Join our newsletter

Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.

Run your data operations on a single, unified platform.

  • Easy setup, no data storage required
  • Free forever for core features
  • Simple expansion with additional credits
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.

Download the file

Oops! Something went wrong while submitting the form.

4 Best Data Lineage Tools in 2022

Learn why you need a data lineage tool and the four best tools on the market.

How To
May 1, 2022
4 Best Data Lineage Tools in 2022
Learn why you need a data lineage tool and the four best tools on the market.

The modern enterprise taps into over 400 different data sources to extract the insights that sharpen its competitive edge.

The complexity, though, does not stop at the origin, where data is generated.

To get valuable insights from raw data enterprises must extract data from its source, transform the data (clean and aggregate it), and finally load the data into a data warehouse or BI tool, where it is served to data scientists for analysis.

Without a data lineage tool, which shines a light on the data flows through the complex ecosystem of interdependent data flows, enterprises would be flying blind.

In this blog, we are going to answer how data lineage tools work, why you need it, and what are the 4 best data lineage tools of 2022:

  1. Keboola
  2. Dremio
  3. Kylo
  4. Octopai

Implement a data lineage software that requires minimal-to-no additional work to speed up your data operations.

How do data lineage tools work?

Any data lineage tool should allow you to map the data lifecycle as it moves from its source to its final destination during the ETL data process.

The data journey should specify multiple entities:

  • The data provenance, aka the sources from where data is extracted (3rd Party Apps, APIs, CSV files, databases, data warehouses, etc.).
  • The transformations each data point undergoes (e.g. apply business logic to define which are new customers, aggregate sales on quarterly levels, remove outliers, …). So you can track data back to its origin.
  • Datastores, where data is either temporarily or permanently loaded: files, databases, data warehouses, and data lakes.
  • Integrations with other tools, such as business intelligence software (Looker, Metabase, Tableau, PowerBI, …) or CRMs.
  • Discover data dependencies both directly and indirectly by looking at a data flow visualization across all data assets and data elements.

The data journey - or if you prefer, the flow of data - needs to be inspectable throughout the lineage. 

The right data lineage tools should provide full visibility of data changes via audit trails that allow an in-depth introspection into the data asset and how they changed through time. 

Data lineage software helps you answer:

  • Who accessed and made modifications to the data? 
  • What changes were made to the data processes?
  • How did these modifications affect the current state?
  • When were these changes made?

Why you need a data lineage tool

Multiple benefits and necessities are driving the implementation of data lineage software. 

  • Enhanced data governance. Crucial for modern enterprises that operate with big data volumes of information, as clear governance policies and practices offer enterprises introspection, control, and operational clarity for managing sensitive data.
  • Simplified regulatory compliance. Legislative regulations such as GDPR and data privacy laws can be extremely hard to achieve if you do not have data lineage software to guide you through all the touchpoints and locations, where personal data resides in your data platforms.
  • Improved data quality. Understanding how business data values were created along the data pipelines, will help you better interpret the data you work with. The interpretability via data lineage software brings higher accuracy and consistency in decision-making, and increased reliability of your data analytics to drive business operations.
  • Detailed impact analysis. Issues with data are often discovered downstream during business reports, not upstream in the data processes, where the error is produced. Data scientists, analysts, and BI reporters find missing data, corrupted data, or plainly wrong data when searching for insights. Data lineage tools allow you to map each stage of the data transformation and trace back those errors, via the data lineage, to the source. This expedites error removal and delivers faster and higher levels of data quality.
  • Easier migrations. Successful migration from on-premise to the cloud (or vice versa) will not interrupt business operations. To move data between different systems, data ops engineers map all the workflows of how data travels, so they can recreate the same system at the new location.

Implementing any of the data lineage tools available on the market should cover the benefits listed, but they all come with their own sets of unique disadvantages you would have to overcome before you get to the good part of streamlined data lineage.

The top 4 data lineage tools of 2022

Now that you learned how data lineage tools work and what benefits you will get, let’s evaluate the market.

Below are the four most popular enterprise data lineage tools available today.

1. Keboola

Keboola is a cloud-based, data operations platform as a service. With Keboola you can automate your entire data integration pipeline: from collecting structured and unstructured data, to transforming and storing it for analysis.

