ANNOUNCEMENT: Octopai has reached Microsoft's Co-Sell Partner Status for Microsoft Azure Customers: Read More

Is your organization Octopied?

With effortless onboarding and no implementation costs, Octopai’s data intelligence platform gives you unprecedented visibility and trust into the most complex data environments.

How important is data lineage in data engineering?

The primary goal of data engineering is to get data into the hands of those who need the data, in a form that is useful and usable. This includes designing systems for collecting, storing and accessing data; validating, cleaning and/or otherwise ensuring accuracy of data; and optimizing the flow of data throughout the organization. 

Data lineage is an important part of the data engineering toolbox. In order to optimize a data flow, you need a clear map of what the data flow looks like right now. In order to check data accuracy and fix issues, you need to be able to trace and validate every step in the data’s journey. When you are designing a system to facilitate data collection, storage and access, you need a way to check if the system is functioning efficiently in its movement of data. Data lineage is a tool for all this – and more. 

Data lineage is the map of your data’s journey, from where it entered your environment until where it ends up. Automated data lineage mapping shows you everything that happened to a given data point along the way: what transformations it went through, what calculations it was a part of, what fields it influenced. 

Data engineering teams might use automated data lineage to do data flow visualization inside of an ETL process, to find redundancies or parallel processes, or to locate dependencies in a report. Data lineage is an important and necessary tool enabling data engineers to do their job.