ETL-C Framework
Context-first data processing. Traditional ETL captures what happened. ETL-C captures why and how. In the generative AI era, context is the difference between data and intelligence.
Adding context as a first-class citizen in data processing
Your data knows what. AI needs why.
Traditional data pipelines strip context. "Transaction: $500" vs "Transaction: $500 (customer's first purchase after browsing for 3 weeks, during a promotional period, from mobile app)". The first is data. The second is intelligence.
Context gets lost
ETL pipelines focus on structure and storage. The business context, relationships, and semantic meaning are stripped away.
AI fills the gaps
Without context, AI models hallucinate to fill gaps. They miss nuance and relationships, requiring constant human correction.
Integration is painful
Matching records across systems without semantic understanding means manual mapping, fuzzy matching, and endless exceptions.
Value stays locked
Data exists but intelligence doesn't emerge. The connections that create insight remain hidden in stripped-down tables.
"Context isn't an afterthought. It's architectural."
ETL-C adds context as a stage
E (Extract) → T (Transform) → L (Load) → C (Contextualize). Context transforms raw data into intelligence that AI can actually use.
Click any stage to explore details
Pull data from source systems while preserving origin metadata.
Extract raw records, discard source metadata
- Capture source system context (timestamps, versions, lineage)
- Preserve extraction conditions (filters, queries used)
- Tag data provenance for downstream trust scoring
Clean, normalize, and reshape data while documenting transformations.
Apply business rules, output clean records
- Record transformation lineage (what changed, why)
- Adaptive transforms based on context (market conditions, segments)
- Preserve original values alongside transformed values
Persist data to target systems with full context preservation.
Write to data warehouse, update indexes
- Store data alongside contextual metadata
- Index for both structured queries and semantic search
- Maintain temporal context (point-in-time queries)
Enrich data with semantic meaning, relationships, and business context.
- Semantic embeddings: Vector representations for meaning-based retrieval
- Entity resolution: "S. Mitra" = "Subhadip Mitra" via contextual joins
- Relationship graphs: Connect entities across datasets
- Business context: Market conditions, customer segments, temporal patterns
- AI-ready output: Data that LLMs can reason about accurately
{"customer_id": "12345", "amount": 500, "date": "2024-01-15"}
{"customer_id": "12345", "amount": 500, "date": "2024-01-15", "context": {"customer_segment": "high_value", "purchase_intent": "first_purchase_after_browsing", "channel": "mobile_app", "campaign": "winter_promo", "confidence": 0.94}}
Context injection patterns
Context can be injected at different stages depending on your use case. Click each pattern to see when to use it.
Best for: Batch analytics, data warehousing, historical analysis
When to use: Context enrichment can happen asynchronously after data is safely stored. Good for large-scale batch processing where latency isn't critical.
Example: Nightly enrichment of transaction data with customer segments and market conditions.
Best for: Adaptive transformations, context-dependent processing
When to use: Transformation logic depends on context. Different business rules apply based on customer segment, market conditions, or data source.
Example: Financial data that aggregates differently during market volatility vs. normal conditions.
Best for: Complex pipelines, AI-native applications, real-time systems
When to use: Maximum context fidelity needed. Early context enables smart routing and adaptive transforms; late context adds semantic enrichment.
Example: Real-time fraud detection where early context determines processing priority, late context enables reasoning.
Best for: Real-time applications, event-driven architectures, live AI inference
When to use: Context must be fresh (sub-second). Events are contextualized in-flight before processing and storage.
Example: Live customer interactions where context (browsing history, segment, intent) must be available instantly.
Core ETL-C capabilities
Adaptive Context
Pipelines that dynamically adjust behavior based on context. Financial datasets aggregate differently during market volatility. Customer data enrichment varies by segment. Processing priority shifts based on business events.
Contextual Joins
Join datasets by meaning, not just keys. "Subhadip Mitra" in CRM, "S. Mitra" in transactions — traditional joins miss this. Contextual joins using embeddings + metadata achieve 95% confidence matches.
Context Store
Scalable infrastructure for contextual metadata. Embeddings for semantic representations, graph storage for relationships, time-series for temporal patterns — unified through a single query API.
Implementation patterns
Contextual Data Lake
Raw zone with context extraction, curated zone with semantic enrichment, consumption zone with context-aware APIs. Progressive contextualization as data moves through zones.
Real-Time Context Pipeline
Stream processing with context injection, event-driven context updates, sub-second contextual queries. For use cases where context must be fresh.
AI-Ready Data Platform
ETL-C pipelines feeding ML features, Context Store as feature store extension, semantic search for RAG applications. Context as the foundation for AI readiness.
How we help
ETL-C Assessment
$25K
2 weeks. Current pipeline audit, context gap analysis, opportunity identification, roadmap recommendation.
ETL-C Design
$50K
4 weeks. Target architecture design, context model definition, technology selection, implementation plan.
ETL-C Implementation
$150K+
8-16 weeks. Full pipeline implementation, Context Store deployment, system integration, team training.
Ready to add context to your data?
Request an ETL-C assessment to understand your context gaps and opportunities.