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Create a new lakehouse and assign a name to it.In the top cell, add the following code snippet:import pandas as pdfrom tqdm.auto import tqdmbase = " load list of tablesdf_tables = pd.read_csv(f"{base}/adventureworks.csv", names=["table"])for table in (pbar := tqdm(df_tables['table'].values)): pbar.set_description(f"Uploading {table} to lakehouse") # download df = pd.read_parquet(f"{base}/{table}.parquet") # save as lakehouse table spark.createDataFrame(df).write.mode('overwrite').saveAsTable(table)Select Run all.After a few minutes, the lakehouse populates with the necessary data.Create an AI skillTo create a new AI skill, navigate to your workspace and select the + New Item button, as shown in this screenshot:In the All items tab, search for AI skill to locate the appropriate option. Once selected, a prompt asks you to provide a name for your AI skill, as shown in this screenshot:After you enter the name, proceed with the following steps to align the AI skill with your specific requirements.Select the dataSelect the lakehouse you created in the previous step, and then select Add. Once the lakehouse is added as a data source, the Explorer pane on the left side of the AI skill page shows the lakehouse name. Select the lakehouse to view all available tables. Use the checkboxes to select the tables you want to make available to the AI. For this scenario, select these tables:dimcustomerdimdatedimgeographydimproductdimproductcategorydimpromotiondimresellerdimsalesterritoryfactinternetsalescactresellersalesProvide instructionsTo add AI instructions, select the AI instructions button to open the AI instructions pane on the right. You can add the following instructions.The AdventureWorksLH data source contains information from three tables:dimcustomer, for detailed customer demographics and contact informationdimdate, for date-related data - for example, calendar and fiscal informationdimgeography, for geographical details including city names and country region codes.Use this data source for queries and analyses that involve customer details, time-based events, and geographical locations.Provide examplesTo add example queries, select the Example queries button to open the example queries pane on the right. 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. AI skill example with the AdventureWorks dataset (preview) Article02/25/2025 In this article -->This article describes how to set up an AI skill, using a lakehouse as a data source. To illustrate the process, we first create a lakehouse, and then add data to it. Then, we create an AI skill and configure the lakehouse as its data source. If you already have a Power BI semantic model (with the necessary read/write permissions), a warehouse, or a KQL database, you can follow the same steps after you create the AI skill to add your data sources. While the steps shown here focus on the lakehouse, the process is similar for other data sources—you just need to make adjustments based on your specific selection.ImportantThis feature is in preview.PrerequisitesA paid F64 or higher Fabric capacity resourceAI skill tenant switch is enabled.Copilot tenant switch is enabled.Cross-geo processing for AI is enabled.Cross-geo storing for AI is enabled.A warehouse, lakehouse, Power BI semantic models, and KQL databases with data.Power BI semantic models via XMLA endpoints tenant switch is enabled for Power BI semantic model data sources.Create a lakehouse with AdventureWorksLHFirst, create a lakehouse and populate it with the necessary data.If you already have an instance of AdventureWorksLH in a lakehouse (or a warehouse), you can skip this step. If not, you can use the following instructions from a Fabric notebook to populate the lakehouse with the data.Create a new notebook in the workspace where you want to create your AI skill.On the left side of the Explorer pane, select + Data sources. This option allows you to add an existing lakehouse or creates a new lakehouse. For sake of clarity,

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Applying business logics, and loading it into multiple destinations (such as Azure SQL DB, ADX, and a lakehouse) in preparation for their respective reporting teams.Mary is an experienced Power Query user, and the data volume is in the low to medium range to achieve desired performance. Dataflows provide no-code or low-code interfaces for ingesting data from hundreds of data sources. With dataflows, you can transform data using 300+ data transformation options, and write the results into multiple destinations with an easy to use, highly visual user interface. Mary reviews the options and decides that it makes sense to use Dataflow Gen 2 as her preferred transformation option.Scenario3Adam is a data engineer working for a large retail company that uses a lakehouse to store and analyze its customer data. As part of his job, Adam is responsible for building and maintaining the data pipelines that extract, transform, and load data into the lakehouse. One of the company's business requirements is to perform customer review analytics to gain insights into their customers' experiences and improve their services.Adam decides the best option is to use Spark to build the extract and transformation logic. Spark provides a distributed computing platform that can process large amounts of data in parallel. He writes a Spark application using Python or Scala, which reads structured, semi-structured, and unstructured data from OneLake for customer reviews and feedback. The application cleanses, transforms, and writes data to Delta tables in the lakehouse. The data is then ready to be used for downstream analytics.Related contentHow to copy data using copy activityQuickstart: Create your first dataflow to get and transform dataHow to create an Apache Spark job definition in Fabric --> Feedback Additional resources In this article Processing System? A File Processing System is a type of data handling method that uses files for data storage and management.How does a File Processing System differ from a DBMS? A DBMS typically uses a database, enabling complex data relations, while a File Processing System lacks complex relationships between data and is often less secure.Can File Processing Systems be used with data lakehouses? Yes, they can serve as initial data ingestion systems in a data lakehouse setup.What are the limitations of File Processing Systems? Main limitations include data duplication, poor data integrity, lack of complex data relationships, and issues related to scale and performance.How does Dremio's technology surpass File Processing Systems? Dremio's data lakehouse handles large data volumes, provides superior querying speed, and offers advanced security, making it more suitable for complex data solutions.GlossaryFile Processing System: A data management method utilizing files for storing and handling data.Data Lakehouse: An architecture combining the best features of data lakes and data warehouses, providing a unified system for all kinds of data.DBMS: Database Management System, a software that manages databases, allowing for data storage, retrieval, and manipulation.Data Ingestion: The process of obtaining, importing, and processing data for later use or storage in a database.Metadata: Data that provides information about other data.

