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Use this topic to learn the differences between the data modeling tools and which tool to use based on what you want to create. Tool Use to create Description Semantic Modeler Governed data models A browser-based modeling tool that developers use for creating, building, and deploying the semantic model to an .rpd file. The Semantic Modeler editor is a fully-integrated Oracle Analytics component. Because the Semantic Modeler generates Semantic Model Markup Language (SMML) to define semantic models. developers have the choice of using the Semantic Model editor, the native SMML editor, or another editor to develop semantic models. Semantic Modeler provides full Git integration to support multi-user development. You can use the Semantic Modeler to create semantic models from the data sources that it supports. Use the Model Administration Tool to create semantic models from data sources that Semantic Modeler doesn't support. See What Is Oracle Analytics Semantic Modeler? and Data Sources Supported for Semantic Models. Model Administration Tool Governed data models You might also see this tool referred to as Administration Tool. A mature, longstanding, heavyweight, developer-focused modeling tool that provides complete governed data modeling capabilities. Developers use the Model Administration Tool to define rich business semantics, data governance, and data interaction rules to fetch, process, and present data at different granularity from disparate data systems. Oracle recommends that you use Semantic Modeler to create semantic models from the data sources Semantic Modeler supports, and that you use Model Administration Tool to create semantic models from any data source that Semantic Modeler doesn’t support. See About Creating Semantic Models with Model Administration Tool and Data Sources Supported for Semantic Models. The Model Administration Tool is a Windows-based application that isn't integrated into the Oracle Analytics interface. You download the Model Administration Tool and install it onto and use it

From its data sources, and the imported data might be updated on a regular or ad-hoc basis. Semantic models in DirectQuery, Direct Lake, or LiveConnect mode to Analysis Services don't import data; they query the underlying data source with every user interaction. Semantic models in Push mode don't access any data sources directly but expect you to push the data into Power BI. Semantic model refresh requirements vary depending on the storage mode/semantic model type.Semantic models in Import modePower BI imports the data from the original data sources into the semantic model. Power BI report and dashboard queries submitted to the semantic model return results from the imported tables and columns. You might consider such a semantic model a point-in-time copy. Because Power BI copies the data, you must refresh the semantic model to fetch changes from the underlying data sources.When a semantic model is refreshed, it's either fully refreshed or partially refreshed. Partial refresh takes place in semantic models that have tables with an incremental refresh policy. In these semantic models, only a subset of the table partitions are refreshed. In addition, advanced users can use the XMLA endpoint to refresh specific partitions in any semantic model.The amount of memory required to refresh a semantic model depends on whether you're performing a full or partial refresh. During the refresh, a copy of the semantic model is kept to handle queries to the semantic model. This means that if you're performing a full refresh, you'll need twice the amount of memory the semantic model requires.We recommend that you plan your capacity usage to ensure that the extra memory needed for semantic model refresh is accounted for. Having enough memory prevents refresh issues that can occur if your semantic models require more memory than available during refresh operations. To find out how much memory is available for each semantic model on a Premium capacity, refer to the Capacities and SKUs table.For more information about large semantic models in Premium capacities, see large semantic models.Semantic models in DirectQuery modePower BI doesn't import data over connections that operate in DirectQuery mode. Instead, the semantic model returns results from the underlying data source whenever a report or dashboard queries the semantic model. Power BI transforms and forwards the queries to the data source.NoteLive connection reports submit queries to the capacity or Analysis Services instance that hosts the semantic model or the model. When using

