DataVolve: AI-Driven Enterprise Data Migration from Legacy Platforms to Databricks, Snowflake and Microsoft Fabric
DataVolve is Tarento's AI-driven data migration accelerator. It moves enterprise data estates from legacy databases, ETL tools and on-premises warehouses to Databricks, Snowflake and Microsoft Fabric. DataVolve automates the four stages that decide migration timelines: discovery, pipeline conversion, testing and post-migration operations. Enterprises use it to reduce manual rewrites, prove that migrated data matches the source, and keep the new platform stable after go-live.
Why are enterprise data migrations slow and risky?
Enterprise data migrations are slow because business logic is spread across many legacy tools, each with its own SQL dialect, workflow format and scheduling model. Teams must find this logic, rewrite it, and prove that the new system produces the same results. When they do this by hand, the work takes months and errors often appear only after cutover.
A typical legacy estate has grown over 10 to 20 years. It usually contains:
- Several database platforms, each with its own SQL dialect and stored procedure language
- More than one ETL tool, often from different acquisitions or business units
- Orchestration logic in workflows, scripts and job schedulers
- Undocumented dependencies between pipelines, reports and downstream systems
- Dead code, meaning pipelines and tables that nobody uses but that still run every night
These estates fail in predictable ways. Scope is underestimated because nobody has a complete inventory. Rewrites are inconsistent because different engineers solve the same pattern in different ways. Validation stops at row counts, so calculation errors reach production. After go-live, the new platform has no operational baseline, so incidents rise. For a wider view of how to sequence modernization, see Data Modernization Best Practices for Enterprises.
Who is DataVolve built for?
DataVolve fits enterprises that must move a large, mixed legacy estate to a modern cloud data platform without a long manual rewrite. It is most useful when the estate is too large or too varied for a single-platform code converter.
| Situation | Why DataVolve fits |
|---|---|
| Several legacy databases and ETL tools | One framework handles many source formats instead of one tool per source |
| Target platform not yet decided, or more than one target | Logic is converted once into a platform-neutral form, then generated for each target |
| A legacy platform reaching end of support | Automated discovery and conversion shorten the time to exit |
| Strict data accuracy or audit requirements | Multi-level testing produces evidence that source and target match |
| A small platform team after go-live | AI-based operations reduce manual monitoring and incident work |
For one example of an end-of-support deadline that drives these programs, see PowerCenter End of Support: 4 Migration Paths for Enterprise Data Teams.
How does DataVolve migrate a legacy data estate?
DataVolve migrates a legacy estate in five stages. It discovers what exists, converts the logic into cloud-native pipelines, tests that results match, runs a controlled cutover, and then operates the new platform with AI-based monitoring. Each stage produces an output that the next stage uses, so nothing depends on memory or spreadsheets.
1. Discover 2. Convert 3. Test 4. Cut over 5. Operate
Metadata, lineage, ─▶ Platform-neutral ─▶ Row, checksum, ─▶ Parallel run, ─▶ Monitoring,
complexity score, parsing, rule-based schema, metric go/no-go gates, anomaly detection,
dependency graph, transformation, and business-rule rollback plan self-healing,
dead-code list native notebooks checks cost optimization
and pipelines
Stage 1: Automated discovery and assessment
An automated agent scans the legacy landscape, extracts metadata and produces a discovery report. The report gives the program a fact base before any code is converted. It typically includes:
- An inventory of pipelines, jobs, tables, views, stored procedures and security objects
- A complexity score for each object, which drives the effort estimate and the migration waves
- Lineage and dependencies, showing which pipelines feed which tables and reports
- Unused and duplicate objects, which you can retire instead of migrate
- Platform readiness findings, such as functions or patterns without a direct equivalent on the target
Retiring unused objects is often the fastest saving in the program. Every pipeline you do not migrate is one less pipeline to convert, test and run.
Stage 2: AI-assisted pipeline conversion
DataVolve converts legacy logic with three methods that work together:
- Dialect-agnostic parsing reads the source code and breaks it into a structure that does not depend on the source platform.
- Rule-based transformation applies tested conversion patterns, so the same legacy pattern always produces the same target code.
- AI assistance handles patterns that rules do not cover, and flags its output for engineer review.
The result is native code for the target platform, such as generated notebooks and pipelines for Databricks, Snowflake or Microsoft Fabric. Because all pipelines follow the same standards, they are easier to review, test and maintain than hand-written rewrites.
DataVolve also migrates security objects, such as roles, grants and access rules. Access control then does not need to be rebuilt by hand, and you can check it against the source before go-live.
Stage 3: Automated testing and validation
The testing suite checks that migrated pipelines produce the same results as the legacy system. Each level of testing catches a different kind of error:
| Test level | What it checks | Error it catches |
|---|---|---|
| Row count | Same number of records in source and target | Missing or duplicated loads |
| Checksum | Same values in each row and column | Truncation, type conversion and encoding errors |
| Schema | Same columns, data types, keys and nullability | Structural drift between source and target |
| Metrics and business rules | Same totals, aggregates and key business figures | Wrong joins, filters, rounding or time zone logic |
Row counts alone are not enough. A pipeline can load the correct number of rows with the wrong values. The combination of checks gives the evidence that audit and business teams need before they accept the new platform.
Stage 4: Controlled cutover
A safe cutover runs the legacy and target pipelines in parallel for an agreed period, and compares the results automatically. The team moves each wave to the new platform only when it meets agreed go/no-go criteria, such as zero open critical test differences and stable run times. A tested rollback plan keeps the legacy system available until the wave is accepted.
