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[Eventhouse](/fabric/real-time-intelligence/eventhouse) is the time series database in Fabric Real-Time Intelligence. It serves as the target for migrating data away from Time Series Insights.
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[Eventhouse](/fabric/real-time-intelligence/eventhouse) is the time series database in Real-Time Intelligence. It serves as the target for migrating data away from Time Series Insights.
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### Prerequisites
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To begin the migration process, ensure that you have created a new Eventhouse in Fabric Real-Time Intelligence. Follow the documentation on how to [create an Eventhouse](/fabric/real-time-intelligence/create-eventhouse).
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* A [workspace](/fabric/get-started/create-workspaces.md) with a Microsoft Fabric-enabled [capacity](/fabric/enterprise/licenses.md#capacity)
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* An [event house](/fabric/real-time-intelligence/create-eventhouse) in your workspace
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### Ingest new data
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To start ingesting new data into your Eventhouse, follow these steps:
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Use the following steps to start ingesting new data into your Eventhouse:
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1. Configure your [Azure Event Hub](/azure/event-hubs/event-hubs-about) with a new consumer group.
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### Migrate reference data
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For migrating reference data, you can follow these steps:
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Use the following steps to migrate reference data:
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1. Use Time Series Insights Explorer or the Reference Data API to download the reference data set. This will allow you to retrieve the necessary data for migration.
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1. Use Time Series Insights Explorer or the Reference Data API to download the reference data set. This allows you to retrieve the necessary data for migration.
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2. Once you have the reference data set, you can upload it into your Eventhouse as another table. This can be done using the [Upload](/fabric/real-time-intelligence/get-data-local-file) feature in Fabric Real-Time Intelligence. By uploading the reference data set, you will be able to access and utilize it within your Eventhouse environment.
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Please note that these steps assume you have already created a new Eventhouse in Fabric Real-Time Intelligence as a prerequisite for the migration process.
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2. Once you have the reference data set, [upload it to your Eventhouse](/fabric/real-time-intelligence/get-data-local-file) as another table. By uploading the reference data set, you can access and utilize it within your Eventhouse environment.
[Eventhouse](/fabric/real-time-intelligence/eventhouse) is the time series database in Fabric Real-Time Intelligence. It serves as the target for migrating data away from Time Series Insights.
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[Eventhouse](/fabric/real-time-intelligence/eventhouse) is the time series database in Real-Time Intelligence. It serves as the target for migrating data away from Time Series Insights.
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High-level migration recommendations.
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#### Create an Eventhouse
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Follow the tutorial on[creating an Eventhouse](/fabric/real-time-intelligence/create-eventhouse) to learn how to set up an Eventhouse for your migration process. This tutorial will provide step-by-step instructions and guidance on creating an Eventhouse in Fabric Real-Time Intelligence.
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Follow the steps in[creating an Eventhouse](/fabric/real-time-intelligence/create-eventhouse) to lset up an Eventhouse for your migration process.
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#### Data Ingestion
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Follow the tutorial on [getting data from Azure Storage](/fabric/real-time-intelligence/get-data-azure-storage) to retrieve data for the storage account corresponding to your Time Series Insights instance. Ensure that you select the appropriate container and provide its URI, along with the necessary [SAS token](/azure/data-explorer/kusto/api/connection-strings/storage-connection-strings#shared-access-sas-token) or [account key](/azure/data-explorer/kusto/api/connection-strings/storage-connection-strings#storage-account-access-key). When configuring the file filters, specify the folder path `V=1/PT=Time` to filter the relevant blobs. Next, verify the inferred schema and remove any infrequently queried columns, while retaining at least the timestamp, TSID column(s), and values. To ensure that all your data is copied to Eventhouse add an additional column and use the [DropMappedFields](/azure/data-explorer/kusto/management/mappings#dropmappedfields-transformation) mapping transformation. Finally, complete the ingestion process.
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Follow the steps in [getting data from Azure Storage](/fabric/real-time-intelligence/get-data-azure-storage) to retrieve data for the storage account corresponding to your Time Series Insights instance.
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Make sure that you:
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1. Select the appropriate container and provide its URI, along with the necessary [SAS token](/azure/data-explorer/kusto/api/connection-strings/storage-connection-strings#shared-access-sas-token) or [account key](/azure/data-explorer/kusto/api/connection-strings/storage-connection-strings#storage-account-access-key).
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1. Configure file filters folder path as `V=1/PT=Time` to filter the relevant blobs.