Multiple refresh attempts.Access refresh detailsYou can access semantic model refresh details from multiple locations: the Monitoring hub historical runs, semantic model refresh settings and semantic model detail page.The following image highlights where to click on the semantic model refresh settings window, to access refresh details:In the following image, you can see where to click on the semantic model details page to access refresh details:View refresh metricsFor each refresh attempt, you can view the execution metrics by selecting the Show link in the Execution details column. Execution metrics can assist with troubleshooting or optimizing the semantic model refresh. Previously, this execution metrics data was accessible through Log Analytics or Fabric Workspace Monitoring.Link from external applicationsYou can link semantic model refresh details from external applications by constructing a URL with the workspace, semantic model, and refresh ID. The following line shows the structure of such URLs: example, the following Fabric Notebook uses semantic link sempy and Power BI API Get Refresh History to create a refresh detail URL for each run of a semantic model:import sempyimport sempy.fabric as fabricimport pandas as pd workspaceId = "[Your Workspace Id]"semanticModelId = "[Your semantic model Id]"client = fabric.FabricRestClient()response = client.get(f"/v1.0/myorg/groups/{workspaceId}/datasets/{semanticModelId}/refreshes")refreshHistory = pd.json_normalize(response.json()['value'])refreshHistory["refreshLink"] = refreshHistory.apply(lambda x:f" axis=1)displayHTML(refreshHistory[["requestId", "refreshLink"]].to_html(render_links=True, escape=False))The previous code generates a table with refresh IDs and their corresponding detail page URLs, as shown in the following image:Refresh cancellationStopping a semantic model refresh is useful when you want to stop a refresh of a large semantic model during peak time. Use the refresh cancellation feature to stop refreshing semantic models that reside on Premium, Premium Per User (PPU) or Power BI Embedded capacities.To cancel a semantic model refresh, you need to be a contributor, member, or an admin of the semantic model's workspace. Semantic model refresh cancellation only works with semantic models that use Import mode or Composite mode.NoteSemantic models created as part of datamarts aren't supported.To start a refresh, go to the semantic model you want to refresh, then select Refresh now.To stop a refresh, follow these steps:Go to the semantic model that's refreshing and select Cancel refresh.In the Cancel refresh pop-up window, select Yes.Best practicesChecking the refresh history of your semantic models regularly is one of the most important best practices you can adopt to ensure that your reports and dashboards use current data. If you discover issues, address them promptly and follow up with data source owners and gateway administrators if necessary.In addition,

Must add all required data source definitions to the same gateway.Deploying a personal data gatewayIf you have no access to an enterprise data gateway and you're the only person who manages semantic models so you don't need to share data sources with others, you can deploy a data gateway in personal mode. In the Gateway connection section, under You have no personal gateways installed , select Install now. The personal data gateway has several limitations as documented in Use a personal gateway in Power BI.Unlike an enterprise data gateway, you don't need to add data source definitions to a personal gateway. Instead, you manage the data source configuration by using the Data source credentials section in the semantic model settings, as the following screenshot illustrates.Accessing cloud data sourcesSemantic models that use cloud data sources, such as Azure SQL DB, don't require a data gateway if Power BI can establish a direct network connection to the source. Accordingly, you can manage the configuration of these data sources by using the Data source credentials section in the semantic model settings. As the following screenshot shows, you don't need to configure a gateway connection.NoteEach user can only have one set of credentials per data source, across all of the semantic models they own, regardless of the workspaces where the semantic models reside. And each semantic model can only have one owner. If you want to update the credentials for a semantic model where you are not the semantic model owner, you must first take over the semantic model by clicking on the Take over button on the semantic model settings page.Accessing on-premises and cloud sources in the same source queryA semantic model can get data from multiple sources, and these sources can reside on-premises or in the cloud. However, a semantic model can only use a single gateway connection, as mentioned earlier. While cloud data sources don't necessarily require a gateway, a gateway is required if a semantic model connects to both on-premises and cloud sources in a single mashup query. In this scenario, Power BI must use a gateway for the cloud data sources as well. The following diagram illustrates how such a semantic model accesses its data sources.NoteIf a semantic model uses separate mashup queries to connect to on-premises and cloud sources, Power BI uses a gateway connection to reach the on-premises sources and a direct network connection to access the

