Top 10 Data Lineage Tools for 2023

Eyal Katz
Content Manager | Aggua
March 7, 2023

As more businesses understand the value of visibility over their data flow, data lineage is a hot topic in almost every industry. Understanding data lineage can ensure the accuracy and trustworthiness of data from sources to end users, ultimately leading to better decision-making.

According to Deloitte, one of the main challenges for modeling data lineage, and extracting insights from it, is dealing with complex data that is highly connected, diverse, dynamic, and contextual.

In this post, we'll look at the features and capabilities of data lineage tools, from open-source choices to enterprise-grade solutions, to help you identify the best fit for your business. This blog will offer helpful insights to assist you in navigating the data lineage environment, whether you're a data engineer, data analyst, data scientist, or a business user/executive.

What is data lineage?

Data lineage is the process of following a piece of information as it moves from its point of origin to its final destination within the data architecture. In plain English, it is the capacity to identify the origin of data, see how it has been changed, and recognize the context in which it is used. Data lineage is crucial for helping organizations assess their data's precision, thoroughness, and dependability.

For example, data processing, ingestion, and querying take advantage of data lineage because organizations can clearly understand their data's history and ensure it is correct, complete, and dependable. It is advisable to adopt a tool like Data Build Tool (DBT) to enhance your data lineage capabilities. Data engineers and analysts may document, test, and improve their data models with the aid of this open-source DBT application.

What are the benefits of using data lineage tools?

Data lineage gives organizations end-to-end visibility over your entire data flow, what its impact will be on downstream systems, and more factors. Anyone who uses the data will be able to understand its source, perform root cause analysis, and guarantee that the data is trustworthy.

Utilizing data lineage technologies can help firms comprehend the precision, thoroughness, and dependability of their data, which is crucial because it enables businesses to recognize and reduce the risks of data breaches and non-compliance with laws like CCPA and GDPR.

Furthermore, by connecting the history of the data via version control, organizations can enhance their data management policies and make sure their data is totally accurate and can inform their strategic decisions.

Another benefit is observability, which is the ability to comprehend what is happening with your data at any given time. Data lineage tools help detect and resolve problems with data quality and preserve data integrity, therefore assisting organizations in increasing the observability of their data.

How to Choose the Right Data Lineage Tool

Almost every startup, SME, and large organization can benefit from the contextual insights that data lineage provides, and deciding what data lineage tool to use depends on a few different factors. For example, which departments or teams will access the tool and frequently use its insights? The right lineage platform should act as a single source of truth that enables your data teams (e.g. data scientists, engineers, and analysts) to work collaboratively with executives and Heads of Data.

Next, you’ll need to consider the tool’s integration capabilities based on your existing tech stack and how you might add to it in the future, including data lakes, databases, and BI tools.

Finally, what about scalability? As your business grows (along with the volume of data you process), will you need to invest in separate tools, like a data cost management or data catalog solution? Instead, it might be better to consider an all-in-one data management platform that will give you visibility over your data knowledge in one location.

Top 10 Data Lineage Tools for 2023

1. Azure Data Catalog & Lineage

Azure Data Catalog is a cloud-based metadata management system. Its newest upgrade is called Microsoft Purview, which maps your data landscape via end-to-end data lineage.

Pros: It integrates seamlessly with other Azure services.

Cons: Very limited lineage features to help you gain visibility over your data as there is no support for visualization and it requires manual development.

Reviewer says:Lineage data was successfully available from data pipelines that were orchestrated by Azure Data Factory. There is no other way to create a lineage, so in most cases (e.g, datasets/assets created by Azure functions or other stacks) there is no lineage information associated with the assets.”

2. Aggua

Aggua is a comprehensive data lineage tool that enables users to monitor, display, and analyze data flow across many platforms and systems.

Pros: Automatically map your data lineage with Aggua for a better holistic view of your entire data pipeline. Discover how you can effortlessly understand your data tech stack from source to target within minutes.

Cons: Currently support only cloud-based modern data stack (no on-premise warehouse support).

Reviewer says: “Aggua lineage capabilities are a must-have for every data group. Aggua helped us save 85% of the time to understand our lineage.”

3. Kylo

Users can construct a central data repository from various sources and carry out data integration, transformation, and governance tasks using the open-source Kylo data lake management platform.

Pros: Kylo is a good solution for managing data lakes with all the functionality required for data integration, transformation, and governance. It also boasts top-notch community support.

Cons: Scalability problems have been mentioned by certain users.

