful data processing capabilities, interactive workspace, and automation features, Databricks has become an indispensable tool for enterprises looking to harness the full potential of their data, enabling them to make data-driven decisions and gain a competitive edge in today’s data-centric landscape.

The Databricks interface.
The Databricks interface. 

Pricing

  • Starts at $23,500 for 25,000 Databricks Compute Units (DCBU) on a one-year plan
  • 14-day free trial and free Community Edition available

Features

  • Unified data analytics lifecycle management in a single platform
  • Leverages Apache Spark for distributed data processing, enabling organizations to process massive datasets in real-time
  • Interactive workspace for writing and executing code in various programming languages
  • Automation and integrated AI features for building out data pipelines and harnessing advanced ML capabilities

Pros

  • Powerful data processing, analytics, and ML in one platform
  • Streamlined collaboration and a unified data toolset for managing multiple tools and platforms
  • A wealth of learning materials and documentation for boosting data science productivity and accelerating the development of data-driven solutions

Cons

  • Relatively expensive option, particularly for smaller businesses or organizations with limited budgets
  • The full range of features presents a significant learning curve
  • Significant vendor lock-in makes transitioning away from Databricks challenging

Snowflake icon.

Snowflake

Best for Cloud Data Warehousing

Snowflake developed a modern and highly regarded cloud-based data warehousing platform that has revolutionized the way organizations manage and analyze their data. Known for its elasticity and scalability, Snowflake’s data warehouse features a unique multi-cluster, shared data architecture that separates storage from compute functions, allowing users to independently and efficiently scale their data storage and processing needs.

Snowflake integrates with popular business intelligence (BI) tools and provides full SQL compatibility, making it ideal for enterprises looking to accelerate their data science endeavors while enjoying the benefits of a fully managed, cost-effective, and secure data warehousing solution in the cloud.

The Snowflake UI.
The Snowflake UI. 

Pricing

  • Based on storage (on-demand, $40 per terabyte per month), compute, cloud service costs
  • Free trial available

Features

  • Enterprise data warehousing offers the flexibility and scalability of the cloud
  • Robust data sharing capabilities for collaborating securely between internal teams and external partners
  • Zero-copy cloning enables users to create instant, cost-effective copies of data for development, testing, or analytical purposes
  • Built-in data processing functions for performing data transformations and analysis directly within the platform

Pros

  • Cloud-based architecture for unprecedented scalability, without the extensive infrastructure management
  • Strong data sharing and collaboration features make it easier to safely share data within and outside the organization for cross-functional/organizational insights and decision-making
  • Simplifies data management by automating many administrative tasks like maintenance, upgrades, and security

Cons

  • Usage-based pricing model can become costly for organizations with substantial data volumes and intensive workloads
  • High learning curve for data professionals new to cloud data warehousing concepts, SQL, and related technologies

Dataiku icon.

Dataiku DSS Data Science Platform

Best for Science and ML

Dataiku is a leConverged ading Data data science and machine learning platform known for its user-friendly interface and comprehensive suite of data tools. The platform enables data scientists, analysts, and business professionals to collaborate seamlessly in a unified environment.

From its data preparation and exploration features to ML model development and deployment capabilities,  Dataiku’s versatility and comprehensive functions make it an invaluable asset for organizations looking to extract insights and make better decisions through advanced data analytics.

The Dataiku interface.
The Dataiku interface. 

Pricing

  • $80,000 per year (in AWS Marketplace)
  • Free trial available

Features

  • End-to-end platform that covers the entire data lifecycle, from data preparation and integration to advanced ML development
  • Visual data wrangling and transformations for cleaning/preprocessing data without extensive coding
  • Collaboration features like shared workspaces and integration with popular team communication tools
  • Comprehensive suite of ML tools and packages offering a range of algorithms, model management, and automations

Pros

  • Intuitive, user-friendly interface caters to both data scientists and business users
  • Unified data management and a comprehensive set of tools for data preparation, integration, analysis, and ML
  • Highly scalable and integrates seamlessly with various data sources, databases, and cloud platforms

Cons

  • Relatively expensive licensing schema as well as infrastructure costs and requirements
  • Steep learning curve and advanced features may require training or deeper familiarity with data science concepts
  • Large-scale data processing and ML tasks can strain infrastructure resources and potentially lead to performance issues

Cloudera icon.

