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3 posts tagged with "visualization"

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· 4 min read
Pablo Fonovich

Resoto makes it easy to create an inventory of your cloud infrastructure. With just a few commands, you get a snapshot of your resources. And with that data ingested into a time series database, you not only know the current state of your cloud but also have insights into changes and trends.

Resoto also offers simple and human-friendly ways to view this data—you can export the data to Google Sheets to create charts or use Resoto Notebook for interactive search visualization and analysis.

Today, we are introducing a new way to visualize this data: Resoto Dashboards.

With Resoto Dashboards, your searches and queries are executed periodically and the results displayed in widgets automatically. You can also customize and organize widgets in the dashboard to keep the most important metrics always within reach. You can even share dashboards across your organization so others can access the data. And of course, you can access your dashboards anywhere you have access to your Resoto installation.

· 5 min read
Nikita Melkozerov

Hello folks! A few months ago, we released Resoto Notebook, a library that makes it easy to query, visualize, and analyze Resoto data using pandas, Plotly, and Jupyter Notebooks.

Today, we'll discuss Resoto's new JupyterLite support, which allows you to use notebooks in the browser without installing and launching a Jupyter server.

Want to analyze raw infrastructure data when only platform engineers can access cloud consoles? Or count infrastructure assets without a data scientist? JupyterLite is a JupyterLab distribution that runs entirely in a web browser, and Resoto's JupyterLite support gives you access to popular data analysis tools without the need for any additional installation steps.

· 2 min read
Nikita Melkozerov

We recently released Resoto Notebook, a library that allows for the visualization and exploration of the Resoto graph interactively using Jupyter Notebook.

Resoto Notebook is similar to Resoto Shell in the sense that you execute queries, but the results are returned in a pandas DataFrame structure. This gives you more flexibility in filtering, aggregation, visualization, etc.