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Morten Andreas Strøm / Ben Skal September 12, 2022 What makes Cognite unique? Why is partnering with Cognite the best investment of your time and resources?This is a 3 part series where @Morten and I answer these questions through an indepth look at how our product, Cognite Data Fusion, can help you use industrial data to ignite your digital roadmaps. The topics we discussing are: What is Cognite Data Fusion and why did we build it? (First post) Data modeling grounded in business impact (Previous post) The opportunity cost of custom building your industrial data platform (This post) In the first Why Cognite post, we discussed the data problem Cognite Data Fusion is built to address. The short answer, industrial companies need simple access to complex industrial data. The reason, most operations teams have many business opportunities, but are struggling to effectively use data to improve production. In the
This post is a hands-on introduction to the features supported in the Transformations Python SDK.Prerequisites Use Case 1: Triggering Transformations Step 1 - Create RAW Tables Step 2 - Uploading data to RAW using Postgres Gateway Step 3 - Create new SQL Transformations Step 4 - Trigger the transformation from Azure Data Factory Use Case 2: Orchestrating Transformations Step 1 - Create RAW Tables Step 2 - Create new SQL Transformations Step 3 - Orchestrate Transformations in sequence PrerequisitesKnowledge: Basic knowledge of Azure Functions and Azure Data Factory Basic knowledge of Cognite Data Fusion RAW and SQL Transformations Prior experience with Python, Postgres and SQL Required Datasets:Download and Unzip the attached hub.zip file, you should find the below structure Use Case 1 : asset-hierarchy.csv UseCase 2: OID-Asset-hirerachy.csv OID-Timeseries.csv OID-Datapoints.csv Use Case 1: Triggering TransformationsData is extracted from source systems and
What makes Cognite unique? Why is partnering with Cognite the best investment of your time and resources? This is a 3 part series where @bskal and I answer these questions through an in-depth look at how our product, Cognite Data Fusion, can help you use industrial data to ignite your digital roadmaps. These posts are for those of you who are new to using Cognite Data Fusion and want to understand how we approach the challenges of working with industrial data without losing focus on delivering business impact. The topics we discussing are: What is Cognite Data Fusion and why did we build it? (Last post) Data modeling grounded in business impact (This post) The opportunity cost of custom building your industrial data platform (DIY) In the first Why Cognite post, Ben and I shared why we built Cognite Data Fusion. The short answer, industrial companies need simple access to complex industrial data. The reason, most operations teams have many business opportunities, but are strugglin
Hi, I'm Damjan, and I work with research in the Cognite Data Onboarding group. We’re on a mission to improve and streamline the data onboarding experience for existing and future Cognite Data Fusion users.Our current focus is connecting data sources, building extraction pipelines and the needs around data onboarding. What's your challenges, needs and expectations with regards to the core Cognite Data Fusion onboarding experience? Shout out, share your thoughts and comments below. Thanks for helping us improve CDF!
Cognite Data Fusion is a product built to address the challenges of working with industrial data by: Making data available - Liberate their IT, OT, ET and visual data from siloed source systems with our extractor pipelines. This is done reliably and at scale. Making data meaningful - We use AI-powered contextualisation services to create an Industrial Knowledge Graph that delivers trusted, contextualised data Make data valuable - Cognite Data Fusion enables your teams to access this data with the best-of-breed tools of your choice to turn this data into business value Monolith solutions often end up creating vendor lock-in and can even end up creating more data silos within your organisation With trusted, contextualised data available in an industrial knowledge graph, your teams are equipped to scale solutions both in the volume of new solutions and replicating successful solutions across assets, lines, or sites.The videos are based on the “ice cream factory” use case: a use case
Morten Andreas Strøm / Ben Skal August 22, 2022 Hello digitalization community. My name is Ben Skal, and this is my first time posting to our community. At Cognite, I am part of our industry team and focus on helping our customers apply Cognite Data Fusion to solving the most difficult challenges within their operations. I’ve spent my career working in industry (11 years and counting). First, for a global steel company, then at a major process automation company, and now at Cognite. I am currently living in Austin, Texas and looking forward to e-meeting and learning from this community. The purpose of this series is to answer the following questions: What makes Cognite unique? Why is partnering with Cognite the best investment of your time and resources? This will be a 3 part series to precisely answer these questions through an in-depth look at how our product, Cognite Data Fusion, can help you use ind
In the 1800s, enterprises organised themselves to use their capital assets effectively. Beginning in the mid-1900s, they organised to take better advantage of their people. Today, “data” are increasingly important to virtually all companies. There are many ways to “put data to work,” each with its own strengths and challenges. One option is to focus on finding and exploiting both value pools for the business and deep, fundamental technical capabilities provided by CDF. This can be done by executing an onsite Use Case Discovery Workshop. There are three main steps to executing a Use Case Discovery Workshop: Identify qualified use case ideas Prioritize the use case ideas and select the top use cases Detail out the top use cases 1. Identify qualified use case ideas 2. Prioritize the use case ideas and select the top use cases 3. Detail out the top use cases How do you find the best opportunity to leverage data ?
