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Hei Everyone! We're excited to announce the latest updates to Charts, designed to enhance your data visualization and analysis experience. In this release, we've focused on improving data viewing capabilities, introducing new features for better visual analysis, and enhancing monitoring job setup and validation. These enhancements are aimed at providing users with greater flexibility, control, and confidence in leveraging Charts for their data-driven decisions. a) Improved Chart Data Viewing and Trust in the data viewed - Increased the maximum number of raw data points viewable in a chart from 500 to 100,000.- Aggregate min/max shading is now enabled by default when switching to aggregate data. This substantial increase in data points allows for a more comprehensive analysis of data trends in RAW mode without the need to switch to aggregated views prematurely. This min / max shading being on by default should ensure that users are better aware of when they are viewing aggregated data.
Hei Dear CDF usersAre you a user using charts for data science purposes / tasks or an active user of python code / jupyter notebooks ? We would love to talk to you as @Lars Moastuen would like to better understand your needs and current workflows. The team is currently working on a jupyter notebooks integration with charts and would like to better understand the needs there. Please feel to comment in the thread below if you are an user using charts for these purposes.
Hi CDF Users,This is a reminder notice for the deprecation of AIR. The alpha-level maturity monitoring solution AIR will be deprecated on 2023/12/31 and we will no longer provide support for the AIR after this date.To ensure a seamless transition and to provide you with improved monitoring capabilities, we are excited to introduce our new solution in Charts which is in open beta and is now available to all customers.Key Features of Monitoring in Charts:Enhanced User Experience: Monitoring in Charts has a better user interface, providing a user-friendly experience for proactive troubleshooting and root cause analysis. Integration with No Code Calculations: Our new solution allows for effortless integration with no code calculations, streamlining the monitoring process and empowering users to derive insights without the need for extensive coding.Stability and Reliability: Monitoring in Charts is built with stability and reliability in mind.Scalability: Monitoring in Charts is built on a
Open Beta Release of Monitoring and Alerting in Charts!Dear CDF User,We are thrilled to announce the open beta release of the monitoring and alerting functionality in Charts within CDF. This release marks a significant leap forward in providing you with a comprehensive monitoring and alerting solution, designed to empower you in managing and optimizing your data flows.Key Features of the Open Beta Release:Native Thresholds on CDF Data: Now, within Charts, you can seamlessly create thresholds on time series data. This native capability allows you to monitor or both your time series data and KPI’s generated from no code calculations, ensuring that you are alerted precisely when needed. Save your no-code calculations in CDF: Now within Charts you have the ability to save your no code calculations within CDF so that they are consumable in other context for example your own dashboards, monitoring etc. This write back is now scoped to a dataset to ensure that the integrity of the data is mai
Hello Early AdoptersMy name is Arun and i work as a product manager for the monitoring and alerting services in CDF at Cognite. We are working towards building a solution that is industry standard and solves all needs going forward in the space of monitoring and alerting. Although we have a beta version of monitoring in charts available today we need your help. We would like to understand how you are doing monitoring today, go through a use case to understand the monitoring user flow and finally validate the concept that we have today with you.The session should only take about 90 mins and we can always split this up into two sessions if that is better. You will get to provide feedback to us early as well as help us build a solution that can help make alerting and monitoring much easier to use and do for your use cases.If you are interested in sharing your thoughts and feedback with us please feel free to leave a comment on this thread.
Monitoring in ChartsHello early adopters! It has been a bit quiet on this group as we have been working on a new monitoring solution in CDF and i can now say that after a lot of hard work monitoring in charts in now available on select clusters in CDF namely az-eastus-1, westeurope-1 and europe-west1-1 (Coming soon).Features:Alerting and Monitoring is a central element in a DataOps platform. This allows users to proactively discover issues with equipment, systems and processes.Cognite Data Fusion provides the ability to monitor data as a core capability. It gives users an easy way to set up monitoring jobs and perform further analysis through Charts and other components within CDF.We have the following features available now:Ability to set up a monitoring job on a time series within Charts Upper and lower threshold capabilities out of the box Get notified through CDF UI and emails Filter and manage monitoring jobs within the charts Filter and resolve relevant alerts Stable and scalable
Hey community we are trying to be more active in this cognite hub page and add content consistently from a month to month basis. This will include a series of articles on alerting and monitoring as well as plans regarding where AIR is going into the future.Why Alerting?Automated alerting is an essential part of monitoring. They allow you to spot issues with equipment groups, time series, data quality, pipelines etc.But alerts aren’t always as effective as they could be. In particular, real problems are often lost in a sea of noisy alarms. In short:Alert liberally meaning its ok to spam users rather than not alert at all Make sure that the user has the complete control of how and when they want to be alertedInherent challenges in AlertingSensitivity: Overly sensitive systems cause excessive false positive alerts, while less sensitive systems can miss issues and have false negatives. Determining the correct alerting threshold requires ongoing tuning and refinement. Fatigue: The common ap
Below we have outlined several frequently asked questions and their corresponding answers.Don’t see the answer to your question? Post as a reply in this thread and we’ll be sure to answer and/or add it to the FAQ list below!Protip: Use [cmd+f] or [cntrl+f] to search for keywords related to your question. FAQs How do i use monitoring in CDF and access the documentation?You can access the documentation for the new monitoring solution in CDF here: https://docs.cognite.com/cdf/charts/#monitoring.
Have a feature request or idea for a new and improved functionality?Then we’d love to hear all about it! We’re constantly working to improve the AIR product experience.You can choose to either create a dedicated post (topic) in the AIR group by clicking the Create topic button or simply post a reply below in this thread. Remember to say whether your feature request is Nice to have, Important, or Critical to you and why. Screenshots, sketches, or explanatory videos are also encouraged.
Found a bug? Have a question about how something works? We want to hear about it!You can choose to either create a dedicated post (topic) in the AIR group by clicking the Create topic button or simply post a reply below in this thread. Remember to include a screenshot or video to help the product team best understand what exactly you’re talking about or referring to.
Hello and welcome to our AIR group! AIR is a powerful tool for engineers and domain experts to set up monitoring jobs and get notified on conditional breaches on time series data. It looks to give access to an easy to use dashboard to set up alerts, get notified by both email and SMS, and be able to look at the problem time series in other tools such as Charts. AIR also provides data scientists and model builders the ability to build data science models in python and deploy them with ease, using our custom workflows and bypassing the need for separate devops solutions.AIR is in the process of becoming a core capability in Cognite Data Fusion and will continue to solve the need for monitoring on both time series and event data in our platform. To start using AIR, simply go to air.greenfield.cogniteapp.com. You will find the "Getting started" documentation on Cognite Docs here. We hope you’d like to share your thoughts and questions with us here in this group so we can build a as useful
Background: A production facility had numerous valves that were being opened and closed at varying rates and amounts. The subject matter expert at hand wanted to be alerted when the pressure values of these valves exceeded or dropped below a certain value as set by the subject matter expert.Problem: Today, the majority of valve maintenance is being done according to a fixed schedule. Without AIR and CDF the pressure sensors on these valves would have to be manually read and then converted into an excel spreadsheet. Once the data has been extracted into excel the subject matter expert had to analyze this data and figure out if the thresholds were being breached. If they were then the maintenance is done in order to prevent accidents. As it can be inferred this process was very manual and not easily scalable. The subject matter expert also wastes their time on tedious tasks as compared to actual important tasks.Solution: Using AIR a data scientist is able to easily define and deploy a t
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