Data lineage tools make it easier for teams to track the transfer of data across several systems, databases, and applications. Ultimately, this translates into better capabilities around understanding and handling data.
But how do you choose the best data lineage solution for your organization?
This article dives into the most widespread data lineage tools to show you their unique features and solutions to common data lineage challenges.
What is Data Lineage?
Data lineage is metadata that tracks the movement of a data asset over time. This allows you to answer important questions regarding data, such as:
- Where did this data come from?
- Who changed it last, and how?
- What function does this data serve?
- Who is using this data elsewhere in the organization?
Data lineage support is an essential feature of any data catalog tool. A data catalog lets users discover and query data no matter where it’s located – and data lineage allows them to carry out activities that increase the quality and utility of data, including:
- Improving user trust in data by identifying data sources and history throughout time
- Boosting data quality by validating and preventing data loss
- Performing an impact analysis to determine how proposed data modifications will affect downstream customers
- Enforce data control by classifying and auditing
Data Lineage vs. Data Provenance vs. Data Governance
Data lineage, data provenance, and data governance are intertwined concepts. They work together to ensure that an organization’s data quality and security remain consistent throughout time.
Data governance establishes an organizational framework for managing data assets by establishing data owners, business terms, rules, policies, and processes that apply throughout the data lifecycle. Note that teams can choose from various data governance frameworks. Data lineage focuses on helping data governance teams in ensuring data complies with these requirements by providing visibility into how data moves throughout the pipeline.
Data provenance is commonly used in the context of data lineage, although it only relates to the initial instance of that data or its source. Data lineage provides a comprehensive audit trail for data; this degree of information is extremely useful for diagnosing any data issues, allowing data engineers to troubleshoot more efficiently and identify solutions faster.
While the breadth of data governance extends beyond data lineage and provenance, this component of data management is critical for enforcing corporate norms.
Why Are Data Lineage Tools Important?
Data lineage solutions help data teams understand how changes to individual assets will affect downstream dependencies so that they don’t have to work blindly and risk causing unpleasant surprises for unknown parties. The data lineage lifecycle depicts how data is acquired and activated, allowing data teams to better understand and monitor upstream and downstream connections.
By enabling you to quickly identify the upstream mistakes that may have caused a report to fail, you can troubleshoot data issues faster when they arise.
Another benefit of data lineage tooling is that they help you communicate the effect of broken data to customers who rely on downstream reports and tables. You can proactively keep them informed when data is erroneous and tell them when any issues are fixed, which is essential to collaboration-driven data quality testing.
Top 8 Data Lineage Tools
When it comes to data lineage tooling, you can choose from commercial and open-source solutions.
1. Alation
Alation is a data catalog that provides lineage tracking and other features to help teams enhance their data governance and collaboration. It comes with a user-friendly interface, which enables users to locate, understand, and use company data assets.
Alation helps users understand and manage data, supports cloud data migration, and includes a few handy tools for improving data accuracy and analytics. Its machine learning features help with data navigation and collaboration while also providing secure data sharing tools.
2. Collibra
Collibra is a comprehensive data governance platform that has data lineage tracing, data cataloging, and other capabilities to help teams better manage and use their data assets. It includes a simple user interface and connects seamlessly with various data tools and platforms.
As a Collibra user, you get automatic lineage mapping and maintenance, improved analytics using automated lineage diagrams, and lineage available at scale. The solution also includes tools for improving data governance and compliance, as well as visibility of upstream and downstream analytics.
3. Secoda
A comprehensive data management platform, Secoda provides teams with AI-powered solutions for data discovery, governance, and collaboration. It lets users explore, analyze, and exchange data across many platforms, making it an effective tool for self-service analytics.
Secoda’s configurable workspace promotes improved cooperation and data stewardship. Additionally, Secoda provides self-service analytics, paving the way for data-driven decision-making. Users can easily search data from all your data sources and analyze structured and unstructured data.
4. Lumada Data Catalog
Lumada Data Catalog is powered by Waterline Data’s data cataloging and offers data lineage tracking and other capabilities that help with data discovery and control. Lumada employs a machine learning-based approach to data discovery and classification, making it easier for users to locate and comprehend data assets.
Lumada lets you unify data from many data sources and systems. It also features machine learning tools for data discovery and management and a unique data ‘fingerprint’ method for tracking relationship lineage.
