Industry: Life Sciences
Client Type: Fortune 500
At a Glance: How Modak built a scalable, template-driven metadata-driven query automation framework that produced 8,000+ unified queries and accelerated scientific analysis through enterprise data automation at scale.
A global life sciences organization wanted to accelerate scientific analysis by unifying compound, assay, curve, and project-level data. Their R&D teams relied on multiple source systems like the Chemical Operations Data Store (CODS), Lot And Compound Information Repository (LACIR), Protocol Authoring and Cataloguing Tool (PACT), Knowledgebase of Assays, Tests, and Experiments (KATE), each with its own schema, naming conventions, and updating cycles. This fragmentation created friction when scientists attempted to run cross-domain analytics for initiatives like Live Design, limiting the organization’s ability to operationalize life sciences analytics use cases that depended on timely, harmonized chemistry data.
The organization’s vision was to build a Comprehensive Chemistry analytical environment, a single foundation layer that enables life sciences data integration by standardizing all chemistry-related datasets into an analytics-ready schema.
This environment served as the backbone for Live Design workflows and was part of a multi-maturity roadmap that progressively enriched metadata, expanded lineage, and added new structured/unstructured sources —powered by scalable enterprise data automation.
Challenge
To support scientific initiatives like Live Design, the organization needed more than simple SQL automation; This needed a repeatable, scalable metadata-driven query automation approach one capable of harmonizing thousands of chemistry datasets, enforce consistent rules, and deliver analytics-ready outputs daily.
The existing manual, fragmented, and hardcoded setup could not meet these requirements, and scaling further would only compound technical and operational challenges. The challenges were rooted not in the availability of data, but in the complexity and volume of transformations needed to make that data usable.
1.1 Fragmented tables and inconsistent schemas
The chemistry ecosystem spanned thousands of tables across CODS, LACIR, PACT, and KATE. While these systems captured valuable experimental and compound information, they evolved independently over many years.
This resulted in:
- Multiple tables representing similar concepts but structured differently
- Columns carrying identical scientific meaning but expressed with different names, units, or formats
- Variations in data completeness and quality
- No standardized foundation to support a scalable data transformation framework across all sources
Before any scientific analysis could take place, engineers had to manually reconcile these inconsistencies that became unsustainable as data grew.
1.2 Manual SQL generation didn’t scale
The traditional approach relied on writing SQL transformations table-by-table. It took nearly a full day to unify just 30 tables, and completing all 8,000 required queries demanded more than 200 hours of repetitive manual work.
- Every table needed its own custom SQL, even though much of the logic was identical
- Human-created queries introduced inconsistencies and errors
- Different team members interpreted transformation rules differently
- Updates had to be applied manually, making reprocessing slow and unpredictable
This manual model fundamentally could not support the pace or volume of modern enterprise data automation or high-throughput scientific pipelines.
1.3 Hardcoded rules created long-term maintenance issues
The key chemistry calculations PIC50, PEC50, PAC50, IC50 (µM/nM), and more were encoded directly into a Java application. While this approach worked initially, it introduced significant technical debt:
- Even small schema updates or new transformation logic required modifying and rebuilding application codes
- Hardcoded rules were difficult to audit, validate, or extend
- Introducing new templates or scientific rules meant reengineering the entire pipeline
- Any engineering change introduced risk, slowing down scientific workflows
As the dataset expanded, the lack of a flexible data transformation framework became a major constraint.
1.4 Lack of reusability and no prioritization of transformations
Despite similar logic being applied across thousands of tables, the engineering teams lacked a reusable framework. Each query was written as a standalone script, with no shared template, priority order, or metadata-driven structure.
- Every transformation was rebuilt from scratch
- No mechanism existed to apply rules consistently across datasets
- Complex transformations could not be prioritized or sequenced reliably
- Scaling to thousands of transformations was impossible without metadata-driven query automation
The absence of a reusable engine created a recurring cycle of duplication, rework, and inconsistency.
Solution
To address the scale, complexity, and fragmentation in the chemistry data landscape, Modak engineered a metadata-driven query automation platform that replaced manual SQL development with a dynamic, template-based data transformation framework. The approach centered on establishing a unified analytical foundation, formalizing transformation logic into reusable structures, and enabling high-throughput automated SQL query generation with consistent scientific rules.
2.1 Establishing a unified schema as the reference model
The first step was defining a single, authoritative schema that captured how chemistry, assay, curve, and protocol attributes should appear in an analytics-ready state. This required studying thousands of tables across CODS, LACIR, PACT, and KATE, identifying overlaps, resolving naming inconsistencies, and determining which fields were essential for downstream workflows such as Live Design.