Cloudera Data Science Platform

Best for Scalability

Cloudera is a long-standing leader in enterprise data management and analytics platform space. Known for its commitment to open-source (and its pioneering role in the Hadoop ecosystem), Cloudera offers a data platform for efficiently storing, processing, and analyzing vast amounts of enterprise data.

For a number of years now, Cloudera’s data platform has been a go-to for enterprises looking for a secure, scalable solution to unlock their data assets. The company is now positioning its solution as a hybrid, multi-cloud data platform for building AI and predictive analytics.

Managing clusters in Cloudera.
Managing clusters in Cloudera. 

Pricing

  • From $0.04 per hour per concurrent user
  • 60-day free trial available

Features

  • Comprehensive suite of tools for data management, including data ingestion, storage, and processing
  • Scalable and distributed computing built on the foundations of Hadoop and Apache Spark
  • Advanced security and governance via robust authentication, authorization, and auditing features
  • A rich ecosystem of compatible tools and integrations that allow organizations to adapt to their specific data processing and analytics needs

Pros

  • Easily scales to large and complex data workloads due to its distributed computing capabilities
  • Allows for myriad customization options, enabling organizations to build highly specialized data solutions
  • Advanced security and compliance features for ensuring data integrity and adherence to regulatory requirements

Cons

  • Setup and configuration can be complex, requiring skilled administrators and in-depth knowledge of big data technologies
  • Licensing and infrastructure costs can be high and unsuitable for smaller organizations or startups
  • Involves a steep learning curve, particularly for new data science and big data users

RapidMiner icon.

RapidMiner

Best for Ease-of-Use

RapidMiner is a highly regarded solution in the data science and ML platform space. It simplifies the process of extracting valuable insights from complex data with its intuitive visual interface and offers a wide array of tools that empower data scientists and analysts to streamline data preparation, modeling, and deployment of predictive models.

The solution’s user-friendly design makes it accessible for both data science experts and newcomers, featuring drag-and-drop operators and a wide range of algorithms, extensions, and plugins.

The RapidMiner interface.
The RapidMiner interface. 

Pricing

  • $11.56 per hour (Azure Marketplace)
  • 30-day free trial available

Features

  • Easy-to-use interface for building data models and analytics through straightforward drag-and-drop process
  • Offers a vast library of pre-built templates and ML algorithms for integrating advanced analytics without extensive coding
  • Myriad data sources (e.g., databases, cloud platforms) enable users to work with diverse datasets and streamline their data analysis workflows

Pros

  • Easy to understand user interface enables non-technical stakeholders to engage with the platform’s data analysis tools
  • Pre-built templates and ML algorithms simplify the model-building process and speed up predictive analytics projects
  • Versatile deployment options make it suitable for a range of applications. from small-scale analysis to large-scale enterprise analytics use cases

Cons

  • Lacks the depth of customization available in more advanced data science platforms
  • Not a viable option for smaller organizations or startups with budget constraints
  • Despite its ease of use, RapidMiner’s more advanced features and capabilities require deeper data science expertise

MathWorks icon.

Mathworks Matlab

Best for Engineering and Scientific Analysis 

Short for “Matrix Laboratory,” MATLAB has been a favorite in the tool arsenal of scientists, engineers, and researchers across various disciplines for almost half a century. Renowned for its versatility and power, MATLAB excels in performing a multitude of tasks, from data analysis and signal processing to complex mathematical computations and the development of sophisticated simulations and models.

