Data Lineage Tracking Service for Pipeline Event Graphs
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Solution Overview
Problem
In large-scale enterprises, tracking data sets as they traverse complex data pipelines is challenging due to asynchronous transitions, diverse data storage formats, and different teams responsible for various stages, making it difficult to understand data set lineage and downstream impacts.
Innovation Solution
A cloud-based data lineage tracking service that automatically generates graphs to represent data set transitions, using event information sources and metadata to provide lineage queries and downstream impact information, enabling centralized tracking and rapid notification of failures or delays.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data sets are tracked manually across multiple stages by different teams, then each team can manage its own workload, but understanding the overall data lineage and downstream impacts becomes difficult and time-consuming
Solution Approach 1:
The patent merges tracking capabilities across all data pipeline stages into a single centralized service. This service collects events from multiple sources (data lakes, data warehouses, streaming platforms) and maintains a unified graph representation of data lineage, allowing any team to query downstream impacts without manually tracing through other teams' systems.
Solution Approach 2:
The centralized lineage tracking service acts as an intermediary between different data pipeline teams. It receives events from various data sources and teams, processes them into a unified graph model, and provides query responses to any team member needing lineage information, eliminating the need for direct inter-team coordination for tracking purposes.
2Loss of information
If detailed tracking of all data set transitions is implemented, then complete lineage information is available, but the system complexity and resource requirements increase significantly
Solution Approach 1:
The tracking system segments data pipeline monitoring into independent event sources (data lakes, data warehouses, streaming platforms) that each publish events independently. The centralized service processes these segmented events separately and maintains a graph structure that can be queried in segments, reducing the complexity of managing complete lineage tracking.
Solution Approach 2:
Instead of implementing complex tracking logic in each data pipeline team's system, the patent uses event copying where each team publishes simplified event representations to a centralized service. The service maintains copies of lineage information in a graph database, allowing teams to query comprehensive information without each team needing to implement and maintain the full tracking infrastructure.
3Ease of operation
If centralized lineage tracking is implemented across all data stages, then downstream impact analysis becomes easy, but integrating with diverse data storage formats and event sources becomes complex
Solution Approach 1:
The centralized lineage tracking service implements a universal event processing framework that handles multiple data source types (data lakes, data warehouses, streaming platforms) through a common event interface. This allows the service to maintain a unified graph representation of data lineage across diverse storage formats and pipeline stages, enabling consistent lineage queries regardless of the underlying data source heterogeneity.
Data Source
AI summary
A plurality of events is extracted from a set of sources, including an event representing a transfer of a first data set from one data storage stage of a data pipeline to another stage to form a second data set, and another event representing a completion of a computation performed on the second data set. Based on analysis of the plurality of events, a graph is stored; the nodes of the graph represent data sets at respective stages of the data pipeline, and edges represent the events. In response to a request for lineage information pertaining to a particular data set at a particular stage of the pipeline, an indication of a sequence of events represented in the graph is provided, including a particular event which led to the presence of the particular data set at the particular stage.