Best Data Lineage Tools

Compare the Top Data Lineage Tools as of February 2026

What are Data Lineage Tools?

Data lineage tools are software solutions designed to track and visualize the flow of data through various stages of its lifecycle, from origin to destination. These tools help organizations understand the data's journey, transformations, and dependencies across different systems and processes. They offer features such as data mapping, impact analysis, and auditing to ensure data accuracy, compliance, and governance. By providing detailed insights into data movement and transformations, data lineage tools enable better decision-making, troubleshooting, and optimization of data workflows. They are essential for maintaining data integrity and transparency in complex data environments. Compare and read user reviews of the best Data Lineage tools currently available using the table below. This list is updated regularly.

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    dbt

    dbt

    dbt Labs

    dbt helps data teams transform raw data into trusted, analysis-ready datasets faster. With dbt, data analysts and data engineers can collaborate on version-controlled SQL models, enforce testing and documentation standards, lean on detailed metadata to troubleshoot and optimize pipelines, and deploy transformations reliably at scale. Built on modern software engineering best practices, dbt brings transparency and governance to every step of the data transformation workflow. Thousands of companies, from startups to Fortune 500 enterprises, rely on dbt to improve data quality and trust as well as drive efficiencies and reduce costs as they deliver AI-ready data across their organization. Whether you’re scaling data operations or just getting started, dbt empowers your team to move from raw data to actionable analytics with confidence.
    Starting Price: $100 per user/ month
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  • 2
    AnalyticsCreator

    AnalyticsCreator

    AnalyticsCreator

    Enhance data governance with comprehensive lineage tracking capabilities, offering clear visibility into the origin and transformations of your data. This improved transparency ensures compliance with auditable lineage trails and facilitates faster root cause analysis for data quality issues. Quickly identify and resolve data quality problems with actionable insights. With AnalyticsCreator, improve transparency, compliance, and data trust by providing a detailed lineage trail for your entire data ecosystem. Empower teams to perform impact analysis and make informed decisions faster with a visual overview of data dependencies and flow.
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  • 3
    Sifflet

    Sifflet

    Sifflet

    Automatically cover thousands of tables with ML-based anomaly detection and 50+ custom metrics. Comprehensive data and metadata monitoring. Exhaustive mapping of all dependencies between assets, from ingestion to BI. Enhanced productivity and collaboration between data engineers and data consumers. Sifflet seamlessly integrates into your data sources and preferred tools and can run on AWS, Google Cloud Platform, and Microsoft Azure. Keep an eye on the health of your data and alert the team when quality criteria aren’t met. Set up in a few clicks the fundamental coverage of all your tables. Configure the frequency of runs, their criticality, and even customized notifications at the same time. Leverage ML-based rules to detect any anomaly in your data. No need for an initial configuration. A unique model for each rule learns from historical data and from user feedback. Complement the automated rules with a library of 50+ templates that can be applied to any asset.
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    Datameer

    Datameer

    Datameer

    Datameer revolutionizes data transformation with a low-code approach, trusted by top global enterprises. Craft, transform, and publish data seamlessly with no code and SQL, simplifying complex data engineering tasks. Empower your data teams to make informed decisions confidently while saving costs and ensuring responsible self-service analytics. Speed up your analytics workflow by transforming datasets to answer ad-hoc questions and support operational dashboards. Empower everyone on your team with our SQL or Drag-and-Drop to transform your data in an intuitive and collaborative workspace. And best of all, everything happens in Snowflake. Datameer is designed and optimized for Snowflake to reduce data movement and increase platform adoption. Some of the problems Datameer solves: - Analytics is not accessible - Drowning in backlog - Long development
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    Databricks Data Intelligence Platform
    The Databricks Data Intelligence Platform allows your entire organization to use data and AI. It’s built on a lakehouse to provide an open, unified foundation for all data and governance, and is powered by a Data Intelligence Engine that understands the uniqueness of your data. The winners in every industry will be data and AI companies. From ETL to data warehousing to generative AI, Databricks helps you simplify and accelerate your data and AI goals. Databricks combines generative AI with the unification benefits of a lakehouse to power a Data Intelligence Engine that understands the unique semantics of your data. This allows the Databricks Platform to automatically optimize performance and manage infrastructure in ways unique to your business. The Data Intelligence Engine understands your organization’s language, so search and discovery of new data is as easy as asking a question like you would to a coworker.
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    Aggua

    Aggua

    Aggua

    Aggua is a data fabric augmented AI platform that enables data and business teams Access to their data, creating Trust and giving practical Data Insights, for a more holistic, data-centric decision-making. Instead of wondering what is going on underneath the hood of your organization's data stack, become immediately informed with a few clicks. Get access to data cost insights, data lineage and documentation without needing to take time out of your data engineer's workday. Instead of spending a lot of time tracing what a data type change will break in your data pipelines, tables and infrastructure, with automated lineage, your data architects and engineers can spend less time manually going through logs and DAGs and more time actually making the changes to infrastructure.
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