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1. Verify the inferred schema and remove any infrequently queried columns, while retaining at least the timestamp, TSID column(s), and values. To ensure that all your data is copied to Eventhouse add an additional column and use the [DropMappedFields](/azure/data-explorer/kusto/management/mappings#dropmappedfields-transformation) mapping transformation.
Time Series Insights is a service that enables operational analytics and reporting on historical data. It offers data ingestion, storage, contextualization, analysis, and querying capabilities. As part of migrating to [Fabric Real-Time Intelligence](/fabric/real-time-intelligence/overview), all of these capabilities can be met and even improved by migrating to [Eventhouse](/fabric/real-time-intelligence/eventhouse), the time series database in Fabric Real-Time Intelligence.
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Time Series Insights is a service that enables operational analytics and reporting on historical data. It offers data ingestion, storage, contextualization, analysis, and querying capabilities. As part of migrating to [Real-Time Intelligence](/fabric/real-time-intelligence/overview), all of these capabilities can be met and even improved by migrating to [Eventhouse](/fabric/real-time-intelligence/eventhouse), the time series database in Real-Time Intelligence.
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## Migration steps
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### Time Series Insights Gen1
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Time Series Insights Gen1 doesn’t have cold Storage or hierarchy capability. All data has fixed retention. The suggested migration path is to set up parallel data ingestion to Eventhouse. After the fixed data retention period passes, the Time Series Insights environment can be deleted, as Eventhouse will contain the same data.
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1. [Create an Eventhouse](/fabric/real-time-intelligence/create-eventhouse)
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1. Set up parallel [ingestion](/fabric/real-time-intelligence/get-data-event-hub) from [Azure Event Hubs](/azure/event-hubs/event-hubs-about) to the Eventhouse
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1. Continue ingesting data for the period of fixed retention
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1. Start using Eventhouse
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1. Delete Time Series Insights environment
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Detailed FAQ and engineering experience are outlined in [How to migrate TSI Gen1 to Fabric Real-Time Intelligence](./how-to-tsi-gen1-migration.md)
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1.[Create an Eventhouse](/fabric/real-time-intelligence/create-eventhouse).
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1. Set up parallel [ingestion](/fabric/real-time-intelligence/get-data-event-hub) from [Azure Event Hubs](/azure/event-hubs/event-hubs-about) to the Eventhouse.
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1. Continue ingesting data for the period of fixed retention.
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1. Start using Eventhouse.
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1. Delete the Time Series Insights environment.
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Detailed FAQ and engineering experiences are outlined in [How to migrate TSI Gen1 to Real-Time Intelligence](./how-to-tsi-gen1-migration.md)
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### Time Series Insights Gen2
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Time Series Insights Gen2 stores all data on cold storage using Parquet format as a blob in the customer’s subscription. To migrate data, the customer should take the blob and import it into Eventhouse using the GetData experience.
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1.[Create an Eventhouse](/fabric/real-time-intelligence/create-eventhouse)
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1.Redirect data [ingestion](/fabric/real-time-intelligence/get-data-event-hub) to Eventhouse
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1.[Import(/fabric/real-time-intelligence/get-data-azure-storage)] Time Series Insights cold data
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1.Start using Eventhouse
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1.Delete Time Series Insights Environment
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1.[Create an Eventhouse](/fabric/real-time-intelligence/create-eventhouse).
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1.Redirect data [ingestion](/fabric/real-time-intelligence/get-data-event-hub) to Eventhouse.
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1.[Import(/fabric/real-time-intelligence/get-data-azure-storage)] Time Series Insights cold data .
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1.Start using Eventhouse.
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1.Delete the Time Series Insights Environment .
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Detailed FAQ and engineering experience are outlined in [How to migrate TSI Gen2 to Fabric Real-Time Intelligence](./how-to-tsi-gen2-migration.md)
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Detailed FAQ and engineering experience are outlined in [How to migrate TSI Gen2 to Real-Time Intelligence](./how-to-tsi-gen2-migration.md)
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### Data consumption and visualization
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Once you have successfully migrated your data and established continuous data ingestion from your sources, you can consume the data using a variety of tools and technologies. These include:
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*[Power BI](/fabric/real-time-intelligence/create-powerbi-report): Power BI is a powerful business intelligence tool that allows you to create interactive visualizations and reports based on your data. You can connect Power BI to your Fabric Real-Time Intelligence environment and leverage its rich set of features to gain insights from your data.
