Data lineage is the process of tracking the origin, movement, transformation, and usage of data across an organization's ecosystem. It helps teams understand where data comes from, how it flows through systems, and how it changes over time.
The ML dashboard provides a centralized interface for building predictive solutions tailored to various analytical needs. You can create the following types of models:
From a workbook, click Actions > ML Dashboard. The ML Dashboard page is displayed.
From a workbook, click Actions > ML Dashboard. The ML Dashboard page is displayed.
From a workbook, click Actions > ML Dashboard. The ML Dashboard page is displayed.
Data source lineage shows you all the different pipelines and workbooks that draw data from that specific source.
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Impact analysis is a capability in the Data Lineage module to help you assess the downstream effects of potentially disruptive actions within the platform. This feature is designed to increase the awareness and reduce unintended consequences when altering or removing data entities.
The data lineage views in DataGOL offer several features to help you manage and understand the details:
Pipeline lineage shows you exactly where the data enters the pipeline, the transformations/queries it goes through within the pipeline, and where it ultimately exits (the destination data warehouse or table).
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This guide aims to provide troubleshooting steps for common Spark errors encountered within the DataGOL platform, specifically related to Apache Kyuubi and Spark Driver failures.
Workbook lineage traces the data back from its original source, showing any materialized views along the way.
Workbook lineage traces the data back from its original source, showing any materialized views along the way.