Community Discussions

Hey there! I am Sunil, Product Manager for Data Integrations. Since this is our first post in the Hub, let me introduce my team and what we do. My team is responsible for the Ctrl+C Ctrl+V of your data from source systems to Cognite Data Fusion . But on a serious note, we are responsible for ELT - Extract, Load, and Transform of data. We are thrilled that you are interested in learning about the Transformations Python SDK. Yayy!In this post, I list a couple of use-cases/scenarios where the python SDK for transformations makes managing and orchestrating transformation jobs simple and seamless. Our goal is to make the transformations SDK more developer-friendly and would like to hear your feedback. Do check out the API and SDK docs for more details, but now on to a few use cases! Use Case 1: Triggering TransformationsCognite Data Fusion helps liberate industrial data silos and provides a unified and contextualized view of the data. It is now easy to derive insights and build application

If you’ve been feeling like that calculations in your Charts are running faster than before… they are!Yesterday, we released a new version (V2) of our calculations backend to production. This update includes  numerous performance improvements, bug fixes, and quality improvements.In short, it’s an all-around better experience built on higher quality code.Test it for yourself on your CDF project. As always, let us know in this group if you have any questions or feedback!

Thank you again to everyone who joined us live for yesterday’s Product Spotlight webinar! To those of you who couldn’t make it to the live event or if you want to re-watch, you can take a look at the recording, below.If you want to read about the the details of the October CDF release, you can visit the post in Product Updates by clicking here. We already received some great feedback and we’re happy to hear several of you found the product demo with a practical use case example to be helpful. If you haven’t already submitted your feedback about the webinar, please do so here. We’re reading and discussing each response so we can make all of our future product webinars as valuable as possible. We’re already planning a new event about Charts, exclusively available for all of you in our Early Adopter community, so stay tuned for updates and invitations.Have a great rest of your week, everyone!P.S. We also appreciate everyone’s patience with the technical difficulties that were experienced

My name is Sigrid Schaanning, and I have written this post together with Atussa Koushan. We both work as Software Engineers in Cognite’s Disruptive-MVP team, which focuses on robotics and computer vision. In this post, we will look further into how to develop a computer vision model, from gathering data to deploying the model on a robot. First, let’s set the scene for what we are trying to achieve in this post.Imagine performing routine inspections at an industrial facility or an oil platform. You might have to read and note down the values of multiple gauges three times a day - every single day. Or, you might have to inspect different parts of the facility to check for corrosion or wear damage.Routine inspections could be tedious and repetitive work, and it might take place in surroundings which are dangerous to humans.Robots, on the other hand, have no perception of tedious work and they could also operate in hostile environments. Robots, together with computer vision, constitute a p

Hello Charts Group! On November 3rd 15:00-15:45 CET, we will be hosting a live webinar to present highlights from the latest CDF release, with a special focus on CHARTS AND THE CHARTS EARLY ADOPTER GROUP.You will learn about why we’re building Charts, what problems it can solve, and how to use key features and functionalities. We’ll demonstrate how to leverage this new CDF functionality as we build and solve real use cases in Charts step by step. From finding the relevant data, to using P&IDs to gain a systems understanding, to configuring the chart, to creating the no-code calculations to solve the problem at hand. This will be an interactive session for asking questions, discussing ideas, and providing feature requests directly with the team. By the end of this session, you’ll feel confident about using Charts in your day-to-day work and feel empowered to think creatively about the solutions you could build to shape a safer, more efficient, more sustainable industrial future.You

Hello Digitalization Community! On November 3rd 15:00-15:45 CET, we will be hosting a live webinar to present highlights from the latest CDF release, with a special focus on CHARTS AND THE CHARTS EARLY ADOPTER GROUP.You will learn about why we’re building Charts, what problems it can solve, and how to use key features and functionalities. You’ll also have the opportunity to join our private early adopter group for Charts. By the end of this session, you’ll feel confident about using Charts in your day-to-day work and feel empowered to think creatively about the solutions you could build to shape a safer, more efficient, more sustainable industrial future.You can RSVP for the webinar via the link below:This is the first webinar in our Product Release Spotlight series, so you can expect many more after our bi-monthly CDF releases in the future. See you on November 3rd at 15:00 CET!

