Datalore

Datalore 2025.1 introduces a new Welcome screen, a redesigned admin panel for On-Premises installations, the ability to import workspaces from Git repositories, and more.

Datalore 2024.5 introduces SQLite support and an improved reporting experience that includes PDF export capabilities, new filters in interactive tables, and much more.

GreenJinn realized they lacked the right tools to allow their non-technical teams to manage data independently, which led to a heavy reliance on the tech team.

Datalore offers two different operational models: On-Premises and Cloud. In this post, we will consider a few advantages of each model and cover the most typical challenges organizations might face when adding a new tool to their daily portfolio.

In this credit risk analysis tutorial, we'll focus on the complex interplay between federal funds rates, 10-year Treasury yields, and corporate bond yields – key indicators that shape investment strategies, economic forecasts, and policy decisions.

Datalore 2024.4 introduces quality-of-life improvements for SQL cells, a revamped folder import workflow, support for Polars DataFrames, and more.

We've decided to revamp our subscription plans to simplify the process of finding the option that best fits your needs. You no longer have to worry about juggling between Team, Professional, and Community plans; we now present a unified and powerful solution for all cloud users: Datalore Cloud.

In this data research, we dive deep into the numbers, comparing the frenzied valuations of the dot-com era to today's AI-fueled market. By examining key financial metrics of tech giants then and now, we aim to uncover patterns, disparities, and potential warning signs that could shed light on the sustainability of current tech valuations.

In this article, we'll dive deep into the numbers behind some of the biggest layoffs in recent tech history. Our mission? To uncover whether these workforce reductions truly allowed these behemoths to become more profitable.

Despite the promising stats, data professionals are struggling to find jobs. AI is also shaking things up with capable LLMs, coding assistants, and loads of smart tools for data analytics.

Previously, neXDos prototyped, backtested, and future-proofed their trading algorithms by hosting JupyterLab and JupyterHub. But this approach was problematic due to complexity in many areas, including managing the server environment, carrying out frequent updates, and handling the associated plugins within JupyterLab.

In this post we’ll discuss how AI-driven data science can accelerate investment strategy implementation, enhance client relationships, and make investment management firms more agile.