What is Data Lineage and Why is it Important for Modern Data Orchestration
To tap into the full value of this data, organizations have stockpiled data from every available source, hoping to glean insights that will accelerate their business success and keep them ahead of the competition.
The need for data lineage is more important than ever as the number of data sources and MAD use cases increases each day. Laurent Paris, SVP of research & development at Astronomer and the CEO at Datakin, delves into what data lineage is and why it is vital for staying ahead in our data-driven economy.
Machine learning, artificial intelligence, and data (MAD) have fueled business innovation for years, driven by the enormous increase in data and data sources available to organizations.
To tap into the full value of this data, organizations have stockpiled data from every available source, hoping to glean insights that will accelerate their business success and keep them ahead of the competition. And thanks to cost-effective cloud data warehouses and lake houses, teams of any size can effortlessly store massive amounts of data.
But as the number of data sources and MAD use cases continues to expand, it becomes increasingly complex to track where the data has come from and how it is used for specific business purposes. Does your marketing team use the most relevant customer data? Are your machine learning models relying on current data? Do you know where that data is coming from? What interdependencies exist within your datasets, and how can one breakdown impact your whole organization?
Real-time data lineage can help you answer all of these questions.
As companies race to take full business advantage of their data, the need for data lineage is more important than ever. It has emerged as an essential foundation for sound data orchestration. For those unfamiliar with the concept, let’s explore what data lineage is and why it is vital for staying ahead in our data-driven economy.
What is Data Lineage?
Data lineage seeks to understand and track how datasets are combined, transformed, and moved across an organization. Effective data lineage processes build trust in your data across the organization and bring context to the often disjointed work of data engineers and scientists.
Data lineage solutions can increase efficiency in data orchestration, a process where siloed data from various sources is centralized, transformed, and then used to provide insights for teams across your company.
By identifying your data lineage throughout your data orchestration, you can answer:
- Where did this data come from (where is it generated or how is it acquired)?
- How has the data been transformed?
- How do these datasets rely on each other?
- Who uses this data to inform business decisions (including your team and applications)?
In practice, your team can more quickly identify and address breakdowns in your data pipelines—before you make critical business decisions based on the data they produce.
How Does Data Lineage Work?
Data lineage is enabled through the collection of lineage metadata, which describes how datasets rely on each other and can be used to study how those interdependencies shift with time.
Without the automatic collection of lineage metadata, this information must be collected when something breaks in your data pipeline. Your team opens an investigation to uncover what went wrong, which leads to them traversing a series of cascading issues, with no way of knowing how deep the problem goes until they reach the root cause.
Without surprise, this process costs your team immeasurable time and money. But there are data lineage tools that can help. These tools come in all shapes and sizes. Some review your code, look for SQL queries and capture the data interdependencies they find. Others will review logs of executed queries to construct a lineage graph, relying upon past events to predict future risks.
Both of these classes of data lineage tracing tools—static code analysis systems and query log analysis systems—are helpful, but they don’t produce compatible metadata and are difficult to use together in a cohesive lineage strategy.
This is why modern, open-source data lineage platforms have emerged to standardize the collection of lineage metadata.
See More: Data Orchestration Basics: How to Organize Data to Gain Real-Time Insights
Open Standards Can Unify Lineage Fragmentation
The data lineage space is fragmented, with many tools and processes that each aim to solve the common hurdles of capturing and using lineage metadata. To limit the risk of further fragmentation—which only further complicates processes for teams and costs them valuable time and resources—an open standard should be adopted to provide a common framework for teams to implement.
Today, open standards make up the foundation of communications and distributed systems. Standards like USB and HTTP allow developers and engineers to work independently, while knowing that what they build will interoperate with much larger infrastructure and systems. This decentralization empowers healthy communities and marketplaces, where one can choose the best tools for their job and trust that they’ll all work together.
OpenLineage is one such open standard for data lineage, intending to provide a consistent language for describing the flow of data across an organization. As an open standard, it acts as an integration point for vendors across the space and allows their solutions to collect lineage metadata consistently. Proprietary data lineage systems can also help, but they do not always make their data available to other parts of the toolchain, which can lead to lineage metadata silos. Don’t underestimate the flexibility and comprehensiveness an open standard can offer when choosing a lineage solution.
Data Lineage Empowers You with Trusted Data
Without a data lineage strategy, your organization could be using low-quality or untimely data to inform critical business decisions or customer-facing services. As the amount of data continues to grow, you would need a full team of data engineers solely dedicated to addressing breakdowns at all hours of the day.
Over time, this broken data orchestration process will erode trust in your business. It could mean you lose market share to organizations with a better handle on their data management.
Adopting open standards for data lineage can save your data engineers and scientists hundreds, if not thousands, of hours that would be spent reverse-engineering data flows to understand where a pipeline broke down. When all the tools in your pipeline speak the same way about lineage, all the teams in your organization will benefit.
With an open standard, data lineage will become baked into your company data DNA. You will be able to identify issues automatically and avoid costly errors, ensuring that your team has the greatest potential to succeed.
How are you making the most of data lineage? Share with us on Facebook, Twitter, and LinkedIn. We’d love to hear all about it!