Hi Community Friends! We thought it might be nice to know who’s who, while we’re actively engaging in our community. I’ll start! My name is Anita, and I’m your Community Director. I joined Cognite in September 2020, and jumped right on the mission of establishing Cognite’s customer community. I’ve worked in a variety of industries, and am passionate about working at the crossroads of sustainable business development and digital transformation. I live in Lommedalen, Norway (translated “the Pocket Valley”), where we have long winters with deep snow. I love horseback riding (Islandic horses in particular), and I have a passion for Italian wine and food.Here’s a fun fact: my grandfather’s brother, who was raised in the remote mountains of Hallingdal in Norway, worked as a chemist with Thomas Alva Edison. The letters he wrote home made me realize from early on that no matter your background, you can change the world. I’m super excited to facilitate for all of you to meet, discuss and learn

Hi everyone,We in C4IR Ocean are starting a series where we are challenging the community to model a certain problem in CDF. The aim with this series is to facilitate discussion and invite community members to share interesting solutions and techniques.The first challenge focuses on Open LineageOpenLineage is an Open standard for metadata and lineage collection designed to instrument jobs as they are running. It defines a generic model of run, job, and dataset entities identified using consistent naming strategies. The core lineage model is extensible by defining specific facets to enrich those entities.In C4IR Ocean, we are dealing with data from multiple providers. Some datasets are open and publicly available, others are closed. It is therefore important to keep track of where data is coming from, and what transformations have been applied to the data after it is read from the provider.We are seeing a good landscape of data lineage solutions - both open and closed source. At the sam

One of the main objectives of the first deployment with an inspection robot is getting your organization ready for robotics. A properly scoped deployment will contribute to engaging the field workers and provide insights on what it takes to keep robots operational on a day-to-day basis. This will lay the foundation for high return on investment for all the following deployments. If the scoping fails, there is a risk of losing trust in robots and gaining less willingness to change in your organization. But fear not. In this post, we’re summarizing our best practices when assisting our customers to scope out their first deployment of an inspection robot.    Selecting the appropriate area of deployment It is common that there is a need to deploy robots in harmful or remote environments. These can be areas that may be highly corrosive, have a risk of explosive gases, have powerful magnetic fields, radiation from heat or nuclear etc. When selecting the appropriate robot for a plant, we reco

In the CDF docs I see there is a PI and OPCUA connector. Many industry 4.0 community members see MQTT rather than OPCUA as the future of Industrial IoT cloud based communication, as it is lightweight, report by exception, client driven, etc…If I want to onboard a facility that has an IoT MQTT gateway, how does Cognite recommend we integrate? I have used Azure IoTHub and Azure Functions to connect and transform the data in the past. Do you have a reference architecture for this? Can we connect to the gateway directly with a CDF connector, or do you recommend using Azure IoTHub and Functions (or similar GCP/AWS)?Also Azure has some neat tools like IoT Device Provisioning Services, be nice to know a POV on if/how to utilize those. Thanks! 

The majority of the hydropower fleet is pretty old. It’s not uncommon to see a 100-year-old power plant still in operation. While it may be a great piece of engineering, how can you access operational data on your laptop or smartphone? After all, you may not want to travel every day to very remote locations. At Ringerikskraft’s Hønefoss II power station in Norway, we installed a set of cameras to passively record alarm events, read gauges, and stream this information to Cognite Data Fusion. It is all connected, so if an alarm goes off, I get an SMS notification and can quickly look up what is happening. Pretty cool, or what? I would like to hear your thoughts. How have you tackled the challenge of digitalizing aging infrastructure?Read the full story 

Hi there,I am looking for resources on CDF templates API.So far I the best introduction I have found is the Python SDK documentation, as well as the information at docs.cognite.com which focuses on using templates in fusion.Can you provide more information about how to use the templates API?

I am trying to stream data from CDF to Azure Event Hub with the Python SDK and cannot find anything related to streaming datasets. Only option so far (as I know) is dps = c.datapoints.retrieve_latest(id=184691546499795)which would need a trigger of some sort to keep running. Data from CDF are so called timeseries from different types of sensors. Is there any documentation on Streaming Data for CDF that I could look at or is Streaming really supported? 