5. IBM InfoSphere Information Governance Catalog
IBM InfoSphere Information Governance Catalog is a metadata management platform that includes data lineage tracing and other features to help teams run efficient data governance and achieve compliance.
The solution has a simple interface that helps users to see and understand data relationships and dependencies. It contains tools for searching and visualising data, as well as determining data ownership and documentation. Data dictionary characteristics are used to define data classifications.
6. Precisely
Precisely, originally Syncsort Zen, is a metadata management application that offers data lineage tracking, data cataloging, and other features that enable effective data governance and management.
It has an intuitive graphical interface that lets users see and understand data linkages and dependencies. Precisely includes a dynamic data library, machine learning, and artificial intelligence for asset identification and tagging. You also get tools for visualizing data lineages.
7. Talend Data Catalog
Talend Data Catalog is a data catalog that offers data lineage tracing, data discovery, and other features to assist enterprises with data governance and management. It takes a machine learning-based approach to data discovery and classification, making it easier for users to locate and understand their data assets.
Talend comes with helpful features like automatic data crawling, machine learning-based data classification, end-to-end data lineage, and custom user access controls that enhance security and compliance.
Types of Data Lineage Tools
Data lineage can be classified into two types: table-level lineage and column-level lineage (sometimes called field-level lineage).
Table-level lineage is the most basic type of data lineage, demonstrating how different tables in a data environment relate to one another. While this can help you understand how a specific table was created, it cannot show you about the origin of the data within the table.
Column-level data lineage, on the other hand, represents a map of a dataset’s journey from ingestion to visualization. Column-level lineage is a critical component of data observability because it allows data engineers to quickly track the root cause of a data quality problem back to its source and do impact analysis to determine which downstream dependencies may be impacted.
Benefits of Using Data Lineage Tools
Companies are collecting more data than ever before, so it’s key to ensure that it is structured, accurate, and trackable. Here are some of the main reasons why data lineage tools are useful:
| Benefit | How It Works |
|---|---|
| Faster discovery of problems and troubleshooting | Tracing data back to its source and seeing the data transformations it went through makes it easier to discover issues and determine where they occurred |
| Improved data management | By troubleshooting issues and identifying defects, you may gradually improve your company’s data management operations. Data lineage enables you to correct current issues with your data while also lowering future error rates |
| Better insights | When data teams can examine the data sources, transformations, and destinations of data, they can acquire new insights into it. They can discover new links between data sets, offer methods to improve your data pipeline, and much more |
| Data transparency and compliance | It’s easier to follow data compliance and requirements when you can see the entire history of your data. Your data team will be better able to develop your data governance plan by ensuring that data flows securely across all touchpoints and determining the best ways to use data |
Data lineage technologies significantly improve data accuracy, security, governance, analytics, among other aspects. The numerous advantages of data lineage technologies make them a worthwhile investment.
The only decision left is which data lineage tool to choose.
Key Factors to Consider When Choosing a Data Lineage Tool
When selecting a data lineage tool, consider the following aspects to make sure you pick the right solution for your organization:
- Compatibility with Data Stack – Make sure the tool works with your existing data systems, such as databases, data lakes, ETL pipelines, and cloud platforms.
- Visualization Capabilities – Look for technologies that give clear, interactive visualizations of data flows, making it simple to monitor data from source to data transformations. Strong visualization support allows you to clearly observe the flow of data and use it to make decisions.
- Automation and Scalability – Select a solution that can automatically record and update lineage as data changes, as well as one that can scale to meet your organization’s data volume.
- Compliance and Auditing – If your company needs to fulfill regulatory standards, make sure the tool includes capabilities such as data traceability, audit logs, and version control to help with compliance.
- User Experience – Choose a product that is easy to use and understand for both technical and non-technical team members.
- Cost and Support – Evaluate the tool’s pricing plan to ensure it matches your budget, as well as the vendor’s degree of customer support and resources.
Selecting the appropriate data lineage tool is a critical decision, but it doesn’t have to be complicated. Following these steps and conducting extensive study will help you choose the ideal technology for your firm.
Challenges in Using Data Lineage Tools
Implementing data lineage is difficult due to the complexities of modern data ecosystems where data passes through many processes and systems. This complexity is exacerbated by the lack of standardized tools and procedures for documenting data’s lifecycle, which can result in discrepancies and gaps in lineage information.
