Introducing Datalore Enterprise 2023.2

The raw data of Developer Ecosystem Survey 2022 is now available for public access. You can now access the answers of 29,269 developers and conduct your own analysis. The raw data is accessible in two formats: csv file and a Jupyter notebook in Datalore with examples of analysis.

Reading data from a MySQL database to a pandas dataframe can be intimidating. Establishing a connection, keeping the credentials safe, creating an SQL query within a string variable, and saving the result to pandas is not a trivial task.

Since January 2023, we have rolled out a number of new features for our Community and Professional users. The list includes the long-awaited Datalore Run API, a more robust reactive kernel, a whole array of Powerful CPUs and GPUs, Datalore credits for Professional users, and a number of performance improvements. 

Working with financial data is not a trivial task, as you can’t just access a production database or a data lake, download the data, and work on it. You have to ensure secure access to the data and produce insights that are easy to share as well.

The last major release of Datalore rolled out no fewer than 11 big features, so in this release cycle we focused our efforts on making sure our development is healthy and fast-paced.

Learn how to install Git repositories, edit the content of these repositories, and version your work with Datalore. 

Today we are introducing 8 new powerful CPU and GPU machines to Datalore Professional and a flexible new way to spend your computation quota – Datalore credits!

Datalore Enterprise 2022.3.1 provides more robust management of computing resources to prevent potential conflicts between scheduled notebook runs and running cells in interactive notebook sessions. This is an update to version 2022.3, which we released in November.

In our first newsletter of the new year, we’ve brought together 5 inspiring pieces of content that will help you plan your data science career for 2023.

Learn about the difference between the typical use cases of R and Python for data science. Discover the tasks most suited to the languages, as well as how to use them in Datalore.

Discover the similarities and differences between R and Python. Programming styles, data visualization and libraries for each programming language are covered.

Learn about data preparation for machine learning and analysis, and avoid some of the most common problems real-world data can throw at you.