Report Infrastructure Health Analysis via SQL Lineage
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Solution Overview
Problem
Business intelligence reporting infrastructure is complex, leading to inaccurate or stale data in reports, making it difficult to determine the cause of failures and impacting downstream reports, and resulting in delayed and inaccurate failure reporting due to lack of contextual data and resource-intensive troubleshooting.
Innovation Solution
A report management device analyzes SQL queries to construct a database table inventory with lineage data, identifying driver and required tables, and generates graphical representations of table lineage to inform users about report failures and estimate repair times, facilitating efficient troubleshooting and repair.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of repair
If traditional manual troubleshooting methods are used to diagnose report failures, then technical representatives can identify and repair issues, but the process is time-consuming and resource-intensive due to lack of contextual data
Solution Approach 1:
The system performs preliminary actions by automatically generating and storing lineage data that maps the relationships between source tables, intermediate tables, and downstream reports before failures occur. This pre-established contextual information is readily available when failures happen, eliminating the need for time-consuming manual investigation of data dependencies and enabling rapid diagnosis and repair.
2Loss of information
If comprehensive lineage data is collected and stored for all database tables and reports, then accurate failure analysis and impact assessment can be performed, but the system complexity and data storage requirements increase
Solution Approach 1:
The system segments the complex reporting infrastructure into discrete, manageable units by creating lineage records for individual database tables and their relationships. Each lineage entry captures specific dependencies between source tables, intermediate tables, and downstream reports, breaking down the overall system complexity into structured, queryable components that can be analyzed independently when failures occur.
3Reliability
If manual methods are used to identify downstream reports impacted by a failure, then affected reports can be found, but the process is difficult and time-consuming without automated lineage tracking
Solution Approach 1:
The system implements feedback by automatically querying the stored lineage data when a failure is detected and providing immediate information about which downstream reports are impacted. This automated feedback loop eliminates manual tracing of data dependencies and rapidly communicates the scope and impact of failures to stakeholders, enabling timely responses.
4Measurement precision
If extensive troubleshooting and analysis are performed to determine report accuracy and failure consequences, then accurate failure reporting can be achieved, but the process is resource-intensive and delays reporting
Solution Approach 1:
The system performs preliminary actions by pre-computing and storing lineage information that documents the complete data flow from source tables through intermediate tables to downstream reports. When failures occur, this pre-established contextual data enables immediate assessment of report accuracy and failure consequences without requiring resource-intensive manual analysis, achieving both high precision and efficiency.
Data Source
AI summary
The disclosed technology relates to analyzing report infrastructure health to determine based on lineage the downstream tables and reports that may be impacted by an infrastructure failure to thereby improve reporting accuracy. An exemplary report management device may extract database table names identified in SQL queries. The SQL queries and report metadata are analyzed to identify driver and required tables, respectively. The database table names are stored in a table inventory and the database table names associated with one of the driver or required tables is marked. For each of the SQL queries comprising a target table, elements and source table names are extracted for source tables of the target table and an association is stored. Lineage data is generated based on the association when the report was not executed successfully. A graphical representation of the report lineage including a failed table and associated source table(s) is then output.