Deephaven Community Core's documentation continues to grow with significant additions to our user guide and a new blog series underway.

This month, we launched our YouTube channel. As of now, you can browse an archive of Learning Sessions and the Community Core team's weekly demos. In our Learning Sessions, a Deephaven developer teaches the dev-rel team about the core concepts foundational to our platform or shows off cool capabilities and new features; in the demos, you'll see the Core platform's evolution as the team discusses recent progress or ideas in the works.

Check out these additions to our user guide:

• a set of plotting how-to guides covering our main chart types.
• introductions to popular UI features, like our File Explorer and Markdown Widgets.
• an overview of the many ways to filter within the UI, no queries required.
• new data import / export cheat sheets specific to data format (CSV, Kafka, and Parquet).

We've kicked off a blog series about the powerful pairing of Prometheus and Deephaven. The first installment, "Monitoring system performance and stability with Deephaven and Prometheus", also features a short video demonstration of the example, which uses Deephaven's Application Mode feature.

Holiday dinners have a familiar cast of characters that most can relate to—from the grandparent heralding the good old days to the little cousin who somehow can work a smartphone better than many of the adults in the family.

If you've ever used Prometheus, you know it's pretty great. It's free, open-source software that uses metric-based monitoring and allows users to set up real-time alerts. Prometheus generates tons of system data, and this data can be pulled from Prometheus through various methods.

Using Prometheus's REST API, it's easy to look at historical data and see trends. Simply choose a time range and at what time intervals to pull the data, then analyze the data and generate metrics, such as maximum values and averages over that period.

But what if you wanted to ingest real-time data from Prometheus, and analyze and make decisions based on this data in real-time? That's where Deephaven comes in!

Credit card fraud causes billions of dollars in damages each year. The most infamous cases have affected tens to hundreds of millions of consumers in single attacks through the unlawful exposure of personally identifiable information (PII) related to credit cards. Isolated cases are also common, and can be caused by a variety of methods including skimming, social engineering, and application fraud.

In order to protect their customers, credit card companies rely on fraud detection and prevention software to analyze credit card purchases. These programs look for unusual or unexpected patterns to classify them as possibly fraudulent. In this blog, we propose our own real-time credit card fraud detection solution using Python with Deephaven.

The code in this blog uses SciKit-Learn, which does not come with Deephaven's base images. To run this code, ensure you have the module installed. Here's how you can Install Python packages and Use Python packages in queries. We also have a guide for How to use SciKit-Learn in Deephaven.

It's the time of year for comfort food and Turkey Trots! Maybe you're training for a race or simply maintaining an exercise routine. Personally, I consistently use my fitness watch to ensure I'm meeting my step goals and to track my progress as I try to shave a few seconds off my mile time. The free Strava app beloved by runners and cyclists is another great resource to motivate you in your fitness journey and connect with a community. You can track a variety of exercises and store your results in the app. Did you know you can also download this data as .fit files?

In this blog, we walk you through (pun intended!) downloading your Strava data and importing it into Deephaven Community Core, where you can get an overview of your performance and even use this information to adjust your routine for more health benefits.

Last week we made our prediction of Bones/No Bones days. How did we do?

Deephaven Community Core's documentation includes several simple how-tos and a section of comprehensive reference articles to get you started.

Each morning, the world waits to see if a 13-year-old pug named Noodle is having a “Bones day” or a “No Bones day.” Over the last few months, Noodle’s owner, Jonathan, has chronicled declarations of Nones/No Bones days via TikTok, which depend on Noodle's reaction after Jonathan picks him up from his dog bed.

If Noodle remains standing when Jonathan pulls away, it is a Bones day. This has come to symbolize a productive day and the permission to take risks. However, if Noodle falls down, it is a No Bones day, when viewers are encouraged to indulge in self-care or enjoy a lazy, relaxing day.

Users across social media have rallied around this concept, eagerly awaiting Noodle's pronouncements, especially if an important event is coming up, like a job interview or even their wedding day. You may be wondering: Is there rhyme or reason to Bones vs. No Bones days?

Right now, there are relatively few data points (by our team’s count, we have less than 40 readings), but there is enough to work with to start building a predictor model that can help guide our days when Noodle hasn’t posted or we have an important date coming up (which ideally would fall on a Bones day).

If you're anything like me, you use Docker for nearly everything. However, for most of my projects, I try to maximize efficiency but don't feel the need to go full Kubernetes on the project. That's where this simple application comes in.

It's hard to avoid the “crypto buzz” that is filling the news and discussion in the world these days. With the release of a powerful, real-time data engine like Deephaven Community, we figured you should know how to pull in both historical and live crypto data. You can navigate to our ready-to-go Docker container and look at our CryptoCurrency example and read along for more information.