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31 бер., 23 - 2 квіт., 23
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APPLIES TO:
Azure Data Factory
Azure Synapse Analytics
Порада
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In this article, you use the Azure Data Factory user interface to create a change data capture (CDC) resource. The resource picks up changed data from an Azure Data Lake Storage Gen2 source and adds it to Azure SQL Database in real time.
In this article, you learn how to:
You can modify and expand the configuration pattern in this article.
Before you begin the procedures in this article, make sure that you have these resources:
Go to the Author pane in your data factory. Below Pipelines, a new top-level artifact called Change Data Capture (preview) appears.
Hover over Change Data Capture (preview) until three dots appear. Then select Change Data Capture (preview) Actions.
Select New CDC (preview). This step opens a flyout to begin the guided process.
You're prompted to name your CDC resource. By default, the name is "adfcdc" with a number that increments by 1. You can replace this default name with a name that you choose.
Use the dropdown list to choose your data source. For this article, select DelimitedText.
You're prompted to select a linked service. Create a new linked service or select an existing one.
Use the Source settings area to optionally set advanced source configurations, including column and row delimiters.
If you don't manually edit these source settings, they're set to the defaults.
Use the Browse button to select your source data folder.
After you select a folder path, select Continue to set your data target.
You can choose to add multiple source folders by using the plus (+) button. The other sources must also use the same linked service that you already selected.
Select a Target type value by using the dropdown list. For this article, select Azure SQL Database.
You're prompted to select a linked service. Create a new linked service or select an existing one.
For Target tables, you can create a new target table or select an existing one:
To create a target table, select the New entities tab, and then select Edit new tables.
To select an existing table, select the Existing entities tab, and then use the checkbox to choose a table. Use the Preview button to view your table data.
If existing tables at the target have matching names, they're selected by default under Existing entities. If not, new tables with matching names are created under New entities. Additionally, you can edit new tables by using the Edit new tables button.
You can use the checkboxes to choose multiple target tables from your SQL database. After you finish choosing target tables, select Continue.
A new tab for capturing change data appears. This tab is the CDC studio, where you can configure your new resource.
A new mapping is automatically created for you. You can update the Source Table and Target Table selections for your mapping by using the dropdown lists.
After you select your tables, their columns are mapped by default with the Auto map toggle turned on. Auto map automatically maps the columns by name in the sink, picks up new column changes when the source schema evolves, and flows this information to the supported sink types.
If you want to use Auto map and not change any column mappings, go directly to step 18.
If you want to enable the column mappings, select the mappings and turn off the Auto map toggle. Then, select the Column mappings button to view the mappings.
You can switch back to automatic mapping anytime by turning on the Auto map toggle.
View your column mappings. Use the dropdown lists to edit your column mappings for Mapping method, Source column, and Target column.
From this page, you can:
When your mapping is complete, select the arrow button to return to the main CDC canvas.
You can add more source-to-target mappings in one CDC artifact. Use the Edit button to add more data sources and targets. Then, select New mapping and use the drop-down lists to set a new source and target. You can turn Auto map on or off for each of these mappings independently.
After your mappings are complete, set your CDC latency by using the Set Latency button.
Select the latency of your CDC, and then select Apply to make the changes.
By default, latency is set to 15 minute. The example in this article uses the Real-time option for latency. Real-time latency continuously picks up changes in your source data in intervals of less than 1 minute.
For other latencies (for example, if you select 15 minutes), your change data capture will process your source data and pick up any changed data since the last processed time.
Примітка
If support is extended to streaming data integration (Azure Event Hubs and Kafka data sources), the latency will be set to Real-time by default.
After you finish configuring your CDC, select Publish all to publish your changes.
Примітка
If you don't publish your changes, you won't be able to start your CDC resource. The Start button in the next step will be unavailable.
Select Start to start running your change data capture.
Open the Monitor pane by using either of these methods:
Select Change Data Capture (preview) to view your CDC resources.
The Change Data Capture pane shows the Source, Target, Status, and Last processed information for your change data capture.
Select the name of your CDC to see more details. You can see how many changes (insert, update, or delete) were read and written, along with other diagnostic information.
If you set up multiple mappings in your change data capture, each mapping appears as a different color. Select the bar to see specific details for each mapping, or use the diagnostics information at the bottom of the pane.
Подія
31 бер., 23 - 2 квіт., 23
Найбільша подія навчання Fabric, Power BI і SQL. 31 березня – 2 квітня. Щоб заощадити 400 грн, скористайтеся кодом FABINSIDER.
Реєструйтеся сьогодніНавчання
Модуль
Відстеження даних і синхронізація з базою даних Azure SQL - Training
Модуль відстеження даних Azure SQL, який охоплює відстеження змін даних. Модуль досліджує такі інструменти, як збирання даних про зміни (CDC) і відстеження змін.
Сертифікація
Microsoft Certified: Azure Data Engineer Associate - Certifications
Демонстрація розуміння поширених завдань з проектування даних для реалізації та керування навантаженнями на проектування даних у Microsoft Azure за допомогою низки служб Azure.
Документація
Change Data Capture Resource - Azure Data Factory
Learn more about the change data capture resource in Azure Data Factory.
Change data capture - Azure Data Factory & Azure Synapse
Learn about change data capture in Azure Data Factory and Azure Synapse Analytics.
Get step-by-step instructions on how to capture changed data with schema evolution from Azure SQL Database to a Delta sink by using a change data capture (CDC) resource.