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SQL Lineage Tools for Oracle
Dataedo
Dataedo allows you to extract lineage automatically or design flows manually and visualize how data moves through the system with interactive diagrams.
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SQLFlow
SQLFlow is an online SQL data lineage tool that visually represents the overall flow of data. It offers automated SQL data lineage analysis across Databases, ETL, Business Intelligence, Cloud, and Hadoop environments by parsing SQL Script and stored procedure. It enables impact analysis at a granular level, drilling down into table, column, and query-level lineage.
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Collibra Data Lineage
Collibra Data Lineage extracts and maintains lineage automatically from source systems, SQL dialects, ETL tools, and BI tools. It provides detailed technical lineage at the table, column, transformation, and SQL query levels to quickly understand the impact of potential changes. Moreover, its interactive lineage diagram shows a summary of lineage from source to destination.
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OvalEdge
OvalEdge provides a powerful and automated way to visualize your Data Flow Effortlessly. Its algorithms build the lineage from the most accurate source, source code. It crawls the source code from various connectors. After that, it parses its language like SQL, PL/SQL, XML, etc., and then builds the lineage.
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OpenMetadata
OpenMetadata is a single place to discover, collaborate and get your data right. It supports a comprehensive lineage for all data assets by capturing the relation between entities. It traces the path of data across tables, pipelines, and dashboards, while the manual lineage helps augment the lineage captured from machine metadata with user knowledge.
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Datafold
Datafold provides plug and play column-level lineage for the modern data stack. It analyzes every SQL statement in your data warehouse and produces a graph of dependencies. It provides a high-level overview of your pipelines, zoom in on particular tables, trace flow on a columnal level, and see the SQL statements for each step.
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Secoda
Secoda provides end-to-end data lineage across your entire data stack. It 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.
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Dremio
Dremio is a SQL Lakehouse Platform that offers effective data lineage support, as the relationships between your data sources, virtual datasets, and all your queries are maintained in Dremio’s data graph, telling you exactly where each dataset came from. It provides self-service analytics with unified data access, modern and intuitive U/I, semantic layer, and built for SQL.
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MANTA
Manta offers a powerful scanner for the Microsoft SQL Server technology. Once configured, Manta can automatically connect to the MS SQL resource for extracting and analyzing the pertinent metadata within the selected databases. This metadata includes but is not limited to tables, views, indexes, SQL procedures, schemas, databases, columns, and triggers. Then, Manta can parse all the SQL programming code and logic stored within. This allows Manta to generate lineage down to the column level while showing all transformation logic associated with individual column elements.
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Azure Purview
Microsoft Purview provides a unified data governance solution that offers many capabilities, one of which is showing lineage between datasets created by data processes. It supports automated asset-level lineage for the datasets and processes, while manual lineage allows you to document lineage metadata for sources where automation isn't yet supported without using any code.
Metadata collected in Microsoft Purview from enterprise data systems are stitched across to show an end-to-end data lineage.
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Fivetran
Fivetran is the automated data movement platform moving data out of, into and across your cloud data platforms. It allows you to monitor data movement, logs, and status from connector extract to successful warehouse load through modeling — all in one data lineage graph (DLGs). DLGs show the dependencies between your dbt models so that you can track the flow of data from your connectors to your destination.
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