A guide for handling Dataphin data-masking requests, including checks for data classification and security levels. It identifies what the public API can support and produces the parameters needed for delivery, but does not create masking rules through undocumented internal APIs.
A way to call already-published Dataphin data-service APIs from Python. It includes a script that creates the required HMAC-SHA256 signature using only Python’s standard library, so no official SDK download is needed.
A command-line workflow for creating and publishing data-service APIs from SQL. It finds the project, creates the API, publishes it to production, and checks whether publishing succeeded.
A workflow for managing data-service applications and linking them to API permissions. It covers creating an application, adding members, granting access, checking the result, and obtaining AppKey/AppSecret credentials.
A monitoring workflow for Dataphin data-service APIs. It shows call totals, trends over time, detailed request logs, and analysis of how errors affect operations.
A way to run temporary SQL, scripts, or metadata queries in Dataphin without creating a permanent scheduled task. It supports different database types through the required operator settings.
A method for finding the virtual root node of a Dataphin tenant project. The virtual root node is a default starting point used when a new task needs an upstream dependency.
A lookup workflow for finding an offline task when you know only its name. It first finds the task’s FileId, then retrieves its code, schedule, dependencies, and related identifiers such as NodeId and DagId.
A skill for submitting an offline computing task from the development area to the operations area. It handles different task types and execution engines, such as Python, Shell, HiveSQL, and MaxComputeSQL.
A skill for updating an existing offline computing task, including its code, data source, schedule, and dependencies. An offline computing task runs scheduled data processing rather than serving live requests.
A skill for managing and querying knowledge-graph data. A knowledge graph stores entities and the relationships between them, and Cypher is a language for querying such graphs.
A Dataphin skill for managing a knowledge-graph schema, which defines entity types, relationships, and properties. Schemas can be exported or imported as YAML and published to the graph engine.
A read-only Dataphin skill for exploring knowledge-graph data through Cypher queries, neighbor traversal, and keyword or semantic search. A knowledge graph stores connected entities and relationships.
A Dataphin skill for backfilling historical data by running a scheduling node for a chosen business date. Backfilling means processing past dates that were missed or need correction.
A Dataphin skill for rerunning failed or incomplete scheduled task instances, either individually, in batches, or together with their downstream tasks.
A Dataphin skill for creating data-integration pipelines that move or transform data between systems. ETL means extracting, transforming, and loading data.
A tool for changing existing DataWorks integration pipelines, which move data between systems. It updates scheduling, resources, field mappings, filters, and other pipeline settings before resubmitting the full configuration.
A lifecycle management tool for Dataphin unstructured datasets, which store information that does not fit ordinary rows and columns. It supports dataset design, creation, lookup, updates, and deletion, including vector-oriented datasets.
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