Schemas & Semantics
- Type and compatibility checks
- Contract verification for renamed or removed fields
- Safe materialization across models
How BoltPipeline Works
You write SQL — or your AI does. From there, the lifecycle takes over: SQL is parsed, validated, certified against your live database, and only then deployed to run. Every stage produces real artifacts. Every gate is enforced.
The 30,000-Foot View
You write SQL. The platform handles compilation, validation, lineage, and deployment.
1
Author SQL business rules
2
Analyze structure, semantics, and dependencies
3
Profile data and establish baselines
4
Validate changes and detect drift with impact
5
Generate certified, executable pipeline artifacts
How It Fits Together
A simple, repeatable flow that separates authoring from implementation so teams move faster without sacrificing trust.
BoltPipeline is the unified control plane for SQL pipelines. Whatever generates your SQL — AI tools, dbt, your team, transformation frameworks — flows through BoltPipeline before reaching your warehouse. Four governed stages — Plan, Certify, Operate, Govern — backed by a unified metadata + lineage graph. Output: certified pipelines to Snowflake, Databricks, Postgres, and any warehouse.
1
PLAN — Build with Intelligence
SQL parsing and compilation. Lineage derivation. Dependency graphs. Impact prediction. Execution planning. SCD and schema automation. Author in plain SQL — the platform handles the rest.
2
CERTIFY — Validate Before Deploy
30+ certification rules run against your live database. Schema and data validation. Lineage impact analysis. Drift and freshness checks. Environment comparison. Security and policy checks. Risk scoring and approval gates.
3
OPERATE — Run with Confidence
Orchestration and scheduling. Pipeline monitoring. Data freshness tracking. Post-deploy drift detection. Alerting and incident management. Performance and cost insights.
4
GOVERN — Enforce & Audit
Ownership and stewardship. Data contracts and policies. Access and permissions. Audit logs and lineage. Compliance and standards. Change history and rollback.
Step 1
Author: Express Business Logic in SQL
BoltPipeline starts where your team already is: SQL. Engineers and analysts describe what the data should mean, not how to wire pipelines by hand.
- Write plain SQL (.sql) files — no DSLs, no proprietary runtime
- Optional hints express intent (materialization, SCD behavior)
- AI assistance drafts or refines SQL grounded in real metadata
- SQL remains the single source of truth — you own every artifact
Step 2
Analyze, Validate & Certify (Shift Left)
This is where BoltPipeline does the heavy lifting. The platform analyzes SQL intent, validates correctness, and surfaces issues before anything ships.
The output is a set of certified artifacts: executable SQL, validation results, lineage, profiles, and audit metadata — portable and customer-owned.
- Schema, type, and contract compatibility checks
- Join correctness and relationship safety validation
- Column profiling and baseline establishment
- Distribution and drift awareness
- Dependency analysis and execution graph generation
- Certified, executable pipeline artifacts output
Step 3
Deploy & Operate in Your Environment
BoltPipeline does not replace your runtime. You deploy and operate pipelines where your data lives.
BoltPipeline provides visibility, safety signals, and governance context — without taking control away from your team.
- Run pipelines directly inside your database
- No data movement outside your boundary
- Artifacts integrate with Airflow, CI/CD, and existing tooling
- Continuous drift detection and impact awareness after deploy
- You own runtime, scheduling, and execution decisions
What Happens at Each Stage
Every stage produces real artifacts and enforces real gates. Nothing is optional.
SQL Compilation
Parse, resolve, generate
- Parse SQL into dependency graph
- Resolve table references and column types
- Generate execution-ready DML
SCD Automation
Tag it. We build the MERGE.
- Auto-generate SCD Type 0, 1, 2 merge logic
- Inject audit columns (created_at, updated_at, etc.)
- Validate natural key and primary key selection
30+ Rule Validation
Hard gate before production
- Schema compatibility and column existence
- Join correctness and cardinality checks
- Type safety and SCD contract enforcement
Column-Level Lineage
Source to target, every column
- Derived from SQL — no manual annotation
- Tracks transformations across every step
- Powers impact analysis and root-cause tracing
Profiling & Baselines
Know your data before you ship
- Push-down profiling inside your database
- Null rates, uniqueness, distributions, cardinality
- Baselines established for drift detection
Drift & Health Scoring
Continuous after deployment
- Schema drift detection on every run
- Volume and freshness anomaly monitoring
- Pipeline health score with root-cause tracing
What the Platform Validates
BoltPipeline continuously validates SQL pipelines as they are implemented and executed — before anything reaches production.
