Inferring Design-Time Data Lineage from Runtime Cloud Artifacts
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Solution Overview
Problem
Traditional data warehouse systems employing a 'schema on write' approach become cumbersome and difficult to adapt as data scales and usage evolves, while 'schema on read' approaches like Apache Hadoop allow unstructured data processing but lack visibility into data processing workflows and operations.
Innovation Solution
A technique is introduced to automatically collect, visualize, and utilize upstream and downstream data lineage by inferring design-time information from run-time artifacts in distributed computing clusters, providing users with insights into data processing systems through metadata extraction and visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a 'schema on write' approach is used to organize data with a predefined schema, then data structure and query efficiency are improved, but system complexity and difficulty of adaptation increase as data scales
Solution Approach 1:
The patent inverts the traditional schema-on-write approach by implementing schema inference at query time rather than requiring predefined schemas during data loading. The system automatically infers data types, relationships, and structures from the actual data content when queries are executed, eliminating the need for complex upfront schema design while maintaining data organization benefits
Solution Approach 2:
The system enables self-service schema generation by automatically inferring data structures, types, and relationships from the data itself without requiring manual schema definition. The metadata service autonomously analyzes data patterns and generates appropriate schemas, reducing design complexity while preserving structural organization
2Adaptability or versatility
If a 'schema on read' approach is used to process unstructured data, then ease of data loading and processing flexibility are improved, but visibility into data processing workflows and operations deteriorates
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously collects runtime artifacts, metadata, and execution logs from data processing operations. This feedback is used to automatically infer data lineages, transformation patterns, and workflow structures, making previously invisible processes observable and manageable without restricting processing flexibility
Solution Approach 2:
The system performs preliminary actions by proactively collecting and storing runtime metadata, artifacts, and execution information during data processing operations. This preliminary collection enables subsequent workflow visualization and analysis without interfering with the flexible schema-on-read processing approach
3Loss of information
If comprehensive metadata collection and visualization infrastructure is implemented, then workflow visibility and system management are improved, but device complexity and resource requirements increase
Solution Approach 1:
The patent introduces a metadata service as an intermediary component that sits between the distributed computing cluster and users. This service aggregates, processes, and visualizes metadata from various sources without requiring complex direct integration between all system components, simplifying the overall infrastructure while maintaining comprehensive workflow visibility
Data Source
AI summary
Transient computing clusters can be temporarily provisioned in cloud-based infrastructure to run data processing tasks. Such tasks may be run by services operating in the clusters that consume and produce data including operational metadata. Techniques are introduced for tracking data lineage across multiple clusters, including transient computing clusters, based on the operational metadata. In some embodiments, operational metadata is extracted from the transient computing clusters and aggregated at a metadata system for analysis. Based on the analysis of the metadata, operations can be summarized at a cluster level even if the transient computing cluster no longer exists. Further relationships between workflows, such as dependencies or redundancies, can be identified and utilized to optimize the provisioning of computing clusters and tasks performed by the computing clusters.