Datalore: The Must-Know Data Science Platform for 2026

Data science is an exciting and rapidly evolving field that combines statistics, computer science, and domain expertise to extract valuable insights from data. As the volume and variety of data continues to grow exponentially, data scientists need powerful tools to efficiently analyze data, build models, and communicate insights.

While there are many great data science platforms available today, one that has been gaining significant traction lately is JetBrains‘ Datalore. Billed as an "intelligent web application for data analysis and visualization," Datalore provides a feature-rich, cloud-based environment for end-to-end data science workflows. Whether you‘re a seasoned data scientist or just getting started in the field, Datalore is definitely a platform you‘ll want to know about in 2023.

What Makes Datalore Special?

At its core, Datalore is a web-based notebook environment, similar to Jupyter Notebook or Google Colab. However, Datalore offers a number of unique features and advantages that set it apart:

Fully managed cloud infrastructure: With Datalore, there‘s no need to set up or maintain your own hardware or development environment. Everything runs in the cloud on JetBrains‘ managed infrastructure. This not only simplifies things from an IT perspective, but also enables real-time collaboration with colleagues (more on that later).

Intelligent coding assistance: Datalore provides smart coding assistance tailored for data science, including code completion, quick documentation, and intelligent refactoring. This allows you to write high-quality code faster and avoid common errors and pitfalls.

Reactive mode: Datalore‘s innovative "reactive mode" automatically re-runs cells and updates outputs as you type, providing instant visual feedback. This is a game changer for data exploration and iteration.

Polyglot environment: While Python is the lingua franca of data science, Datalore also supports R, Scala, and SQL, allowing you to use the best tool for the job. Libraries and packages are kept up-to-date and additional ones can be easily installed.

Integrated version control: Datalore integrates with Git out of the box, allowing you to track changes, create branches, and manage collaboration directly within the tool. This brings best practices from software engineering to data science.

Publishing and sharing: With the click of a button, Datalore allows you to publish your notebooks and share them with colleagues or the world. Viewers can leave comments enabling quick feedback cycles.

Datalore Tutorial

So what does working in Datalore actually look like? Let‘s walk through the key steps of getting started.

Step 1 – Create an Account: The first step is signing up for a Datalore account at datalore.io. Datalore offers both free and paid plans depending on your needs.

Step 2 – Create a Project: Once logged in, create a new project to organize your notebooks. You can invite team members to collaborate on the project.

Step 3 – Create a Notebook: Within your project, create a new notebook. This will open the notebook editor where you can start coding. Datalore supports both Python and R kernels.

Step 4 – Load Data: Click the "Attached Data" icon in the left sidebar to upload data files to your notebook. Datalore supports loading data from CSV, Excel, JSON, and database connections.

Step 5 – Explore Data: With your data loaded, you can start exploring it using Datalore‘s data viewer or by executing code in cells. Enable "Reactive Mode" to see your code and outputs update in real-time.

Step 6 – Analyze & Visualize: Use Datalore‘s intelligent code assistance to quickly analyze your data and create visualizations using popular libraries like Pandas, Matplotlib and Plotly.

Step 7 – Publish & Share: Once your analysis is complete, publish your notebook to share with others. They can view and comment on the notebook without having to log in to Datalore.

Datalore Use Cases

Datalore is incredibly versatile and can be used for a wide variety of data science tasks and use cases, such as:

Exploratory Data Analysis: Datalore is great for interactively exploring and visualizing datasets to uncover initial insights and inform next steps.

Machine Learning: Build, train, and evaluate machine learning models using popular frameworks like scikit-learn, TensorFlow, and PyTorch. Datalore‘s cloud GPUs enable rapid model training.

Dashboard Building: Use Datalore‘s built-in charting or packages like Dash and Streamlit to build interactive dashboards to track key metrics.

Report Generation: Combine code, outputs, and markdown in Datalore to generate polished, reproducible reports that can be shared with stakeholders.

ETL & Data Pipelines: Datalore can act as an orchestration layer for ETL and data pipelines. Ingest data, transform it, and pipe it to a destination, all within the same notebook.

Datalore for Teams & Enterprise

While Datalore is great for individual data scientists, it really shines in a team setting. Datalore‘s real-time collaboration features allow multiple users to work on the same notebook simultaneously, seeing each other‘s edits and cursor positions. Commenting and chat functionality fosters asynchronous collaboration and knowledge sharing.

For enterprise data science teams, Datalore offers features like single sign-on, role-based access control, and audit logging to support security and compliance needs. Centralized billing and license management simplifies procurement and vendor management.

Integration with other JetBrains tools like PyCharm and IntelliJ provides a seamless experience for developers and data engineers working alongside data scientists. Data scientists can also leverage Datalore‘s API to programmatically execute notebooks and integrate with CI/CD pipelines.

The Future of Datalore

Datalore has come a long way since its initial launch in 2018 and the JetBrains team continues to release regular updates and enhancements. Some exciting items on the roadmap include:

• Enhanced variable explorer for easier data debugging
• Side-by-side cell execution for comparing code and outputs
• Auto-complete for SQL and enhanced database integrations
• Pre-built project templates for common use cases
• Tighter integration with JetBrains IDEs

As the data science ecosystem continues to evolve, it‘s clear that JetBrains is fully committed to making Datalore the go-to platform for data scientists. If you haven‘t checked it out yet, there‘s no better time than now to take Datalore for a spin and experience the productivity boost firsthand. The future of data science is collaborative, interactive, and driven by powerful tools like Datalore.