Scalability
The volume of data handled by businesses grows in tandem with their size. A scalable data lineage system is required to handle this expansion without sacrificing performance. Implementing AI-powered data discovery and governance helps improve scalability through intelligent insights and automation.
Furthermore, the lineage system must be fault-tolerant, which means it can recover from faults without losing vital lineage data.
Changing data regulations
Changing data protection legislation, such as GDPR and CCPA, have a substantial impact on data lineage. Organizations must ensure that their lineage procedures are adaptive in order to comply with these standards. Automated data lineage systems help improve governance and compliance, making it easier to respond to regulatory changes.
This adaptability calls for constant changes to lineage tracking techniques and documentation procedures.
Granularity of data
The granularity with which data is tracked can have a significant impact on lineage tracking efficacy. Too much granularity can lead to information overload, while too little can result in overlooking essential details. Understanding data lineage representation in governance is critical for defining the necessary amount of detail.
Finding the correct mix is essential for giving useful and comprehensible lineage information.
How to Implement a Data Lineage Tool: Best Practices
1. Include the proper context in data lineage
While gathering more data for the sake of data may not help you accomplish your business needs, collecting and managing additional metadata – with correct business context – is probably a smart idea.
Data lineage with rich, contextual metadata is extremely beneficial since it allows teams to troubleshoot issues quickly and understand how prospective schema changes may influence downstream reports and stakeholders.
When contextual information about how data assets are used inside your business is exposed and searchable via comprehensive data lineage, incident management becomes easier. You can fix data outages faster and communicate the status of impacted data assets to key stakeholders in your organization.
2. Prioritize quality over quantity
Modern businesses want to be data-driven, but collecting more data isn’t always the best option. Data that is not relevant or useful for analytics can just become noise. Amassing the most data does not always result in more value but it does guarantee increased storage and maintenance costs.
Rather than simply automating the collecting and production of surface-level data graphs, lineage solutions should contain relevant data, such as which assets are being used and by whom.
With a more complete picture of data utilization, teams can have a clearer idea of which data is most important to their organization. Outdated tables or assets that are no longer in use can be deprecated to minimize future issues and misunderstandings, allowing the business to focus on data quality rather than quantity.
3. Organize the data lineage for better interpretation
Data lineage solutions should use clear naming conventions and colors for the data they describe, right down to the logos used for the various tools that comprise data pipelines.
As data systems get more complex, arranging lineage for simple interpretation will help teams extract the most value from their lineage as rapidly as feasible.
4. Highlight important data using field-level data lineage
Data teams don’t need to view everything in their data; they simply need to be able to locate what is relevant to solving a problem or answering a question.
This is why field-level lineage is key. While table-level lineage has been the norm for several years, data engineers require greater granularity to understand why and how their pipelines break. Field-level lineage allows teams to identify the impact of specific code, operational, and data changes on downstream fields and reports.
When data fails, field-level lineage can reveal the most important and extensively used downstream reports that are affected. That same lineage also reduces time-to-resolution by allowing data teams to immediately identify the source of data issues.
5. Expand data lineage to meet the needs of the company
Data lineage must be rich, relevant, and scalable in order to bring value. Otherwise, it’s merely visual candy that looks good in executive presentations but offers little to assist teams prevent data breaches or resolve them more quickly when they occur.
Automation has made lineage the hottest new layer in the data stack. It alleviates a part of this problem by allowing lineage to scale to accept new data sources, pipelines, and more complex transformations.
But then you’re left with the task of making lineage meaningful by combining metadata for all of your data assets and pipelines into a single, coherent view. Enterprise data lineage solutions must expand via automation without sacrificing coverage. Every ingestor, pipeline, layer of the stack, and report must be accounted for, down to the field level while remaining rich and discoverable so teams can find exactly what they’re looking for.
Conclusion
Data can move through various touchpoints, so having historical context and the capacity to track data throughout its lifecycle is critical. Companies that can track and visualize data during migrations, updates, and other events can guarantee that their data is valid and maintains its integrity during all changes and transformations.
Data lineage is difficult. But when done correctly, it’s extremely powerful. Data teams can choose from a variety of tools to track the origins of data, understand the relationships between their data sets, and discover why problems occurred in their data analytics procedures. With the right tools at their side, data teams may be more confident that their data is accurate and up-to-date.
Now that you know how data lineage works, here’s how to put it into action – follow this step by step tutorial for how data version control provides data lineage for data lakes.