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*[Power BI](/fabric/real-time-intelligence/create-powerbi-report): Power BI is a powerful business intelligence tool that allows you to create interactive visualizations and reports based on your data. You can connect Power BI to your Real-Time Intelligence environment and leverage its rich set of features to gain insights from your data.
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*[KQL Querysets](/fabric/real-time-intelligence/create-query-set): KQL (Kusto Query Language) is a query language used in Azure Data Explorer and other Azure services. With KQL, you can write powerful queries to analyze and manipulate your data in Fabric Real-Time Intelligence.
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*[KQL Querysets](/fabric/real-time-intelligence/create-query-set): KQL (Kusto Query Language) is a query language used in Azure Data Explorer and other Azure services. With KQL, you can write powerful queries to analyze and manipulate your data in Real-Time Intelligence.
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*[Real-Time Dashboards](/fabric/real-time-intelligence/dashboard-real-time-create): Fabric Real-Time Intelligence provides built-in support for creating real-time dashboards. These dashboards allow you to monitor and visualize your data in real-time, providing you with up-to-date insights and analytics. You can customize these dashboards to suit your specific needs and requirements.
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*[Real-Time Dashboards](/fabric/real-time-intelligence/dashboard-real-time-create): Real-Time Intelligence provides built-in support for creating real-time dashboards. These dashboards allow you to monitor and visualize your data in real-time, providing you with up-to-date insights and analytics. You can customize these dashboards to suit your specific needs and requirements.
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*[KustoTrender](https://github.com/Azure/azure-kusto-trender): A JavaScript library for custom time series dashboards
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* SDKs for Custom Applications: Fabric Real-Time Intelligence offers SDKs (Software Development Kits) for various programming languages, including [C#](/azure/data-explorer/kusto/api/netfx/about-the-sdk?context=%2Ffabric%2Fcontext%2Fcontext-rti&pivots=fabric), Java [Java](/azure/data-explorer/kusto/api/java/kusto-java-client-library?context=%2Ffabric%2Fcontext%2Fcontext-rti&pivots=fabric), and [Node.js](/azure/data-explorer/kusto/api/node/kusto-node-client-library?context=%2Ffabric%2Fcontext%2Fcontext-rti&pivots=fabric). These SDKs provide a set of libraries and tools that allow you to integrate Fabric Real-Time Intelligence into your custom applications. You can use these SDKs to perform data ingestion, querying, and other operations programmatically.
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* SDKs for Custom Applications: Fabric Real-Time Intelligence offers SDKs (Software Development Kits) for various programming languages, including [C#](/azure/data-explorer/kusto/api/netfx/about-the-sdk?context=%2Ffabric%2Fcontext%2Fcontext-rti&pivots=fabric), Java [Java](/azure/data-explorer/kusto/api/java/kusto-java-client-library?context=%2Ffabric%2Fcontext%2Fcontext-rti&pivots=fabric), and [Node.js](/azure/data-explorer/kusto/api/node/kusto-node-client-library?context=%2Ffabric%2Fcontext%2Fcontext-rti&pivots=fabric). These SDKs provide a set of libraries and tools that allow you to integrate Real-Time Intelligence into your custom applications. You can use these SDKs to perform data ingestion, querying, and other operations programmatically.
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### Migration of the Time Series Model
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Time Series Insights Gen 2 offers the capability to contextualize raw time series data using a model known as the [Time Series Model](/azure/time-series-insights/concepts-model-overview), which defines assets and their relationships as hierarchies. Leveraging the [Kusto Graph Semantics](/azure/data-explorer/graph-overview?context=%2Ffabric%2Fcontext%2Fcontext-rti&pivots=fabric) within Eventhouse, it is possible to model the Time Series Model hierarchy as a graph. This enables the traversal of assets within the graph and facilitates the contextualization of time series data.
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> [!NOTE]
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> Your Time Series Insights resources will be automatically deleted if you cannot migrate from Time Series Insights to Fabric Real-Time Intelligence by 7 July 2024. You’ll be able to access Gen2 data in your storage account. However, you can only perform management operations (such as updating storage account settings, getting storage account properties/keys, and deleting storage accounts) through Azure Resource Manager.
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> Your Time Series Insights resources will be automatically deleted if you cannot migrate from Time Series Insights to Real-Time Intelligence by 7 July 2024. You’ll be able to access Gen2 data in your storage account. However, you can only perform management operations (such as updating storage account settings, getting storage account properties/keys, and deleting storage accounts) through Azure Resource Manager.
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