At each step of the pipeline, Keboola automatically tracks all relevant metadata and constructs logs. This gives you a granular view of data lineage so you can identify root cause of errors faster. 

Each operation on the data platform is tagged with operational metadata describing the user activity on the events level, job activity (for automated workloads), the schema evolution throughout the changes, compliance with security rules, and even monitors for optimal pipeline performance.

Thousands of users love Keboola because it offers more than just metadata management. You can tap into its rich resources for any needs you have for your data operations.

Key features

  • All the metadata collection is done automatically. You do not need to waste any energy building up the data lineage, or integrating with any additional tools. Simply perform your usual data operations, and data lineage comes out of the box.
  • Hundreds of ready-to-use integrations. No API knowledge and writing scripts required. Get data from your existing tools or send it to them instantly. 
Keboola reviews sourced by G2

You can explore all the power Keboola has to offer without sharing your credit card details. Register for the forever-free plan and take it for a spin.

2. Dremio

Via Dremio

Dremio is a querying engine that allows you to access your data in data lakes, such as on Amazon Web Services (AWS) or Microsoft Azure.

Key features

  • Data graph helps you to maintain the relationship between your data sources, virtual datasets, and your queries 
  • Virtual datasets. A semantic layer of data views so you can easily manage, share and curate data without the need to copy or move data.
  • Dremio’s dataset history features tools for inspecting how a dataset was created, a visual ‘undo’ and a method for ‘forking’ earlier versions of datasets.
  • Easy to use by non-technical personnel when constructing query-like tasks.

Main disadvantage? 

Dremio was built as a querying software, so it does not offer out-of-the-box flexibility and maturity of features that come from data operations platforms. For example, Dremio has hiccups when integrating from the data sources within the platform to other BI tools, as well as limited data governance and auditing features.

3. Kylo

Via Kylo

Kylo is an open-source data lake management software platform.

Kylo is rather versatile. It allows users to ingest, cleanse, validate, profile, and wrangle (in SQL) data lakes data, as well as monitor it.

Key feature

  • Kylo maintains a rich metadata store that automatically fills in from the data tables. Users can access field - and table - metadata by querying, as well as visually inspect data provenance.

A common shortcoming is that Kylo necessitates specialized engineering skills to understand and use it. It can be thought of as an accelerator for data engineers, who want to build their own data lineage, but is not suitable for non-technical users or business users.

4. Octopai

Via Octopai

Octopai is a different data lineage solution from the ones presented above. Instead of centralizing on a data operation task (end-to-end, querying, ingestion, …), Octopai is primarily focusing on offering just data lineage.

It connects to your data storage or BI environment and extracts metadata to build data lineage insights.

Key features

  • Octopai specializes in only one thing - tracking data lineage.
  • Useful if you want to add only a data lineage feature to your existing data management system.
  • A straightforward and easy-to-use data catalog that creates a single source of truth
  • Automated data discovery for instantaneous datasets search

The disadvantage of Octopai is that because its data lineage capabilities, business glossary, and data discovery are limited, it doesn’t pair well with other tools that offer data governance, such as access control, security monitoring, etc. Additionally, being rather novel, Octopai has the shortcoming of integrating with only a selected few technologies.

No extra work and no coding required. Keboola automatically tags each data operation with extensive metadata, so you can focus on delivering value, not on the infrastructure.

Which tool should you choose?

In our opinion, there is only one solution that offers perfect data lineage and will bring peace of mind to the full data team. 

With Keboola you get the full picture of your data flow and run a complete data stack as a service, without writing a single line of code. 

Keboola automates the entire data integration pipeline with simplified drag ‘n’ drop functionalities: from collecting data, transforming it, and storing it for analysis, and although the central features are focused on automating data work, Keboola was built with tracing data lineage in mind.

Each data operation in the platform is tagged with operational metadata describing:

  • user activity
  • job activity
  • data flow
  • schema evolution
  • data pipeline performance
  • compliance with security rules
  • and more!

The extensive metadata is triggered by the platform running its usual operations, so you do not have to do any additional work. 

Less manual work means fewer errors, better productivity, and increased efficiency. 

So, our question to you is...

Ready to finally understand where your data is coming from and how it is used?

Take Keboola for a spin and check for yourself what it can do for your data ingestion and lineage use case. 

Start with a forever-free tier and pay as you grow.

Recomended Articles