Hi, When developing with the Cognite python SDK, a common restriction is  the API imposed restrictions on queries. Quering time-series, for example, is restricted by 100 asset-ids.  I believe the Python SDK should recognize these constraint violations and batch / concurrently dispatch requests in chunks that do not violate constraints. Optionally with a performance-warning to the developer.Is this sane, or do you think that the developer/customer should maintain a wrapper on the SDK for batching each endpoint (as we’ve currently done at Statnett)?

 If a user includes https:// or http:// in the Override Azure Tenant field the url to Azure AD will not include a correct tenant and login is prevented. This has caused login issues for at least a few users.  The request is simply that the form is modified to strip the protocol scheme, ie removing http:// or https:// automatically, if present.  

Hey, In our current workflow we’re expanding the use of Functions as a tool. We’re somewhat hampered by how “clumsy” it is to bundle and version control proprietary dependencies. Is there something in the pipe to address this issue? Kind regards,Robert

Hey!In our tooling for the power-analyst we make computations using the SyntheticTimeseries API. Results from these are used in subsequent analysis, where we have identified a problem for us.    When a synthetic timeseries is computed with a specified aggregate and resolution, that specification is returned  irrespective of there being data on the originating timeseries. In our case, we compute aggregates over long stretches of time which results in situations like the image below: In the figure, the opaque line is the comptued by addition of the two other signals. Spanning over roughly a year, the period in the middle has a linear rise in the SyntheticTimeseries while the originals are empty. These values are of course meaningless and should be omitted in the successive steps of the analysis. Have you considered implementing a density filter or the like for these types of situations? Or, do you believe this is best solved client side by identifying the holes prior to a set of Syntheti

 McKinsey Quarterly did post an interesting set of articles recently, regarding the transition and change management when looking into digital services. I believe some of the topic and views could be interesting to reflect upon. Go here to find out : https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/five-fifty-so-you-want-to-be-a-software-company?cid=other-eml-alt-mip-mck&hdpid=034603b3-3fba-4313-a963-553dca543b0f&hctky=12386931&hlkid=8635b3c5c91d439a8a47f0bf353a4968  

How a DataOps platform can boost improvement in all parts of the business! And this time is focusing on what is most important - HSE!https://www.pgs.com/investor-relations/ir-news-stock-announcements/pgs-digitalization-initiative-improves-crew-safety/https://www.cognite.com/customers_stories/dataops-in-action-improving-vessel-crew-safety One more big shout out to @Cerys James and @Sverre Olsen ! Congratulations for another amazing use case!

Hey! I’ve a problem with filling a gap from a source to a timeseries in CDF.  Problem descriptionWe’re filling a hole in a time-series from time A to  B. There are some datapoints on the edges of the interval in CDF.Data is extracted from the source, and in python prepped for the datapoints API as a list-of-tuples payload. For an arbitrary period I extract 2976 datapoints which I upload to CDF. Subsequently, I query the time-series for the same period of time and recieve 2928 datapoints. There are no NAN values in the input for either the date-time or value. The data is also hourly, and so I’m wary of it just being an edge effect of poor timestamp specifications for the retrieval. What other PEBCAK things have I missed? Simplified example included below:payload>> [{'externalId': 'ts_externalid', 'datapoints': [...]}]payload[0]["datapoints"][10]>> (1617271200000, 0.0)client.datapoints.insert_multiple(payload)meter_data = client.datapoints.retrieve( start=dates[0], end=dat

 It's been a while, but as promised, here is the follow-up post to my previous article. If you haven't read the previous article, you can find it here.In the first part, I shared the basic concepts about data integration. Today I will continue on the same theme, but the focus will be on latency and frequency. They are related concepts, but not necessarily the same. Why latency and frequency? They are crucial to defining your data pipeline, and they influence which Azure resources you use to cover your needs.What is data latency and frequency? Data latency is how fast/slow data can be retrieved or stored. Low latency means that the data is available in real-time or close to real-time and is vital in use cases where you need to respond quickly to information. Examples are alerts for critical events on machinery and equipment and online games based on real-time experiences. This leads us to the data frequency concept. Data frequency is how many times in a specific period the data should b