MATLAB’s flexibility and extensive support community have cemented its place with data professionals and academics seeking to tackle complex computational challenges efficiently.

The Matlab interface.
The Matlab interface. 

Pricing

  • Starts at $99 per month
  • Free trial available

Features

  • Custom, high-level programming language tailored for numerical and scientific computing, making it well-suited for a wide range of mathematical and engineering applications
  • Extensive ecosystem of toolboxes for various domains (signal processing, image processing, control systems, ML) enables users to access specialized functions and algorithms
  • Powerful data visualization tools for creating plots, graphs, and interactive visualizations essential for data analysis and scientific presentation
  • Seamless integration with other programming languages like Python, C/C++, and Java, allowing users to leverage its capabilities alongside existing tools and workflows

Pros

  • Extensive collection of specialized toolboxes offers a wealth of functions and algorithms for various scientific and engineering domains
  • High-level programming language simplifies complex mathematical and numerical computations, making it accessible to engineers, scientists, and researchers with limited coding experience
  • Extensive data visualization capabilities for creating insightful and interactive graphs and plots, aiding in data analysis and research presentation

Cons

  • Commercial licensing can be expensive, especially for individual users or small organizations
  • Limited collaboration options and code sharing abilities with open-source communities
  • Substantial learning curve that involves both proficiency with MATLAB’s high-level language and the advanced platform features and toolboxes for complex tasks

Alteryx icon.

Alteryx

Best for ML and Workflows

Alteryx helps organizations easily blend, cleanse, analyze, and visualize their data with its user-friendly, no-code drag-and-drop interface. This makes the platform accessible to both data analysts and business operations professionals, allowing users across the enterprise to transform raw data into actionable insights with ease.

Beyond data preparation, Alteryx offers advanced predictive and spatial analytics capabilities, allowing users to create complex workflows and models, as well as automate repetitive data-related tasks.

The Alteryx UI.
The Alteryx UI. 

Pricing

  • From $4,950
  • 30-day free trial available

Features

  • Advanced data blending and transformation tools for combining, cleaning, and preparing data from various sources without complex coding
  • Automated workflows for data analysis, processing, and reporting
  • Advanced analytics and predictive modeling capabilities, including data mining, statistical analysis, and ML
  • User-friendly, visual interface designed for both technical and non-technical users

Pros

  • Intuitive dashboards and visual workflow management features accelerate data-related tasks and foster collaboration
  • Strong data blending and workflow automations features help to reduce manual data-related work
  • Advanced analytics and predictive modeling capabilities

Cons

  • High price tag makes it out-of-reach for individual users and organizations with budget constraints
  • A limited open-source ecosystem limits its flexibility and integration options
  • Running complex workflows in Alteryx may require significant computing resources

Saturn Cloud icon.

Saturn Cloud

Best for This and That

Saturn Cloud is a cloud-based data science platform that provides a powerful and accessible environment for data scientists and analysts to develop and deploy data-driven solutions. The solution is primarily a suite of tools and resources for data processing, ML, and model deployment.

With an emphasis on scalability and reproducibility, Saturn Cloud supports both small-scale experiments and large-scale data projects.

The Saturn Cloud UI.
The Saturn Cloud UI. 

Pricing

  • From $0.10 per GB per month
  • Free trial available

Features

  • Cloud-based, managed data science platform capabilities through a centralized environment for data scientists and analysts—no infrastructure management required
  • Scalable data analysis for processing and analyzing large datasets (e.g., for ML and/or big data tasks) with cloud resources
  • Strong collaboration and version control features
  • Integrates with popular data science tools and libraries like Jupyter, Python, and Dask, allowing users to leverage existing knowledge and technical expertise

Pros

  • Managed cloud environment for data science and analytics that removes infrastructure management concerns
  • Scalability for processing and analyzing large datasets by harnessing cloud resources to handle resource-intensive tasks
  • Collaboration via version control and shared project environments, enhancing teamwork, and ensuring reproducibility in data science workflows

Cons

  • Metered cloud services can quickly add up
  • Saturn Cloud requires an internet connection; subsequently, users may experience limitations or disruptions in their work if they encounter connectivity issues or outages

Microsoft icon.

