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Data lineage refers to the historical record or lineage of a piece of data, tracing its origin, transformations, and movement throughout its lifecycle. It provides a detailed account of how data is sourced, processed, and integrated across various systems, applications, and processes. Data lineage includes information such as the data sources, data transformations, data quality checks, and the relationships and dependencies between different data elements. It helps to understand the data's lineage and its journey, ensuring transparency, trust, and compliance while facilitating data management, data governance, and decision-making processes.

Data lineage plays a crucial role in supporting data cataloging and data discovery efforts. By providing a comprehensive view of the origin, transformations, and flow of data, data lineage helps in accurately documenting and cataloging data assets. It enables users to understand the relationships and dependencies between datasets, identify potential data quality issues, and assess the trustworthiness of data. Data lineage also facilitates impact analysis by allowing users to trace the effects of changes or updates to datasets, enhancing the overall efficiency and effectiveness of data cataloging and data discovery processes.

Tracking data lineage offers several benefits for organizations and data management processes. Firstly, it enhances data trustworthiness and reliability by providing visibility into the origin and transformations of data, enabling users to understand and verify the data's accuracy and validity. Secondly, data lineage supports regulatory compliance and data governance efforts by enabling organizations to track and audit the lineage of sensitive or regulated data, ensuring data integrity and accountability. Additionally, data lineage facilitates data troubleshooting and root cause analysis, as it allows organizations to trace and identify issues or anomalies in the data flow. Moreover, data lineage aids in impact analysis, change management, and understanding the dependencies and impacts of data changes, supporting decision-making and reducing risks associated with data transformations. Overall, tracking data lineage improves data quality, transparency, and the efficiency of data management processes.

Yes, Secoda has an API for our docs, collections, lineage, dictionary, catalog and questions. The API can be accessed on our Business and Enterprise plans.

Yes, Secoda has the ability to connect with tools like Okta and Active Directory to manage your permissions from your RBAC tools. This way, you can confidently share Secoda knowing that the right people are seeing the right information.

Secoda integrations with Snowflake, Big Query, Redshift, Databricks, Postgres, Oracle, Microsoft SQL, MySQL and S3. For more information about our integration, you can visit our integration docs.

Secoda integrations with Tableau, Looker, Metabase, Redash, Mode, Sigma, Power BI and Google Data Studio. For more information about our integration, you can visit our integration docs.

Yes, Secoda works with both dbt Cloud and Core. Secoda is able to take the information from your YAML file and present it in your data discovery platform. In addition, Secoda also brings in dbt tests, tags, metrics and column level lineage into Secoda.

Secoda automates column and table level data lineage. In additional, Secoda also brings in tests, events and ETL into data lineage. All of Secoda's lineage is automated, but users can also manually contribute to lineage using Secoda's API.

Secoda does not have data monitoring or quality features built into the native integrations. Although Secoda doesn't have monitoring built natively, tools like great expectations and dbt tests can be brought into Secoda to manage data monitoring within the lineage UI.

Yes, Secoda integrates with Git and provides a version control for all changes made in Secoda in Git. This allows data teams to have a version of all metadata changes in Github or Gitlab and roll back changes if needed. In addition, data teams are able to merge changes to a metadata and review changes to their workspace in Git using Secoda.

Secoda does not pull in your data. Instead, Secoda pulls in metadata about your data. For a majority of the integrations connected to Secoda, this means Secoda pulls in resource names, popularity, lineage, queries, descriptions and frequent usage. For more information about our integration, you can visit our integration docs.