Once finalized, this unified schema became the anchor for the entire transformation process—every mapping, rule, and query was designed to populate this schema with clean, standardized data.
2.2 Creating a metadata-driven mapping layer to replace hardcoded logic
To eliminate brittle, Java-based rule definitions, Modak created a central mapping table that encoded how each source field should map to the unified schema, along with the associated transformation logic.
This mapping layer included:
- Source column names and expected unified column names
- Calculation logic for PIC50, PEC50, PAC50, IC50 variants
- Data type conversions and formatting rules
- Rule precedence, fallback logic, and conditional transformations
- Template category (e.g., PXC50, IC50_UM, PEC90, etc.)
This structure allowed all transformation logic to be updated through metadata rather than code, enabling extensibility while strengthening metadata-driven query automation governance.
2.3 Designing a standardized, parameterized SQL template
Instead of writing SQL manually for each table, Modak developed a single reusable SQL template capable of adapting itself dynamically based on the mapping metadata. The template was built to handle:
- Cascading scientific transformations
- Conditional logic based on available columns
- Automatic generation of CASE statements, joins, and field derivations
- Sequence-aware rule execution (e.g., readout mappings before analyte mappings)
This allowed consistent automated SQL query generation across thousands of chemistry data sets.
2.4 Engineering an automated query-generation framework
Using the unified schema, metadata mapping, and parameterized template, Modak developed a query-generation engine that could automatically assemble SQL for every eligible table.
The framework performed the following steps:
- Scanned for new or updated tables across chemistry domains
- Pulled relevant mappings from the metadata layer
- Applied transformation rules in the correct priority order
- Generated fully executable SQL queries tailored to each table
- Validated field availability and logged discrepancies for review
This shifted the workload from manual query development to metadata-driven query automation.
2.5 Enabling parallel execution to process thousands of tables efficiently
To overcome the limitations of serial SQL execution, Modak introduced parallelized query execution, allowing large volumes of queries to be generated and executed much faster than the previous manual process. This drastically reduced processing time and enabled daily refresh cycles that previously took days to execute manually.
The execution engine included:
- Intelligent batching based on table size and dependencies
- Retry logic for transient failures
- Detailed logging for auditability and debugging
- Full lineage reporting for every unified table
This ensured reliable enterprise data automation across the entire chemistry domain.
2.6 Publishing unified tables into Live Design-ready formats
Once transformed, tables were written into the unified schema and packaged into Live-Design-compatible summary formats, ensuring scientists could immediately work with the latest, consistently structured chemistry data.
The process supported:
- Selective deletion and insertion of updated rows
- Refreshing downstream summary tables
- Feeding enriched datasets directly into scientific and analytical workflows
This closed the loop from raw datasets → unified schema → analytics-ready output.
2.7 Designing for ongoing schema evolution and future maturity levels
Finally, the architecture was built to adapt. New columns, new templates, and new scientific rules could be added through metadata rather than rebuilding code. This made the system ready for future maturity levels that would incorporate additional domains, unstructured sources, and deeper ontology-driven mappings.
Impact
The implementation of the metadata-driven query automation framework fundamentally changed how the organization processed chemistry data. What was once a labor-intensive, inconsistent, and slow workflow became a streamlined, scalable, and fully automated engine capable of supporting scientific decision-making at scale.
Reduction in Processing Time
- From 200+ manual hours → fully automated generation & execution
- Thousands of queries could be produced instantly, not manually rewritten.
- Time to unify 30 tables dropped from 1 full day → minutes
Enhanced Data Quality
- Mapping table ensured consistent rule application
- Fewer manual errors
Easier Maintenance
- Rules were removed from Java code
- Updates happened within mapping tables, not source code
- Onboarding new columns into the unified schema could be automated going forward
The unified environment now supported
- Live Design integration
- Cross-domain analytics
- Ontology-aware transformations
- Future ingestion of external unstructured sources in Maturity Level 3
Outcome
The metadata-driven query automation framework transformed chemistry data integration from a manual, table-by-table exercise into a scalable, repeatable analytical capability. By replacing hardcoded logic and hand-written SQL with unified schemas, parameterized templates, and automated SQL query generation, Modak enabled the organization to operationalize thousands of chemistry transformations with speed, consistency, and scientific rigor.
Modak’s architecture-led approach did more than automate query generation. It established a durable enterprise data automation backbone, that accelerates research, reduces engineering dependency, and positions the organization to advance confidently across future maturity levels—turning data complexity into a sustained advantage for scientific innovation.