Learn why you need a data lineage tool and the seven 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 7 best data lineage tools of 2023:
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 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:
Multiple benefits and necessities are driving the implementation of data lineage software.
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.
Now that you learned how data lineage tools work and what benefits you will get, let’s evaluate the market.
Below are the seven most popular enterprise data lineage tools available today.
Keboola is a cloud-based data platform as a service. With Keboola you can automate your entire data 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.
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.
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.
Atlan is a cloud-based data democratization company designed to help business manage their entire data ecosystems with tools for data discovery, data catalog, active data governance and embedded collaboration.
The main shortcomings of Atlan are limitations with third-party integrations and missing features that are yet to be developed.
Alation is a data catalog pioneer that has added a wide range of data intelligence solutions to their portfolio such as; data, data search and discovery, data stewardship, data governance, analytics, and transformation.
Together with their partner Manta, a unified lineage platform, they provide advanced data lineage solutions with comprehensive visibility and understanding of the data lifecycle.
Alation will work well for small businesses, but running complex queries is reportedly extremely slow, and can rank up your costs.
Collibra is data intelligence company with a cloud-based platform that features flexible governance, continuous quality and built-in privacy for all data types. Collibra is best for creating an inventory of the data assets, capturing information and for data governance.
The disadvantage of Collibra is its complexity, which means you will require some technical expertise before you can start exploring the tool. Users have also mentioned that it is not an easy-to-use solution and can be “clunky” to get certain things to work out.
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.
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.
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.
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.
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 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:
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...
Contact Keboola's Sales team to find out what it can do for your data ingestion and lineage use case.
Start with a forever-free tier and pay as you grow.
What is data lineage in ETL? Data lineage gives you a visual representation of the ETL data pipeline and tracks how data sets were built, used, how they relate to each other, what transformations happened, and who made the modifications
Why is data lineage important? It helps fulfill regulatory compliance, speeds up error tracking and bug fixing, increases data quality and eases data migrations.
How do you build a data lineage? Best option is to rely on software that helps you record and trace data lineage at scale.
What is the difference between data lineage and data provenance? Data lineage records a data flow in details, whereas data provenance is a more abstract and high-level description of the data journey.
What is the difference between data lineage and data governance? Data lineage tracks and audits along its flow. Data governance encompasses other practices as well, such as data governance policies, data stewardship, master data management (MDM), reference data management (RDM), etc.
Further readings:
Sources:
.png)