Data lineage is the process of tracing the origin, transformation, and usage of data across different systems, applications, and environments. It helps data engineers understand the data flow, dependencies, quality, and impact of changes in their data pipelines. In this article, we will explore how you can track data lineage using various methods and tools.
Tracking data lineage can be highly beneficial for data engineering and data-driven organizations. It can improve data quality and trustworthiness by helping to identify and fix errors, inconsistencies, and anomalies. Additionally, it can enhance data governance and compliance by providing visibility and accountability for data sources, owners, and consumers. Furthermore, it facilitates data discovery and reuse by allowing engineers to find and access relevant data sets and metadata across different platforms. Finally, it supports data analysis and decision making by enabling engineers to understand the context, meaning, and history of the data.
Tracking data lineage manually using spreadsheets, diagrams, or text files can be a time-consuming and error-prone process, as data engineers have to update the documentation every time there is a change in the data pipeline or the data sources. Furthermore, this method can be difficult to maintain and share, as the documentation can become outdated, inconsistent, or inaccessible over time. Additionally, it is limited in scope and detail, as the documentation may not capture all the nuances and complexities of the data flow and dependencies.
Tracking data lineage manually is a terrible idea, as the growing complexity is often described / documented poorly. It is error-prone and time consuming, which makes it inefficient and lowers the return on time spent. Furthermore, it makes changes and updates slow, and brings the TCO up significantly.
Another way to track data lineage is to use automated tools that can capture, store, and visualize the data flow and metadata for each data pipeline. These tools can leverage various techniques such as code analysis, data profiling, data tagging, and a data lineage graph. Code analysis involves parsing the code or scripts that define the pipeline and extracting the sources, transformations, destinations, and metadata. Data profiling scans the datasets for types, formats, values, statistics, and quality indicators. Data tagging assigns labels or attributes to datasets and metadata for search, classification, and filtering. Finally, a data lineage graph creates a graphical representation of the data flow and dependencies for each pipeline with nodes (datasets) and edges (transformations).
There are many data lineage tools available in the market, each with unique features, capabilities, and integrations. Apache Atlas is an open-source data governance and metadata framework that provides data lineage, catalog, classification, and policy management for Apache Hadoop and other big data platforms. Collibra Data Intelligence Platform is a cloud-based platform offering data lineage, catalog, glossary, quality, privacy, and governance solutions. Informatica Enterprise Data Catalog is a cloud-native data catalog that delivers data lineage, discovery, profiling, tagging, and governance for hybrid and multi-cloud environments. Talend Data Fabric is a unified data platform integrating data lineage, catalog, quality, preparation, integration, and transformation for various sources and destinations.
Data lineage is an essential part of data engineering, helping to ensure the quality, governance, and usability of data assets. When selecting a data lineage tool, it’s important to consider the type and volume of data sources and destinations you need to track, the level of granularity and accuracy of data lineage you need to capture, the ease of installation and configuration, scalability, performance, cost, support, and security. With the right methods and tools in place, you can track data lineage effectively and efficiently, gaining valuable insights for your organization.
One needs to consider his present and future needs and technology when choosing a data lineage solution. There's no "one size fits all": the data lineage horizon is as fragmented as the data lakehouse horizon itself: there is a plethora of options with many advantages and disadvantages one needs to consider before choosing.