Best Data Lineage Tools for Hadoop
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Compare the Top Data Lineage Tools that integrate with Hadoop as of September 2025
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This a list of Data Lineage tools that integrate with Hadoop. Use the filters on the left to add additional filters for products that have integrations with Hadoop. View the products that work with Hadoop in the table below.
What are Data Lineage Tools for Hadoop?
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 for Hadoop currently available using the table below. This list is updated regularly.
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1
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. -
2
Ataccama ONE
Ataccama
Ataccama reinvents the way data is managed to create value on an enterprise scale. Unifying Data Governance, Data Quality, and Master Data Management into a single, AI-powered fabric across hybrid and Cloud environments, Ataccama gives your business and data teams the ability to innovate with unprecedented speed while maintaining trust, security, and governance of your data. -
3
PHEMI Health DataLab
PHEMI Systems
The PHEMI Trustworthy Health DataLab is a unique, cloud-based, integrated big data management system that allows healthcare organizations to enhance innovation and generate value from healthcare data by simplifying the ingestion and de-identification of data with NSA/military-grade governance, privacy, and security built-in. Conventional products simply lock down data, PHEMI goes further, solving privacy and security challenges and addressing the urgent need to secure, govern, curate, and control access to privacy-sensitive personal healthcare information (PHI). This improves data sharing and collaboration inside and outside of an enterprise—without compromising the privacy of sensitive information or increasing administrative burden. PHEMI Trustworthy Health DataLab can scale to any size of organization, is easy to deploy and manage, connects to hundreds of data sources, and integrates with popular data science and business analysis tools. -
4
Kylo
Teradata
Kylo is an open source enterprise-ready data lake management software platform for self-service data ingest and data preparation with integrated metadata management, governance, security and best practices inspired by Think Big's 150+ big data implementation projects. Self-service data ingest with data cleansing, validation, and automatic profiling. Wrangle data with visual sql and an interactive transform through a simple user interface. Search and explore data and metadata, view lineage, and profile statistics. Monitor health of feeds and services in the data lake. Track SLAs and troubleshoot performance. Design batch or streaming pipeline templates in Apache NiFi and register with Kylo to enable user self-service. Organizations can expend significant engineering effort moving data into Hadoop yet struggle to maintain governance and data quality. Kylo dramatically simplifies data ingest by shifting ingest to data owners through a simple guided UI. -
5
Apache Atlas
Apache Software Foundation
Atlas is a scalable and extensible set of core foundational governance services – enabling enterprises to effectively and efficiently meet their compliance requirements within Hadoop and allows integration with the whole enterprise data ecosystem. Apache Atlas provides open metadata management and governance capabilities for organizations to build a catalog of their data assets, classify and govern these assets and provide collaboration capabilities around these data assets for data scientists, analysts and the data governance team. Pre-defined types for various Hadoop and non-Hadoop metadata. Ability to define new types for the metadata to be managed. Types can have primitive attributes, complex attributes, object references; can inherit from other types. Instances of types, called entities, capture metadata object details and their relationships. REST APIs to work with types and instances allow easier integration. -
6
Secuvy AI
Secuvy
Secuvy is a next-generation cloud platform to automate data security, privacy compliance and governance via AI-driven workflows. Best in class data intelligence especially for unstructured data. Secuvy is a next-generation cloud platform to automate data security, privacy compliance and governance via ai-driven workflows. Best in class data intelligence especially for unstructured data. Automated data discovery, customizable subject access requests, user validations, data maps & workflows for privacy regulations such as ccpa, gdpr, lgpd, pipeda and other global privacy laws. Data intelligence to find sensitive and privacy information across multiple data stores at rest and in motion. In a world where data is growing exponentially, our mission is to help organizations to protect their brand, automate processes, and improve trust with customers. With ever-expanding data sprawls we wish to reduce human efforts, costs & errors for handling Sensitive Data.
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