Data lineage is the process of identifying the source of data, recording the transformations and flows of data over time, and visualizing data flow from sources to end users.
Data lineage empowers data scientists, engineers, and data teams with granular visibility of data dynamics to help them and their organizations keep tabs on errors, carry out system migrations, execute process changes with lower risk, and more.
One challenge is documenting all that, and that’s where data lineage tools come in handy.
Data lineage documentation is a resource-intensive process in terms of personnel, funds, time, etc. There are two methods of documenting data lineage; descriptive and automated. Each method has its benefits and drawbacks, and each can be optimal depending on the nature of the data lineage project.
Descriptive data lineage uses a manual process to describe data lineage, and as such it has a large time and effort requirement. That’s where automated data lineage comes in.
Automated data lineage reduces time lost in the collection, storage and processing of chunks of data because it replaces manual processes plus human error with intelligent operations, software and even AI. Automated data lineage eases root cause analysis, helps organizations achieve and maintain compliance, ensures data accuracy and simplifies BI system migration.
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Here are some of the top data lineage tools, based on our analysis and review of the market.
Alation is an automated data lineage tool that offers a wide range of data intelligence solutions like data search and discovery, data stewardship, data governance, analytics, and transformation. It is AI-driven, with an advanced behavioral analysis engine to generate actionable insights.
Moreover, Alation profiles data and examines usage to ensure users have the accurate insights and offers insight into data usage by users. It also has built-in collaboration capabilities.
Con: Processing complex queries and large amounts of data impacts performance.
Pricing: Even though Alation offers a weekly live demo, you would have to contact their sales team for pricing information.
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Atlan is a modern data workspace that aims to empower data democratization within organizations while maintaining security and governance standards. The platform offers capabilities in areas such as data lineage and governance, data quality and profiling, data cataloging and discovery, and data exploration and integration.
In addition, Atlan has automated data profiling, discovery, an intuitive search interface and more features that help data teams free up huge chunks of their time to improve their productivity and replace tedious manual tasks.
Con: Atlan has limited data transformation nodes.
Pricing: As pricing information is not publicly available, you would have to contact Atlan. They also have a free trial upon request.
OvalEdge is a data catalog engineered for end-to-end data governance, quick and credible analytics, and privacy compliance. It has a cost-effective on-premises data catalog and governance tool set that crawls the BI platforms, databases, data lakes, and ETL (extract, transform, load) tools of an organization to build an easy-access, smart inventory of their data assets.
Additionally, OvalEdge offers a discovery platform that analysts, regardless of experience, can use to discover data and generate influential insights quickly. It also uses advanced algorithms and machine learning to automatically organize data.
Con: Some UX (user experience) features can introduce a learning curve to the tool.
Pricing: The Essential package offers manual lineage features and starts at $15,600 per year. The Professional and Enterprise packages offer automated lineage features, but you would have to contact OvalEdge sales.
Collibra is a data intelligence company with a cloud-based platform that merges business and IT to impart a data-driven culture to today’s digital enterprise. It has a data dictionary that documents the technical metadata of an organization and its usage. It also describes data structure, relationship to other data, format, origin, and use, providing a searchable repository for users.
Moreover, Collibra Data Lineage automatically maps relationships between applications, systems, and reports to ensure enterprises enjoy a context-rich view of data.
Cons: The UI (user interface) may be clunky and unintuitive. Additionally, data lineage capabilities need to mature to better handle customer needs.
Pricing: Contact Collibra, as pricing information is not publicly available.
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Octopai is an automated data intelligence platform that offers data and analytics teams multilayered data catalog, data discovery, and data lineage, which allow organizations to trace their assets, understand their data flows, and add credibility to their resources. As a cross-platform metadata management automation tool, it enables its users to discover and govern shared metadata.
Additionally, Octopai automatically gathers metadata—from databases, ETL and reporting tools—which is stored in a centralized repository. A smart engine searches all metadata to quickly present results.
Cons: The UI may come off as clunky, and the product may present a learning curve for business users.
Pricing: Contact the vendor for current pricing information.
Datameer provides a data engineering and analytics life cycle platform that enables data teams to directly transform and model data in their cloud warehouses using either SQL code or a no-code UI to handle complex data analytics projects.
Featuring 70+ source connectors, for ingestion of unstructured, semi-structured and unstructured data, Datameer goes beyond data ingestion to data preparation, exploration and consumption. With its interactive spreadsheet-like interface, Datameer users can blend, transform, and enrich complex data.
Cons: Datameer may experience lags when dealing with large data volumes.
Pricing: Even though Datameer offers a demo and a 14-day free trial, pricing information is only available after contacting Datameer.
Your choice of data lineage tools will be influenced by several factors. You want a tool with the ability to not only trace but also verify data history, as tractability makes sure you have high-quality data. Immutability should also come into consideration, as it brings credibility to data lineage tools.
Data lineage tools with collaboration features ensure remote teams collaborate on shared data. They also ensure different types of teams across the enterprise are involved in the data journey. Additionally, you should consider how easily integrated the tool is with third-party applications, as there are many tools and stages involved in a data journey.
For your unique use case, always consider the tools with the features that satisfy your needs and are within your budget. Finally, make good use of hands-on demos and free trials, as they provide an opportunity to get first-hand experience of your shortlisted tools.
Read next: Top Data Quality Tools & Software
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