We are very excited about being officially in General Availability with Cognite Functions! A big thank you to everyone who helped in this journey and your tremendous contribution! Please keep posting feedback and issues, as we are constantly improving the service.As part of GA, there are a few things that you should consider:We have support in the official SDK (cognite-sdk version 3.9.0) and have moved to V1 API endpoint. We recommend you to use only the official Python SDK when creating new functions and migrate the old functions that point to the experimental one. We will remove Functions from the cognite-sdk-experimental starting version 0.94.0. You will still be able to use the experimental SDK with versions < 0.94.0 until we remove the playground API (because the experimental SDK uses the playground URL) by November 1st. Functions in API playground is retired at 1st of November.Check out here more details about the release:
We’re @Uzair Wali and @kelvin, Senior Data Scientist and Data Science Lead in Cognite’s Manufacturing delivery team. In this post we talk about the increasingly important ability to intuitively and flexibly query data from all steps of a product life cycle and across source systems, with examples we’ve implemented on Cognite Data Fusion together with our users.The need for traceabilityIn many manufacturing industries, the ability to trace a product through its manufacturing life cycle, whether internal or supply chain is extremely important. It entails the collection and management of information regarding what has been done in manufacturing processes, from the raw materials and parts used to the shipment of finished products. An industrial knowledge graph that enables this traceability has the potential to not only let users speed up or automate existing use cases, it also opens up possibilities for considerable value addition.Typical questions A customer complains about the quality o
Hello Charts Community,Let me begin by saying thank you for all of your input, feedback, and contributions you’ve provided thus far. On behalf of the entire team, we couldn’t have made Charts into what it is today without your invaluable contributions. Charts General AvailabilityFor our August 2022 Cognite Data Fusion release, we have announced that Charts is transitioning from early adopter to general availability! We are eager and proud to move this valuable functionality into its next phase of life.In practice, it’s a stamp of approval that Charts is a reliable and stable Cognite Data Fusion feature. As we have done throughout our early adopter phase, we still intend to release new functionalities continuously and as soon as they’re ready to be made available. We will roll-up the communication in our bi-monthly CDF release communications, but will post in this group as soon as anything new is ready for use. What’s next for Charts?For the remainder of the year, our key focus area w
In the spirit of summer reading- here’s a pretty interesting blog post covering 5 emerging challenges in commodity trading. What similarities/differences/additional challenges apply to power trading? Anything missing here?https://www.cognite.com/en/blog/commodity-trading-data-challenges1. Rapid increases in the number and availability of new data sources are accelerating the complexities of managing data and analytics in global markets2. Reliance on legacy systems means participants in high-paced commodity markets struggle to make data relevant and actionable3. Participants in commodity markets need to accelerate their data management and digital development just to keep up with technological improvements4. Existing data solutions in commodity markets are often designed as one-stop single solutions or platforms, with little room to create a proprietary competitive edge5. Continued growth in market complexities will require that IT platforms and data architectures are designed to remain
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
Hello Charts Early Adopter Community,There have been several new features and functionalities released lately, with some being released just today. I’ve recorded a video (below) to explain several of these core features in detail, namely – calculations running on individual data points, not aggregates.You can also scroll down to read the written details.Please do leave comments below with questions and feedback!DetailsCharts UI/UX Calculations are now running on individual data points, not aggregates In the past, calculations have been running on time series aggregates. This meant that, although calculations were approximately calculations were often not correct his is a very important new feature that greatly increases the accuracy and trustworthiness of calculation results in Charts. Watch the above video or see the slides, below, for more info. If you receive a warning that looks like this (see image, below), then it means that the results of the data has been downsampled to perfo
WhyWhen starting out in Cognite Data Fusion (CDF) project, it's natural to start by creating data governance elements like groups, datasets, and Raw databases from the CDF user interface. But as the solution begins to scale, you'll quickly realize that it is demanding to set up a detailed configuration handling multiple solutions, sources, roles, and other dimensions. For scaling, precise control is needed for access management and data governance and to enforce the guidelines and rules across the solution. The problem is not trivial, and a good way to solve it is by replacing the manual approach with a configuration-driven system, where the configuration language supports higher-level concepts for data lineage and access control. With configuration files as the foundation, you can set up an automated DevOps process and review and approve any changes to the structure before they are deployed. This approach also dramatically simplifies sharing the same configuration across multiple envi
