Data Funneling & Transformation Ingest, standardize, and curate data with contracts, tests, lineage, and SLAs. Turn scattered feeds into clean, modeled data your teams can trust. We build data funnels—from raw ingestion to standardized, curated layers—using ETL/ELT, CDC, and transformation frameworks with strong contracts, tests, and lineage. Ops teams get observability and SLAs; leadership gets dashboards that reflect reality.
Key Benefits Trusted Metrics: Conformed models across tools
Fresh & Timely: CDC/streaming with freshness SLAs
Audit-Ready: Lineage, reconciliation, and approvals
Cost-Efficient: Incremental loads, partitioning, selective reprocess
Observable: Health, lag, and error budgets
What We Deliver Source Assessment & Mapping: inventory feeds, ownership, update cadence, and constraints. Funnel Architecture: landing → staging → standardized (conformed) → curated/semantic layers. Transformations: normalization, enrichment, SCD handling, KPI-ready aggregates. Quality & Reconciliation: constraints, duplicate checks, totals balancing, and drift detection. Security & Privacy: PII classification, masking/tokenization, environment segregation. Runbooks & SLAs: freshness/error budgets, backfill & replay procedures, on-call steps. Data Funnel Stages Landing (Raw): immutable copies from APIs, files, webhooks, or CDC logs; schema snapshotting. Staging: type casting, null policy, basic dedupe, key standardization. Standardized (Conformed): domain models (Accounts, Orders, Cases, Users), survivorship rules. Curated/Semantic: KPI/subject-area marts; audit columns; snapshot tables for reporting. Transformation Patterns Row-Level: joins, merges, de-dup, keys/ID stitching, survivorship. Time & History: SCD1/2, audit columns (created/updated/effective), late-arrival repair. Aggregations: windows (tumbling/sliding), daily rollups, incremental materializations. CDC Blends: upserts with idempotency (hash keys), delete handling, and soft-deletes. Data Contracts, Schemas & Lineage Contracts: OpenAPI/JSON Schema; versioning & backward-compatibility guidance. Schema Evolution: add-only, deprecations, and breaking-change playbooks. Lineage & Metadata: column-level lineage, owners, data dictionary, and change logs. Quality Gates & Controls Validation: not-null/unique/accepted-values, referential checks, thresholds. Reconciliation: source-to-target totals, hash totals, variance alerts. Escalations: DLQ triage, automated ticketing, rollback markers. Performance & Cost Management Incremental Loads: change flags, partition pruning, clustering. Compute Efficiency: parallelism, adaptive batching, selective reprocessing. Storage Strategy: cold vs. hot, compaction, retention rules. Operationalization CI/CD for Data: tests on pull requests, environment promotion, release markers. Backfills & Replays: reproducible transforms with audit trails. Observability: freshness, volume, schema, and distribution checks with alerts. Delivery Approach Assess sources, contracts, and reporting needs; define SLAs and governance. Design the funnel and models; pick batch/stream mix. Build ingestion + transformations with test coverage and lineage. Validate quality and reconciliations; prove KPIs match the ground truth. Operate with dashboards, alerts, and continuous improvements. FAQs Q: How do you keep reports consistent across tools?
Q: Can you support both batch and streaming feeds?
Q: How is sensitive data handled?
Q: Will this fit our CI/CD process?
Power Dashboards with Data You Can Defend. 