Stage 5: AI-based data operations after go-live
After migration, AI-based operations keep the new environment stable and cost-efficient:
- Proactive monitoring of pipeline run times and data volumes
- Anomaly detection for unexpected changes in data patterns
- Self-healing pipelines that retry or correct known failure types automatically
- Automated error handling and routing
- AI-driven cost optimization of compute and storage
For a detailed description of this stage, see AI-driven Data Operations: How DataVolve Strengthens the Post-migration Journey.
Which accelerators support each stage of the migration?
DataVolve includes a set of accelerators, and each one supports a specific stage of the lifecycle. Together, they make migration repeatable across business units and waves.
| Accelerator | Stage | What it does |
|---|---|---|
| Assessment Accelerator | Discover | Profiles the legacy landscape, scores complexity and supports the migration strategy |
| Migration Automator | Convert | Automates pipeline and code migration to the target platform |
| Accelerated development frameworks | Convert and build | Speed up new data flows and semantic models on the target |
| Testing automation tools | Test | Run end-to-end validation between legacy and target systems |
| DevOps and governance tools | Cut over and operate | Manage deployment, versioning, lineage and monitoring |
| AI Data Ops | Operate | Monitor, self-heal and optimize cost after go-live |
Programs usually begin with a Vector Sprint, a short engagement that covers landscape discovery, architecture planning and the migration strategy before the full program starts.
What business impact does DataVolve deliver?
DataVolve reduces engineering effort, total migration cost, time to value and post-migration incidents. Tarento measures each figure against a baseline estimate for a manual migration of the same scope.
| Outcome | Result | How it is measured |
|---|---|---|
| Engineering hours | 30 to 60% saved | Hours for automated conversion and testing compared with a manual rewrite estimate |
| Total migration cost | 20 to 40% lower | Full program cost, including people, tools and parallel-run infrastructure |
| Time to value | Value within 50 to 60% of a typical project timeline | Time until the first migrated workloads run in production |
| Post-migration incidents | 50 to 60% fewer | Production incidents in the first months after go-live |
The range depends on the estate. The upper end of each range is more likely when the estate uses repeated patterns, has good metadata and has a large share of objects that can be retired.
Worked example: what the ranges mean for one program
The following model is illustrative. Assume a manual migration estimate of 20,000 engineering hours:
- A 30% saving removes 6,000 hours. A 60% saving removes 12,000 hours.
- If discovery shows that 20% of objects are unused, those objects are retired before conversion. This reduces the scope before any automation saving applies.
- Automated testing also lowers the cost of the parallel run, because comparisons that used to be manual now run on every load.
Use your own baseline estimate in the same way to calculate the expected range for your program.
What are the most common data migration failures, and how does DataVolve address them?
Most data migration failures come from gaps in visibility, consistency or validation, not from the conversion itself. DataVolve addresses each gap at a specific stage.
| Common failure | Typical effect | How DataVolve addresses it |
|---|---|---|
| Incomplete inventory | Scope grows during the program, and deadlines slip | Automated discovery with a full inventory and dependency graph |
| Inconsistent rewrites | Hard-to-maintain code and uneven quality | Rule-based, standardized conversion patterns |
| Validation limited to row counts | Wrong calculations reach business reports | Checksum, schema, metric and business-rule tests |
| No operations baseline after go-live | Incidents rise and costs grow | AI-based monitoring, anomaly detection and cost optimization |
Problem, solution and vision
Problem. Legacy data estates hold years of business logic in many tools and formats. Manual migration is slow and hard to validate, and it often moves unused code and old errors to the new platform.
Solution. DataVolve combines automated discovery, standardized AI-assisted conversion, multi-level testing, controlled cutover and AI-based operations in one framework, across Databricks, Snowflake and Microsoft Fabric.
Vision. Data platform migration becomes a repeatable, measured process instead of a one-time, high-risk project. Enterprises can move workloads between platforms as their strategy changes, with automated evidence that every result still matches. Building a strong foundation before the move is part of this. See Building a Snowflake-Ready Data Foundation Starts Before Migration.
Start your data migration with a Vector Sprint
A Vector Sprint shows you the size, complexity and risk of your migration before you commit to a full program. It covers landscape discovery, complexity assessment, architecture choices and a clear migration strategy.
Frequently asked questions
What is DataVolve? DataVolve is Tarento's AI-driven data migration accelerator. It automates discovery, pipeline conversion, testing and post-migration operations to move legacy data platforms to Databricks, Snowflake and Microsoft Fabric.
Which target platforms does DataVolve support? DataVolve supports migration to Databricks, Snowflake and Microsoft Fabric. It converts legacy logic into a platform-neutral form first, then generates native code for the chosen target.
How does DataVolve validate migrated data? DataVolve runs automated row-count, checksum, schema, metric and business-rule checks between the legacy and target systems. These checks confirm that the new platform produces the same results as the old one.
How much effort can DataVolve save? DataVolve typically saves 30 to 60% of engineering hours and reduces total migration cost by 20 to 40%, compared with a manual migration of the same scope. The result depends on the size and structure of the estate.
Does DataVolve migrate security settings? Yes. DataVolve migrates legacy security objects, such as roles, grants and access rules, so teams do not need to rebuild access control by hand.
What happens after the migration is complete? AI-based data operations monitor the new platform, detect anomalies, fix known failure types automatically and optimize cost. This keeps the environment stable after go-live.
How do I start a DataVolve migration? Most programs start with a Vector Sprint. This short engagement covers discovery, complexity assessment, architecture planning and the migration strategy before the full program begins.
Planning a move to Databricks, Snowflake or Microsoft Fabric? Talk to Tarento about DataVolve and start with a Vector Sprint for your data estate.
AI-driven Data Operations: How DataVolve Strengthens the Post-migration Journey
