DataWind supports integration with the lakehouse analytics service LAS to obtain data, create datasets, and perform further analysis.
If your BytePlus account has purchased LAS resources, a default LAS storage data connection will appear when you create a new dataset. This connection can be used for modeling and data extraction.

(1) Click Data Preparation -> Dataset, click the plus sign on the left, and select "Create dataset".

(2) Click LAS.

(3) Choose to create a table.

• On the model configuration page, select a custom SQL or a table from the database on the left, and drag it to the canvas on the right.
• Only tables that you have permission to access are displayed in the Hive table list.

(4) To join LAS data with other data, after selecting the LAS data source, click the green plus sign again to add a new data source.

3.1 Model configuration #

In model configuration, you can set field retention, data filtering, extraction parameter settings, and more. For details, see Model Configuration.

3.2 Data filtering #

In data filtering, you can filter data from the data source and retain only the data that meets the specified conditions. For details, see Data Filtering.

3.3 Field configuration #

Field configuration allows you to add complex calculated fields based on model fields or perform other configurations. For details, see Field Configuration.

3.4 Synchronization settings #

In synchronization settings, you can configure synchronization frequency, time, dependency settings, synchronization monitoring, performance settings, and more. For details, see Synchronization Settings.

3.5 Recommended dependencies #

This product provides flexible task scheduling dependency configuration functionality. Scheduling dependencies usually refer to the upstream and downstream relationships between periodic scheduling nodes during data synchronization and data development. By scheduling tasks for upstream and downstream nodes in order according to their dependencies, downstream nodes will only start running after upstream nodes have run successfully, ensuring the timely production of valid business data.
Recommended dependency refers to the system's ability to automatically construct data source partition detection tasks and automatically configure scheduling dependencies in LAS data sources with partitions. The actual execution of the task instance for the day is only triggered when the detection task verifies that the upstream partition is ready. (Recommended dependency configuration is not supported for data sources without partitions.)
Note: After the system wakes up the Sensor task at the scheduled time, it checks every 5 minutes whether the specified partition table of the data source has been generated. If the partition table is generated, the current task is executed immediately. If the partition is not produced for more than 50 hours, the Sensor task will be marked as failed.

Sensor definition: A Sensor task mainly refers to a scheduled task generated by the system script to detect partitions, not the current task. For user-defined settings, the system will automatically detect and probe whether the partition table required by the task exists in the user's data source. If it exists, this means that the preparation of upstream data is complete and downstream data can start running.

The system will automatically discover dependency task Sensors that have already been created for the current table and then recommend them for selection. If there are no recommendations, the list will be empty, and the user can choose to configure them manually.
Configuration method: Confirm that the data source is a partitioned table, set the partition data filtering criteria in the model configuration, and in Synchronization Settings > Dependency Configuration > System Auto Configuration, you will see the recommended dependency.