Reviewer says: “I really like the compatibility between Teradata Kylo and Github.”

4. Atlan

Atlan is a metadata platform that integrates data from a wide range of sources, like Databricks and Tableau. It offers column-level lineage and pre-built integrations with BI tools and warehouses.  

Pros: Atlan’s platform has a graph view feature that allows users to adjust the lineage depth across downstream/upstream layers, which provides a clear visual and enhanced flexibility.

Cons: Some users noted the lack of documentation, both official and unofficial, available online to support their queries. Also,there are inconsistencies in the lineage according to several reports.  

Reviewer says: “The most helpful thing here is all the dashboards that provide visibility in one place. In my experience, less suited for collaboration across teams. Not a ton of ways to do things in real-time together.”

5. Talend

Talend’s Metadata Manager supports data lineage across multiple platforms and presents the results in an easy-to-understand user interface. It’s more suitable for legacy and on-premise use cases rather than cloud.  

Pros: Supports platforms like Hadoop and NoSQL. Talend is hot on providing lineage capabilities to help users meet data privacy regulations, hence it provides plenty of documentation regarding GDPR and other regulatory requirements.

Cons: Users have mentioned data mapping difficulties, and have noted that there is a lack of visibility when trying to diagnose the root cause of an error.  

Reviewer says: “Product does basic data profiling functions and meets our needs for pre-data migration analysis.”

6. DataHub

DataHub (built by LinkedIn) is an open-source metadata platform that enables end-to-end granular lineage. It offers native integration with Airflow, Superset, and others.

Pros: If your team prefers to take a hands-on approach, DataHub supports manual editing of lineage between entities, allowing you to add or remove upstream or downstream lineage edges. However, manual editing is not always the best practice and automated lineage should take precedence.

Cons: DataHub previously only supported table-level lineage. The column-level lineage feature is very new and supports a very limited amount of sources.

Reviewer says: “Although semantic modeling offers some cool features, my impression is that there is not always a technical logic behind it in the DataHub. Missing, I think, is a two-way relationship view (perhaps in a visual form as well) so that a data steward can see the big picture at a glance.”

7. Octapai

Octapai is a data lineage and metadata management that enables users to automatically find, examine, and record data flows across numerous systems and platforms.

Pros: Octopai enhances data governance and compliance.

Cons: Some users pointed out that Octopai’s UI is more suited to a data expert and not friendly for a non-technical business user as it takes time to learn the ins and outs of the platform. Octopai also lacks support for some modern data stack tools.

Reviewer says: “I like how in the lineage module you are able to drill backward and forward. The lack of columnar lineage is a void to fill.”

8. Data.World

Data.World is a data management and collaboration platform that enables users to effortlessly discover, share, and collaborate on data projects. It began as a go-to solution for enterprises using legacy and on-prem solutions, and rolled over to the modern tech stack for wider compatibility.

Pros: Data.World increases data discovery and sharing, and has an excellent user experience.

Cons: Data.World’s Explorer Lineage is still relatively new, and some areas lack nuance around metadata management.

Reviewer says: “Limited visualization functionality – live charts not easy to implement.”

9. CastorDoc

CastorDoc is a data lineage and metadata management platform that enables users to manage data flows across many platforms and systems.

Pros: CastorDoc is a good tool for managing metadata that enhances data governance and compliance.

Cons: According to some users, navigating the lineage user interface can be challenging.

Reviewer says: “It took us a little longer than anticipated to get our database synced with Castor and automatically refreshing daily.”

10. Alation Data

Alation’s data catalog and metadata management platform enables you to use customer database connectors and include your own query log parsing logic for lineage. It takes a deterministic lineage approach.

Pros: Alation enhances data discovery and consumption, and automates BI lineage to help reduce the need for manual effort.

Cons: According to some users, the platform might be challenging to grow and scale, while the costs grow very high.

Reviewer says: “Needs more lineage capabilities for automated lineage.”

Unlock the Power of Data Management with Aggua

In this post, we examined each tool's specifications, testimonials, advantages, and disadvantages. Using these tools, data teams can have total control over their data workflows using a centralized location and all-encompassing view.

Aggua is the answer if you want to gain a holistic view of your entire data pipeline. Aggua enables you to precisely map all your data assets from source to target with column-level dependencies – and identify important events in the flow. Track and map the transformation, see the latest version changes, and gain an answer to any data question. With features like quick navigation, drag & drop, and easy sharing, their self-service platform enables your data teams to collaborate and rely on a single source of truth for lineage across the board.

Try Aggua Today »

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