Just stumbled upon an article from McKinsey in 2019 which articulates well what I believe is a crucial role at many of the customers I’ve worked with: the Analytics Translator. The technology in industry is continuing to move forward quickly opening up many new opportunities, but the deep domain expertise in production processes, maintenance, and more is still as important as ever. To me, the Analytics Translator role seems a perfect bridge between these two worlds. I’ve seen a number of manufacturers successfully “upskill” prior machine operators, process engineers, and more to fulfill this role. It’s always very impactful, making for significantly more effective digital initiatives. From my personal experience, I see them doing things like:Running use case workshops and prioritizations Internal training of users Adoption tracking Interim project manager during new technology initiatives Liaison with external technology partners and SIs Curious how others see this role, and whether yo
Cognite Data Fusion is a product built to address the challenges of working with industrial data by: Making data available - Liberate their IT, OT, ET and visual data from siloed source systems with our extractor pipelines. This is done reliably and at scale. Making data meaningful - We use AI-powered contextualisation services to create an Industrial Knowledge Graph that delivers trusted, contextualised data Make data valuable - Congite Data Fusion enables your teams to access this data with the best-of-breed tools of your choice to turn this data into business value Monolith solutions often end up creating vendor lock-in and can even end up creating more data silos within your organisation With trusted, contextualized data available in an industrial knowledge graph, your teams are equipped to scale solutions both in the volume of new solutions and replicating successful solutions across assets, lines, or sites.The videos are based on the “ice cream factory” use case: a use case
Today’s topic is about interacting with Cognite Data Fusion (CDF) from Android devices. Android devices can be phones, but also other kind of devices like hands-free devices (with speech commands). Since Kotlin, the Android preferred language, has interoperability with Java, it facilitates the integration of interactions with CDF in your Android apps. You can indeed use our Java SDK in an Android application, which makes it really easy to retrieve and upload data from/to CDF. The Cognite Java SDK is complete and maintained. This can broaden your thoughts/ideas about what to do with CDF. Below a few examples of what can be done : Retrieve information from CDF to display it on your mobile device Upload data to CDF from a mobile device, which can be useful in an industrial context Using specific features, from mobile devices (like a camera for example) to upload particular data to CDF (raw, or to extract features from them with an ML model, thanks to a Cognite function for example)
We have identified a bug in the time series datapoints fetcher in the Python SDK which may cause duplicated timestamps to be returned. Affected versions are 2.47 through 2.51. Please check if you have anything deployed using the datapoints API in this version range. If you do, please upgrade to >=2.52 ASAP.https://github.com/cognitedata/cognite-sdk-python/blob/master/CHANGELOG.md
This is how it looks like when you push geospatial to the limit and show 4+ billion essential ocean variables (temperature, salinity, pH, oxygen etc) captured from 220,000 research cruises from 1890-YTD. Such data is very important to understand the effects of climate change, for instance ocean warming, deacidification, dead-zones (lack of oxygen), biodiversity migration and more. In the Ocean Data Platform, we have implemented something called the “Ocean Data Connector” which is a cloud-based JupyterHub infrastructure where you can analyze this data in a very efficient way.Ocean Data Platform and the “Explorer” interface
On 8th March our amazing data scientists at HUB Ocean and Cognite gave a workshop at the Woman in Data Science (WiDS) event in Oslo. Have a look at the video and blog post. The data challenge was related to coral reef bleaching events applying open data on the Ocean Data Platform.Closing the gender gap in Ocean Science? — HUB Ocean | Dedicated to Unlocking Ocean Datahttps://www.linkedin.com/posts/hubocean_coralreefs-biodiversity-collaboration-activity-6928641478181154816-IImk?utm_source=linkedin_share&utm_medium=member_desktop_web
As we announced in our last release post, we recently updated our calculations backend to run on individual data points if the total count of data points is less than the predefined maximum limit.(You can watch the video walkthrough for more information.)When this improved functionality was released a few weeks ago, this limit was set at the intentionally low value of 10,000 individual data points. We did this to test our infrastructure, gather feedback, and ensure our backend will not crash with these more expensive requests — Thanks to those of you who have provided us with input!As of today, we have released an update which increases this limit from 10,000 data points → 100,000 data points.This 10x improvement in performance will help to provide accurate, trustworthy calculation results for larger ranges of time and data. With this new 100k limit, we’ve reached the maximum number of data points we can retrieve from the Cognite Data Fusion Time series API with a single request.While
Putting your learning into practice can be challenging. Not to worry, we're here to help! 🚀 Our subject matter experts and the Cognite Academy team joined forces to create a course on CDF Transformations. The course is designed for data engineers and anyone who wants to learn about CDF Transformations.In this course, you'll learn how to transform data into the CDF data model using CDF Transformations. This course will walk you through lessons on the target schema, writing SQL queries, and running and scheduling transformations, accompanied by interactive knowledge checks, reading materials, and a hands-on exercise.Upon completion, you'll be able to: Understand why you should use CDF Transformations. Find detailed information about the target schema. Write SQL queries and use SparkSQL and Cognite's custom functions to transform data. Schedule and run data transformations. Register for this course right away! 🕰Happy Learning! 😊
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