Azure Synapse Analytics

Best for Azure Ecosystem Functionality

Azure Synapse Analytics (formerly SQL Data Warehouse) is a comprehensive, powerful analytics service available via Microsoft Azure. The service integrates big data and data warehousing in a unified platform for data storage, processing, and analysis.

With its robust data integration capabilities, analytics, and AI features, Azure Synapse Analytics gives organizations a full range of options for efficiently exploring, visualizing, and sharing data-derived insights in a secure and compliant manner.

The Azure Synapse Analytics UI.
The Azure Synapse Analytics UI. 

Pricing

  • From $4,700 per 5,000 Synapse Commit Units (SCUs)
  • Free trial available

Features

  • Analysis of both structured and unstructured data in a single environment
  • Scalable computing and storage resources for handling massive datasets and complex analytics workloads
  • Seamless integrations with various Azure services, including data storage, ML, and data pipelines
  • Versatile tool for building end-to-end data workflows

Pros

  • Single platform for data warehousing and big data analytics, streamlining data workflows and reducing the need for multiple tools
  • High scalability allows organizations to adjust analytic capabilities and workloads by tweaking computing and storage resources on-the-fly
  • Integrations with Azure services provide a comprehensive ecosystem for building powerful data solutions

Cons

  • Implementation and configuration can be complex, requiring specialized skills and expertise
  • High scalability and feature-rich environment comes at high operational costs
  • Poses a significant learning curve for new Azure/cloud users

Knime icon.

Knime Analytics Platform

Best for Open Source Usage

KNIME Analytics is a leading open-source data analytics and ML platform renowned for its user-friendly, visual workflow design. With a diverse and extensive suite of tools and integrations, KNIME empowers data scientists and analysts to preprocess, analyze, and model data, facilitating the creation of robust data-driven solutions.

The platform’s modular and flexible nature allows users to customize workflows for their specific needs, and the platform supports unstructured and structured data from a wide range of data sources.

The Knime Analytics UI.
The Knime Analytics UI. 

Pricing

  • From $263 per month (Team plan) to $8,000 per month (Enterprise Advanced plan)
  • Free open-source version available

Features

  • Open-source platform includes a comprehensive set of tools for data analytics
  • Visual workflow designer offers a user-friendly, drag-and-drop interface for designing data analysis workflows
  • Extensive integrations with a variety of data sources, databases, and external tools, making it flexible and versatile for working with diverse data types and analytics processes

Pros

  • Open-source, cost-effective choice for powerful data analytics
  • Visual workflow design enables complex data analysis without extensive coding skills
  • Various data sources and external tool integrations for addressing different data analysis use cases

Cons

  • Mastering advanced features takes time, especially for new data analytics professionals
  • Performance may not be as robust as some commercial tools
  • Limited support and documentation compared to other data science tools

Key Features of Data Science Tools

A competent data science tool should provide features for extracting insights from data in the shortest amount of time possible. These include data import and manipulation capabilities—for example, allowing users to easily ingest and preprocess datasets—as well as visualization tools for exploring and communicating findings and discoveries, advanced analytics algorithms, and integrated ML capabilities for developing predictive and descriptive models.

How We Evaluated Data Science Tools

In comparing and contrasting these 10 leading data science tools, we scored each solution against a range of criteria and calculated an overall ranking.

Core Features | 20 percent

Data at-rest and in transit requires proper and specific handling and storage; a data science tool should therefore provide comprehensive data management features to meet these requirements. And with the prevalence of predictive analytics, data professionals will likely require data pipeline management and workflow creation tools to support their organizations’ ML data infrastructures. Rich data analytics and collaboration features for sharing insights are also key considerations for evaluating data science tools.

