alicloud-data-lake-dlf-nextManage Alibaba Cloud Data Lake Formation (DlfNext) via OpenAPI/SDK. Use for listing resources, creating or updating configurations, querying status, and troubleshooting workflows for this product.
Install via ClawdBot CLI:
clawdbot install alicloud-data-lake-dlf-nextCategory: service
Use Alibaba Cloud OpenAPI (RPC) with official SDKs or OpenAPI Explorer to manage resources for Data Lake Formation.
1) Confirm region, resource identifiers, and desired action.
2) Discover API list and required parameters (see references).
3) Call API with SDK or OpenAPI Explorer.
4) Verify results with describe/list APIs.
1) Environment variables: ALICLOUD_ACCESS_KEY_ID / ALICLOUD_ACCESS_KEY_SECRET / ALICLOUD_REGION_ID
Region policy: ALICLOUD_REGION_ID is an optional default. If unset, decide the most reasonable region for the task; if unclear, ask the user.
2) Shared config file: ~/.alibabacloud/credentials
DlfNext2025-03-101) Inventory/list: prefer List / Describe APIs to get current resources.
2) Change/configure: prefer Create / Update / Modify / Set APIs for mutations.
3) Status/troubleshoot: prefer Get / Query / Describe*Status APIs for diagnosis.
Use metadata-first discovery before calling business APIs:
python scripts/list_openapi_meta_apis.pyOptional overrides:
python scripts/list_openapi_meta_apis.py --product-code <ProductCode> --version <Version>The script writes API inventory artifacts under the skill output directory.
If you need to save responses or generated artifacts, write them under:
output/alicloud-data-lake-dlf-next/
references/sources.mdGenerated Mar 1, 2026
IT administrators use this skill to list and describe existing Data Lake Formation resources like catalogs, databases, and tables across regions. This helps in auditing resource usage, tracking configurations, and ensuring compliance with organizational data governance policies.
Data engineers automate the creation and updating of Data Lake Formation configurations, such as setting up new catalogs or modifying table schemas. This streamlines data pipeline setups, reduces manual errors, and accelerates deployment cycles for big data projects.
Support teams query the status of Data Lake Formation workflows to diagnose issues like failed jobs or performance bottlenecks. This enables quick identification of problems, reducing downtime and improving operational efficiency in data processing environments.
Cloud architects manage Data Lake Formation resources across multiple regions to ensure data availability and disaster recovery. This involves using list and update APIs to synchronize configurations, supporting global data analytics initiatives and regulatory requirements.
Companies offer managed services to handle Data Lake Formation setup, monitoring, and optimization for clients. This generates revenue through subscription fees, providing expertise in cloud data management and reducing client operational overhead.
Consulting firms use this skill to audit and configure Data Lake Formation for clients, ensuring compliance with data policies. Revenue comes from project-based fees, helping organizations implement best practices in data security and governance.
SaaS providers integrate Data Lake Formation management into their analytics platforms, enabling seamless data ingestion and processing. This drives revenue by enhancing platform capabilities and attracting enterprise customers with scalable data solutions.
💬 Integration Tip
Prioritize setting environment variables for AccessKey to avoid credential issues, and use the provided scripts for API discovery to ensure compatibility with the latest OpenAPI versions.
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