Skip to main content

Product Ideas Pipeline

Filter by Idea Status

Filter by Topic

1298 Ideas

Kubota MamiSeasoned ⭐️⭐️

【Canvas】Feature Request: Improvements for Canvas Usability and Operational EfficiencyGathering Interest

We create a Canvas like the attached example once a month to monitor long-term trends of key operational parameters.Previously, this work was done using Excel, but we have recently migrated to Charts and Canvas.As we move toward full-scale operational use, we would appreciate your support in resolving the following two challenges.These improvements are critical for reducing operational workload and enabling sustainable use of Canvas for monthly monitoring.Request ①We previously requested a permission transfer feature for Charts. However, it is currently difficult to locate individual Charts from within Canvas.Cognite HubTherefore, we would like to request the following enhancement:Ability to select Charts directly within Canvas and transfer their ownership/permissions in bulkWithout this capability, when the responsible person changes, users must first locate all relevant Charts individually and then manually duplicate them one by one. This process is time-consuming and creates a significant operational burden. Request ②When downloading the entire Canvas as it is, each Chart becomes too small to view clearly, making the feature practically unusable.Each month, we include “Special Notes” (as shown in the attached example) and review selected Charts together with those notes.Therefore, we would like to request the following functionality:Ability to select specific Charts on Canvas and:Extract only the selected Charts Automatically format them into an A4-sized layout Export both the visual output and the underlying data (CSV)This feature would enable us to generate clear and usable reports for monthly reviews. Resolving these issues is essential for enabling effective operational use of Canvas.We would greatly appreciate your consideration and support.

Prashant ChauhanPractitioner ⭐️⭐️⭐️

Dynamic Space Assignment in DB Extractor QueriesGathering Interest

Problem StatementThe Cognite DB Extractor currently requires space to be a static, query-level configuration parameter, forcing users to create multiple identical extraction queries when distributing data across spaces based on hierarchical entity relationships.When working with hierarchical data structures, each distinct parent entity requires a separate extraction query, even if the source data and transformations are identical.Asset hierarchy:L1: Assets  L2: Area L3: Field (defines CDF space) L4: Installation L5: WellCurrently, if there are 10 distinct fields across multiple assets, the DB Extractor requires 10 separate extraction queries to distribute timeseries data across 10 spaces - one query per field, even though they all read from the same source table with identical transformations. With multiple assets, areas, and fields, this results in hundreds of extraction queries for a single source table. This creates:Configuration Bloat: Hundreds of nearly identical YAML configurations to maintain Filter Duplication: Adding or modifying a filter condition requires updating hundreds of queries Scheduling Overhead: The same database table is scanned multiple times per ingestion cycle instead of once Resource Inefficiency: Hundreds of independent jobs running on the same data multiplies query load Error Management: One failed field extraction leaves only that space with stale data; manual recovery required per space Maintenance Burden: Updating logic across all queries is error-prone and time-consuming Scalability: As fields are added or wells multiply, query count grows exponentiallyProposed Solution:Allow space to be derived dynamically from query result columns using template syntax, similar to how external_id is generated.

Oussama ALLALI
Seasoned ⭐️⭐️
Oussama ALLALISeasoned ⭐️⭐️

Transformations — Non-deterministic silent upsert when duplicate externalIds are split across workers: request for configurable deduplication strategyGathering Interest

Observed behaviorWhen a CDF Transformation produces rows with duplicate externalId values , the behavior depends on how CDF partitions the data across workers:Duplicates within the same API request (same batch) → the API raises an error → the transformation fails visibly ✅ Duplicates split across multiple API requests (different CDF partitions/workers) → each request succeeds individually → the transformation completes with status "success", but which version of the node was actually written to the knowledge graph is unknown and non-deterministic ❌ProblemThe second case is the dangerous one:Silent and invisible — the run reports success, no alert is triggered, no engineer investigates. The data in the knowledge graph may be incomplete or wrong with no trace. Non-deterministic — which duplicate "wins" depends entirely on which CDF worker flushes first. Two identical runs on the same input can produce different results. No user control — there is no way to express intent: "fail if duplicates exist", "always keep the latest", "deduplicate before write". The behavior is undefined and undocumented at the transformation level.Feature RequestAdd a conflict resolution option at the transformation level for duplicate externalId within a single run: Option Behavior fail Abort the run and report an error if any duplicate externalId is detected across all batches keep_last Last row processed wins deduplicate_before_write CDF deduplicates globally before dispatching to the API

Luciana Velasco MedaniPractitioner ⭐️⭐️⭐️

Feature Request: Individualized Pagination Endpoint for Time Series Datapoints APIClosed

Hi,It would be great to have the creation of an endpoint based on the one below:https://api-docs.cognite.com/20230101/tag/Time-series/operation/getMultiTimeSeriesDatapointsCurrently, this endpoint allows requesting multiple items in a single call and returns the next page individually for each requested item.For Celonis’ REST extractor, this pagination model creates a limitation, as the platform works better with individual pagination, where each response contains only one component and a single pagination continuation token.Today, to enable this extraction, a Python-based workaround is being used within the environment, since the current model is not compatible with the standard extractor.We would like to check if it is possible to make the endpoint available in a more individualized format, with one time series per response, or if there is already an alternative endpoint that supports this scenario. This would greatly help improve the standardization and stability of the integration.Sharing the user’s assessment regarding the available data extraction methods:Extraction BuilderThis solution, offered by Celonis, works very well for REST APIs. However, for the current endpoint structure, pagination is not supported. To solve this issue, it would be enough to update or create a new endpoint that returns only one result per response instead of a nested list structure.SDKThe SDK offers a flexible solution but carries higher implementation risk. It would require using an existing middleware layer to orchestrate the extractions, which is currently considered unfeasible due to resource constraints, access dependencies, and a longer development timeline compared to other alternatives.

Anders Brakestad
Seasoned ⭐️⭐️⭐️
Anders BrakestadSeasoned ⭐️⭐️⭐️

Atlas AI Query Tool: Resolve graph relations for deterministic agent evaluationGathering Interest

Hello :)  Use caseAs an AI evaluation engineer, I want graph relations returned by the agent to be resolved into the underlying equipment and time series instance IDs in the structured JSON output, so that recall metrics reflect the instances the agent actually identified, not only the relation objects it retrieved. BackgroundI am evaluating agents that retrieve process data from an industrial knowledge graph in Cognite Data Fusion. I use a simple Python-based recall evaluator that compares expected equipment/time series IDs against IDs returned in the agent’s structured JSON output. Current behaviorThe agent may identify the correct equipment or time series in the natural-language response, but the structured JSON sometimes only contains the relation objects it used to infer them. This is mostly invisible to the end user, because the answer can still be correct. The issue is automated evaluation: correct retrievals may be undercounted because the expected equipment/time series IDs are missing from the JSON. I am spending too much time prompt engineering around this. The agent can produce the desired JSON structure sometimes, but not in a stable or deterministic manner. Desired behaviorWhen graph relations are returned, the agent should perform one or more final listInstances calls to resolve/dereference them into the underlying equipment and time series instance IDs. This would make the structured JSON reflect the natural-language response, enabling faster, cheaper, and more deterministic recall evaluation for quality control and governance.