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And selecting View Details. Then, copy and paste the SQL connection string.The entire endpoint name looks similar to the following example:x6eps4xrq2xudenlfv6naeo3i4-l27nd6wdk4oephe4gz4j7mdzka.datawarehouse.pbidedicated.windows.netWorkaround: Split dataflow in a separate ingest and load dataflowIf you're unable to update the firewall rules, you can split the dataflow into two separate dataflows. The first dataflow is responsible for ingesting the data into the staging lakehouse. The second dataflow is responsible for loading the data from the staging lakehouse into the data destination. This workaround isn't ideal, as it requires the use of two separate dataflows, but it can be used as a temporary solution until the firewall rules can be updated.To implement this workaround, follow these steps:Remove the data destination from your current dataflow that ingests data via your gateway.Create a new dataflow that uses the dataflow connector to connect to the ingested dataflow. This dataflow is responsible for ingesting the data from staging into the data destination.Set the data destination to be the data destination of your choice for this new dataflow.Optionally, you can disable staging for this new dataflow. This change prevents the data from being copied to the staging lakehouse again and instead copies the data directly from the ingested dataflow to the data destination. --> Feedback Additional resources In this article

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(V3.0) cluster, click Create Privileged Account. For a Data Lakehouse Edition (V3.0) cluster, click Create Account. In the Create Privileged Account or Create Account panel, configure the parameters described in the following table. ParameterDescriptionAccountThe name of the privileged account. Enter a name that meets the on-screen requirements. Account TypeFor a Data Warehouse Edition (V3.0) cluster, this parameter is automatically set to Privileged Account. For a Data Lakehouse Edition (V3.0) cluster, select Privileged Account. New PasswordThe password of the privileged account. Enter a password that meets the on-screen requirements. Confirm PasswordEnter the password of the privileged account again. DescriptionOptional. The description that is used to identify the account for future management. Click OK. Reset the password of a privileged accountIf you forget the password of a privileged account, you can reset the password in the console. Important For data security purposes, we recommend that you change the account password on a regular basis. On the Accounts page, find the privileged account and click Change Password in the Actions column. In the dialog box that appears, enter and confirm a new password as prompted, and then click OK. Create and grant permissions to a standard accountConsole operationsYou can create and grant permissions to a standard account in the console only for Data Lakehouse Edition (V3.0) clusters. Log on to the AnalyticDB for MySQL console. In the upper-left corner of the page, select a region. In the left-side navigation pane, click Clusters.On the Data Lakehouse Edition (V3.0) tab, find the cluster that you want to manage and click its ID. In the left-side navigation pane, click Accounts. Click Create Account. In the Create Account panel, configure the parameters described in the following table. ParameterDescriptionAccountThe name of the standard account. Enter a name that meets the on-screen requirements. Account TypeSelect Standard Account.New PasswordThe password of the standard account. Enter a password that meets the on-screen requirements. Confirm PasswordEnter the password of the standard account again. DescriptionOptional. The description that is used to identify the account for future management. Click OK. Find the created account and click Permissions in the Actions column to grant permissions. Lakehouse 3D models ready to view, buy, and download for free. Popular Lakehouse 3D models View all Download Lakehouse 3D for free. Lakehouse 3D - This 3D screensaver and wallpaper features beautiful butterflies, birds, frogs, swimming fish, dragonflies

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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. Ingest data into an Azure Databricks lakehouse Article01/24/2025 In this article -->Azure Databricks offers various methods for ingesting data into a lakehouse backed by Delta Lake. This article lists supported ingestion tools and guidance on which method to use based on criteria like data source and latency.Ingestion methodsYou can ingest data into Databricks using the following methods:Batch ingestion of a set of data rows for infrequent processingStreaming ingestion of individual data rows or sets of data rows as they arrive for real-time processingIngested data is loaded into Delta tables that can then be used across your downstream data and AI use cases. Because of Databricks’ Lakehouse architecture, you do not need to duplicate your data across use cases, and you can leverage Unity Catalog for centralized access control, auditing, lineage, and data discovery across all of your data.Batch ingestionWith batch ingestion you load data as sets (or batches) of rows into Databricks often based on a schedule (for example, every day) or triggered manually. This represents the “extract” piece of traditional extract, transform, load (ETL) use cases. You can use batch ingestion to load data from:Local files like CSVsCloud object storage, including Amazon S3, Azure Data Lake Storage, and Google Cloud StorageSaaS applications like Salesforce and databases like SQL ServerBatch ingestion supports a wide range of file source formats, including CSV, TSV, JSON, XML,