Enterprise Features | 15 percent

An enterprise data science tool requirement gaining prominence as of late is support for hybrid implementations—that is, data infrastructure/architectures that allow for local data storage—for example, a corporate data center—coupled with cloud-based compute and scaling services. Cloudera was arguably the first vendor to focus exclusively on this need; these days, the company positions its solution as a “hybrid data platform.” Other key enterprise features include strong data privacy and compliance controls and regular feature enhancements to support evolving enterprise data tooling needs.

Support | 15 percent

Data science tooling vendors should offer multiple channels for support, including live chat, phone, email, and other forms of self-service support (e.g., knowledgebase, support forums). Premium support is also a critical requirement for data science tools, as the main buyers in this category are usually enterprises with critical/urgent data needs.

Integrations | 20 percent

Data science tooling integration features should include plentiful developer resources and a fully-realized REST API, as well as libraries for common data transformations and algorithms, and other in-application, data-specific tools and utilities.

Pricing | 10 percent

Typically, data science tooling vendors will offer a free trial but limited upgrades/tiered pricing, opting for a metered (e.g., per hour, compute unit) pricing model instead. Because today’s data volumes and enterprise requirements necessitate cloud-enabled scalability and processing power, data science tooling vendors are increasingly moving to pricing models that align with the cloud.

Vendor Profile | 20 percent

Organizations looking to implement a data science tool should carefully consider the vendor’s market position and longevity; key metrics to consider include customer base size, and length of time in business, and overall business reputation and third-party risk profile (e.g., data breaches, privacy incidents, acquisitions).

Selecting the Right Data Science Tools

In evaluating data science tools, consider your organization’s unique needs, the scale and complexity of your data projects, and the expertise of your data science team.

Integrated data science platforms like Alteryx or Dataiku provide user-friendly interfaces and automation, while cloud-based solutions like Azure Synapse Analytics and Cloudera offer scalability and easy access to computational resources. Your selection should align with your data infrastructure, compliance requirements, and the skill sets of the people who will be working with the platform.

Frequently Asked Questions (FAQs)

What are data science tools?

Data science tools are software applications and platforms that enable data professionals to collect, process, analyze, and visualize data in order to derive meaningful insights and make informed decisions.

Why are data science tools critical for enterprise strategy?

Data science tools empower organizations to harness the full potential of their data, enabling evidence-based decision-making and innovation. These solutions provide the means to extract insights from vast datasets, optimize operations, identify trends, and discover new opportunities.

What features should be a priority when evaluating data science tools?

Key evaluation priorities should include usability and scalability for accommodating varying skill levels and data project sizes. Additionally, you should also evaluate features for data integration, collaboration, ML model deployment, and compliance.

Is data security a concern when using data science tools?

In today’s cyber threat landscape, controls for ensuring the protection of sensitive information and compliance with data privacy regulations is an operational imperative. A competent data science tool should provide robust security features, strong data encryption, access control, and auditing capabilities to mitigate potential risks.

Should I select an on-premises or cloud-based data science solution?

This depends on your organization’s specific needs—cloud-based solutions offer scalability, accessibility, and reduced infrastructure management, while on-premises solutions may be preferred for greater control and compliance, particularly in cases where data governance or regulatory requirements are a primary concern. Your decision should align with your data infrastructure, budget, and long-term strategic goals.

Bottom Line: Enterprise Data Science Tools

Data professionals have never had more tool options at their disposal for harnessing the power of data. These top 10 data science tools represent the current leaders in this space—whether you’re an aspiring data scientist, seasoned data analyst, or business professional/casual data wrangler, one or more of these offerings are likely to meet your organization’s data requirements and objectives.

Read Data Science Best Practices to learn how to implement the tools in this buyer’s guide most effectively.

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