Joins & Relationships
- Join correctness and duplication safeguards
- Detection of unsafe join patterns
- Guided remediation suggestions
Data Profiling
- Completeness and uniqueness baselines
- Range, length, and distribution tracking
- Trend awareness over time
Drift & Impact
- Schema and data drift detection
- Downstream blast-radius analysis
- Change explainability before deploy
Cross-Environment Drift Detection
Your dev, QA, and prd should match. When they don't, you usually find out at deploy time — badly. BoltPipeline compares schemas + data across every environment pair and blocks promotions when divergence exceeds policy.
No other data platform ships this. Schema drift across environments is the silent killer of release confidence. We diff every environment pair on cadence + at promotion time. Block, classify, and remediate — before customers see broken dashboards.
Cross-Database Reconciliation
Your data lives across multiple databases. The same customers, orders, and products exist in different places — slightly different names, slightly different types. Until now, finding those overlaps meant months of manual analysis.
BoltPipeline profiles every connected database and automatically identifies duplicate and overlapping objects using deterministic scoring and AI semantic analysis. The result: a clear map of what can be consolidated, what needs migration, and the exact column-level mappings to get there.
- Automatic similarity detection across databases
- AI-powered semantic matching for ambiguous column names
- Database-to-database migration with automated DDL and type mappings
- Reconciliation queries to validate data integrity
- Cost optimization — eliminate redundant storage and compute
The 4-Step Flow
1
Profile — BoltPipeline collects schema, column stats, and data quality metrics from every connected database
2
Score — A deterministic engine compares table names, column overlap, type compatibility, cardinality, and null ratios
3
Resolve — AI semantic analysis maps ambiguous columns and recommends consolidation direction
4
Migrate — Generate DDL scripts, type mappings, and reconciliation queries — ready to execute
The Three-Layer Scoring Engine
No black boxes. Three layers of analysis run on every pair of tables.
L1
Deterministic Scoring
Fast, rule-based comparison using structured metadata. No AI needed — pure math.
- • Table name trigram similarity
- • Column name Jaccard overlap
- • Data type compatibility matrix
- • Row count proximity
- • Cardinality & null ratio matching
L2
AI Semantic Resolution
For ambiguous matches where names differ but meaning aligns. AI resolves what rules can't.
- •
cust_id↔customer_identifier - • Semantic type matching (email, phone, address)
- • Business context inference
- • Confidence scoring with explanations
L3
Migration Plan Generation
From scored matches to executable migration artifacts. Ready to run, not ready to guess.
- • DDL scripts with cross-platform type mappings
- • Column-level mapping documentation
- • Reconciliation queries (pre & post migration)
- • Estimated cost savings from consolidation
Why metadata matters
Connecting AI to Your Database Isn't Enough
AI can connect to your database — that's easy. But all it sees is table names and column types. Without structured metadata — column roles, SCD strategies, PII classifications, data quality scores, relationship cardinality — AI guesses. Confidently. Incorrectly.
What AI gets from a raw database
- ✗ Table names:
dim_customer - ✗ Column names:
id, email, status - ✗ Data types:
varchar, integer, date - ✗ No context. No quality. No relationships.
Result: hallucinated SQL that looks right but isn't.
What AI gets from BoltPipeline
- ✓ Column roles: primary key, foreign key, business key
- ✓ SCD strategy: Type 0, 1, or 2 with tracking columns
- ✓ PII classifications, data quality scores, health scores
- ✓ Relationship cardinality, lineage, drift baselines
Result: correct SQL, first time. 80+ fields of context.
We bring clarity to your data model. We never see your data. Our agent sends structure and statistics — table names, column types, null rates, uniqueness scores. Never row values. Never PII. Never data previews.
Business Outcomes
BoltPipeline reduces pipeline failures, review cycles, and operational overhead — while giving leadership confidence that data products are governed, explainable, and safe to scale.
Speed
Weeks to hours
From SQL to certified, production-ready pipelines
Trust
Built in
Certification gates, lineage, and explainers at every stage
Flexibility
No lock-in
SQL-first, portable ANSI artifacts you own
Cost
In-DB only
No external compute, no data movement, fewer incidents
Compliance
By design
Data stays in boundary with audit-ready evidence
See It on Your SQL
Walk through a real pipeline using your schemas and business rules — no migration, no lock-in, no data leaves your database.