Skip to main content This browser is no longer supported. Upgrade to Microsoft Edge to take advantage of the latest features, security updates, and technical support. Direct Lake overview Article01/26/2025 In this article -->Direct Lake is a storage mode option for tables in a Power BI semantic model that's stored in a Microsoft Fabric workspace. It's optimized for large volumes of data that can be quickly loaded into memory from Delta tables, which store their data in Parquet files in OneLake—the single store for all analytics data. Once loaded into memory, the semantic model enables high performance queries. Direct Lake eliminates the slow and costly need to import data into the model.You can use Direct Lake storage mode to connect to the tables or views of a single Fabric lakehouse or Fabric warehouse. Both of these Fabric items and Direct Lake semantic models require a Fabric capacity license.In some ways, a Direct Lake semantic model is similar to an Import semantic model. That's because model data is loaded into memory by the VertiPaq engine for fast query performance (except in the case of DirectQuery fallback, which is explained later in this article).However, a Direct Lake semantic model differs from an Import semantic model in an important way. That's because a refresh operation for a Direct Lake semantic model is conceptually different to a refresh operation for an Import semantic model. For a Direct Lake semantic model, a refresh involves a framing operation (described later in this article), which can take a few seconds to complete. It's a low-cost operation where the semantic model analyzes the metadata of the latest version of the Delta tables and is updated to reference the latest files in OneLake. In contrast, for an Import semantic model, a refresh produces a copy of the data, which can take considerable time and consume significant data source and capacity resources (memory and CPU).NoteIncremental refresh for an Import semantic model can help to reduce refresh time and use of capacity resources.When should you use Direct Lake storage mode?The primary use case for a Direct Lake storage mode is typically for IT-driven analytics projects that use lake-centric architectures. In this scenario, you have—or expect to accumulate—large volumes of data in OneLake. The fast loading of that data into memory, frequent and fast refresh operations, efficient use of capacity resources, and fast query performance are all important for this use case.NoteImport and DirectQuery semantic models are still relevant in Fabric, and they're the right choice of semantic model for some scenarios. For example, Import storage mode often works well for a self-service analyst who needs the freedom and agility to act quickly, and without dependency on IT to add new data

ISCC - Semantic Image-Codeiscc-sci is a proof of concept implementation of a semantic Image-Code for theISCC (International Standard Content Code). Semantic Image-Codes aredesigned to capture and represent the semantic content of images for improved similarity detection.CautionThis is an early proof of concept. All releases with release numbers below v1.0.0 maybreak backward compatibility and produce incompatible Semantic Image-Codes.What is ISCC Semantic Image-CodeThe ISCC framework already comes with an Image-Code that is based on perceptual hashing and canmatch near duplicates. The ISCC Semantic Image-Code is planned as a new additional ISCC-UNIT focusedon capturing a more abstract and broad semantic similarity. As such the Semantic Image-Code isengineered to be robust against a broader range of variations that cannot be matched with theperceptual Image-Code.FeaturesSemantic Similarity: Leverages deep learning models to generate codes that reflect thesemantic content of images.Bit-Length Flexibility: Supports generating codes of various bit lengths (up to 256 bits),allowing for adjustable granularity in similarity detection.ISCC Compatible: Generates codes that are fully compatible with the ISCC specification,facilitating integration with existing ISCC-based systems.InstallationBefore you can install iscc-sci, you need to have Python 3.8 or newer installed on your system.Install the library as any other python package:UsageTo generate a Semantic Image-Code for an image, use the code_image_semantic function. You canspecify the bit length of the code to control the level of granularity in the semanticrepresentation.import iscc_sci as sci# Generate a 64-bit ISCC Semantic Image-Code for an image fileimage_file_path = "path/to/your/image.jpg"semantic_code = sci.code_image_semantic(image_file_path, bits=64)print(semantic_code)How It Worksiscc-sci uses a pre-trained deep learning model based on the 1st Place Solution of the ImageSimilarity Challenge (ISC21) to create semantic embeddings of images. The model generates a featurevector that captures the essential characteristics of the image. This vector is then binarized toproduce a Semantic Image-Code that is robust to variations in image presentation but sensitive tocontent differences.DevelopmentThis is a proof of concept and welcomes contributions to enhance its capabilities, efficiency, andcompatibility with the broader ISCC ecosystem. For development, you'll need to install the projectin development mode using Poetry.git clone iscc-scipoetry installContributingContributions are welcome! If you have suggestions for improvements or bug fixes, please open anissue or pull request. For major changes,