Professional developer tools for data scientists and data engineers
DataSpell for Data Scientists
DataSpell integrates with Jupyter Notebook to combine the interactive nature of Jupyter Notebook with the benefits of the most intelligent Python IDE. In addition to the built-in Python coding assistance, you can also install a plugin that adds the R support.
DataSpell combines the full intelligence of its code editor with the collaborative and interactive capabilities of Jupyter notebooks. Work with local or remote Jupyter notebooks as you would do in a web-based Jupyter application, but with intelligent coding assistance and overall ergonomics the IDE provides to let you keep your focus on the code and the data.
DataSpell makes it easy for you to create and select the right environment — keep your dependencies isolated by having separate Conda environments per project.
Jupyter Notebook integration not only includes auto-completion, navigation and code analysis, but provides a full-featured graphical debugger with the ability to step into declarations.
DataSpell has built-in support for scientific libraries. It supports Pandas, Numpy, Matplotlib, and other scientific libraries, offering you best-in-class code intelligence, graphs, array viewers and much more.
Try DataSpell and make it a new home for your data science experimentsGet now
Big Data Tools
Big Data Tools is a plugin for IntelliJ IDEA Ultimate that is tailored to the needs of data engineers and data analysts. It makes exploratory data analysis and writing ETLs faster, easier and safer. Use it to run Spark jobs, manage Spark and Hadoop applications, edit Zeppelin notebooks, monitor Kafka clusters, and work with data across distributed storages.
Enjoy IntelliJ IDEA’s famous coding assistance for working with Zeppelin notebooks. The plugin offers smart navigation, code completion, inspections & quick-fixes, and refactorings inside the notebooks.
Manage and monitor your Spark and Hadoop applications, inspect your Spark jobs execution in an IDE tool window – just as you would do it using Spark History Server or Hadoop Web UI.
The plugin allows you to connect to remote file systems, such as HDFS, or S3, and conveniently work with the files. Browse buckets and folders, search for files, move, create, rename folders and files without leaving the IDE. The plugin supports Parquet and other columnar file formats.
While notebooks offer the original intelligent experience of the IDE editor, they seamlessly integrate a rich set of tools for working with paragraphs’ output, table data, and charts. You get the full functionality of Zeppelin notebooks embedded into the notebook editor. Browse table data, switch between various chart types, export data, and a lot more – without breaking your flow.
Install Big Data Tools to make IntelliJ IDEA Ultimate you get-go tool to run Spark jobs, manage Spark and Hadoop application, and to work with the data across distributed storagesGet now
SQL remains one of the most convenient and efficient ways to work with large data, be it a relational database or a data warehouse. Regardless of the type of data, as long as it offers the SQL interface, DataGrip offers the most ergonomic environment to run SQL queries and browse table data.
Allows you to execute queries in different modes and provides a local history that keeps track of all your activity and protects you from losing your work.
Lets you jump to any table, view, or procedure by its name via the corresponding action, or directly from its usages in the SQL code.
Add DataGrip to your toolset in order to master your SQL queries and run them efficiently and conveniently.Get now
Datalore for Data Science in the Cloud
Datalore is available to everyone online and as an on-premises installation to companies. In the cloud version you can use Datalore Community for free or choose Datalore Professional to get more powerful CPUs and GPUs and unlimited collaboration. The Enterprise version is a custom on-premises solution hosted on your servers.
Work with familiar Jupyter Notebooks online and enjoy smart coding assistance from the PyCharm IDE. Code autocompletion, quick-fixes, documentation popups, and refactoring will all help you work more productively with data. There is no notebook setup required.
In Datalore, you can upload datasets to notebooks, quickly explore and clean up data, create machine learning and deep learning models, create interactive visualizations, and share the results by publishing a static copy of the notebook.
A monthly subscription to Datalore Professional offers 20 hours of a GPU machine (1 NVIDIA T4 GPU, 16 GB GPU RAM, 4 vCPU cores) and 120 hours of a powerful CPU instance (16 GB RAM, 2 vCPU cores, 5x faster than the basic machine on the Community plan). We highly recommend subscribing to Datalore Professional if you are working with heavy datasets or train powerful models. For simple tasks use the Community plan.
In Datalore, you can organize team tasks in shared workspaces. Collaborate on notebooks in real-time and create history checkpoints for version management. You can publish a static copy of the notebook and share it via a link.
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