- Data Lifter
Harness Your Data at Superhuman Scale
Your data grows every year, but your team doesn’t. Data Lifter reads schemas, stored procedures, ETL code, documents, and query logs to trace decisions to their source and draft the engineering work that follows. When a source changes, only the answers that depend on it come back for review.
For data teams tired of solving the same problem twice.
Why Catalog Fields Stay Empty
A platform generalizes across thousands of customers, so it can only ship what is true everywhere. Everything true about your estate alone arrives as an empty field. An empty field is labor. That is why the catalog was bought, populated for a quarter, and then stopped being current.
Recovering that knowledge by hand is where the time goes.
45%
of a data scientist's time goes to loading and cleaning data before it can be used.
50%
of a data professional's week goes to redoing work that was already done or searching for work that couldn't be found.
Five tells that the meaning in your estate was never written down:
What gets lost in the estate
Which reading applies
The undocumented exception
The buried rule
The manual spreadsheet
The orphaned field
What someone recovers by hand, every time
Which of several meanings of a metric is right, and where it applies
A rule someone introduced years ago and never wrote down
Logic inside a stored procedure or ETL job still affects the outcome
A business-maintained source that is part of how the data is used
A field still consumed after the system that populated it was retired
See How Data Lifter Works
Cut Data Migration Time from Weeks to Days with Data Lifter
Eight Stages. One Connected Data Lifecycle.
The eight stages define the work Data Lifter is built to address, from discovering what exists to making data usable. Agents work across each stage, while engineers remain part of the decision loop.
01
Discovery
Reads the code and documents
- Source Inventory
- Complexity Estimator
- Dependency Mapper
- Document Reader
02
Ingestion and Movement
Drafts connectors and load plans
- Connector Agent
- Migration Optimizer
- Workload Profiler
03
Storage
Proposes schema, types, and layout
- Schema Analyzer
- Schema Optimizer
- Layer Designer
04
Transformation
Converts code and states the rule
- ETL Converter
- Transform Designer
- dbt Model Generator
05
Quality and Observability
Proposes rules and groups alerts
- Reconciler
- Data Quality Agent
- Anomaly Detection
- Schema Evolution
06
Catalog and Metadata
Drafts entries with evidence
- Data Profiler
- Lineage Mapper
- Glossary Agent
- Compliance Agent
07
Semantics and Metrics
Proposes metrics and entities
- Metric Definition
- Semantic Model
- Synonym Agent
- Ontology Builder
08
Consumption
Answers what's already been decided
- Text to SQL
- Dashboard Designer
- Data API Builder
Automate What the Source Makes Explicit
Schema, data types, and key relationships are fixed by the source and can be transformed through explicit, checkable rules. Cases like entity resolution, embedded logic, or metric selection may depend on undocumented context and require interpretation.
Data Lifter separates the two and routes cases requiring judgment for human review.
What Changes With Data Lifter
Each new engagement builds on prior work and shared context rather than starting from zero.
Handle 50% more data with a smaller team
Unlock More Time for Analysis and Decisions
Shorter Effort Peaks, Quicker Cycles
Catch Problems Earlier
Data Lifter Runs Where Your Data Already Lives
Deployment model
On-premises deployment supported, alongside cloud and hybrid options.
Data handling
Data Lifter can run entirely within your own environment using private and open-source models. No code or data needs to leave your control.
Auditability
Every proposal, the evidence behind it, and the engineer's decision that settled it are recorded and exportable.
Bring a Real Data Workload
Point Data Lifter at a real slice of your estate. The agents draft against it, and your engineers review every draft before it’s kept
What you walk away with:
- Estate inventory, complexity assessment, and dependency map
- Converted ETL and stored-procedure code with plain-English conversion logic
- A clear record of agent proposals and engineer decisions
Frequently Asked Questions About Data Lifter
Data Lifter reads your existing data estate, including schemas, stored procedures, ETL code, documents, and query logs, and uses that context to draft data engineering work. This can include source inventory, complexity assessment, ETL conversion, target schema proposals, catalog entries, and metric definitions. Your engineers review the output and make the final call where human judgment is needed.
No. Data Lifter works with the data platform you already have. It reads from your Lakehouse, catalog, compute environment, and source systems such as stored procedures, DDL, ETL packages, spreadsheets, and query history. It adds an agentic layer around the engineering work without replacing the platform underneath it.
A coding agent can help you generate code for a specific task. Data Lifter is designed to retain the context around that work. It keeps track of source information, dependencies, evidence, and decisions so the next piece of work can build on what was already understood instead of starting from scratch.
No. The agents propose; your people decide. When a question depends on business context, ambiguity, scope, or trade-offs around cost and access, it goes to a reviewer. The agent provides evidence and recommendation, while the reviewer makes the final decision.
Data Lifter links decisions back to the source information they were based on. If that source changes, the affected decisions can be flagged for review. Unrelated decisions do not need to be reopened, so changes are handled where they actually matter.
Data engineering decisions do not exist in isolation. The context graph captures what depends on what and how those relationships were established. This helps connect the work across the estate rather than treating each task as a standalone activity.