Your data platform was built to inform people. But to get the most out of AI agents, you need a data platform that allows AI to actually use data and perform tasks. Leave Teradata, Netezza, Oracle, SQL Server or Synapse, or redesign the Microsoft Fabric, Databricks or Snowflake platform you already run, up to 4–5× faster than a manual rewrite, at around half the cost.
You leave
TeradataNetezzaOracle & ExadataSQL Server & SSISAzure SynapseCognos & BOAI rebuilds
People approve every rule
You land on
Microsoft FabricDatabricksSnowflakeRetire unused tables, pipelines and reports, cut compute and simplify maintenance, instead of paying to move them.
Reusable architecture, automation and AI-assisted engineering, so a new source or KPI takes days, not a project.
Validated business rules, consistent models and reconciliation, so finance and operations see the same figures on the old and new platform.
Governed definitions, relationships and access, so Copilot, Genie, Cortex and your own agents can act on the data, not just query it.
Data Semantics helps businesses modernize their data platforms by migrating legacy systems to modern platforms such as Microsoft Fabric, Databricks and Snowflake, or optimizing existing ones. Using AI to automate code conversion, fixes, validation and reconciliation, we deliver modernization faster than a manual rewrite, and at around half the cost. We also transform existing business logic into knowledge graphs and structured context, making enterprise data ready for AI agents to do more than generate reports and actually take action.
Moving to Fabric, Databricks or Snowflake modernizes the technology. Most platforms still carry a dashboard-era design: meaning hidden in report formulas, nightly loads, access set up for people and output that ends in a chart. That works for people reading reports. It breaks when an agent has to decide and act.
Some teams need to leave legacy technology; others need to get more from a cloud platform they already run. Warehouses, pipelines and reports usually move together, so the same rules, models and tests carry through from source to report.
Data Warehouse Modernization moves a legacy warehouse onto a modern lakehouse or cloud warehouse, with the business logic carried over, cleaned up and proven equal, including industry data models such as Teradata FSLDM.
ETL & Pipeline Modernization rebuilds ageing ETL jobs as tested, monitored pipelines that are cheaper to run and easier to change.
Report & Semantic Model Migration moves reporting onto governed semantic models in Power BI and Fabric, and retires the reports nobody uses.
Already on Fabric, Databricks or Snowflake? You have modern technology. Now give it a design AI agents can act on, usually without another migration.
AI takes on the slow, manual work that makes platform migrations drag on, so you run two platforms for weeks, not years. Our engineers review every output and your data owners approve every rule. Target modeling, code generation and data quality checks run in DataPrep 360 today. Architects own the design, engineers review every generated object and business owners validate the critical calculations.
AI scans every table, pipeline, procedure and report in the old platform, with usage, and flags what nobody uses any more.
You get: a move, fix or retire list
Checked by: your data owners decide what moves
AI reads legacy SQL, ETL and report logic and writes each business rule out in plain language, with its source.
You get: a plain-language rule catalog
Checked by: architects verify each rule against the source
AI drafts Spark SQL, PySpark, dbt or Fabric pipeline versions of legacy procedures and ETL jobs.
You get: converted code in your repository
Checked by: engineers review and test every object
Runs in DataPrep 360 →Medallion layers and dimensional models are proposed from the extracted rules and source tables.
You get: a documented target design
Checked by: architects approve the design
Runs in DataPrep 360 →When a pipeline or test fails, AI reads the error and proposes the fix.
You get: fewer stalled builds
Checked by: an engineer approves every fix
Old and new outputs are compared, from tables and KPIs to full reports, and every difference is explained.
You get: a parity report per domain
Checked by: data owners sign off before switch-over
Moving fast only helps if leadership trusts the new numbers. Every rule we extract and rebuild sits in a decision record: what the old platform did, what the new one does, who approved it and why. So every difference at parity is either a defect to fix or an improvement someone signed off.
Same numbers where business rules are unchanged. Explained and approved differences where improvement is intended.
Legacy rules are written out in plain language before they're rebuilt, so you don't carry years of workarounds onto a new platform.
Usage data shows which tables, jobs and reports nobody touches. Retiring them cuts migration effort and running cost.
Old and new outputs are compared after every build, so each domain can switch over as soon as it's proven, not at one big-bang date.
Workloads move by business domain or release wave, with old and new run in parallel only until each domain is signed off.
An ontology is a shared model of your business: the customers, products, orders, contracts and KPIs, how they relate, and which system each comes from. Most migrations throw the old platform's logic away and rewrite it. We extract it, have it approved, and turn it into the semantic layer, knowledge graph and context graph your reports and AI agents run on, ready for Fabric IQ, Copilot, Databricks Genie, Snowflake Cortex or your own agents. That is what lets the new platform do what the old one never could: let agents act on your data, not just report on it.
Every rule we extract from legacy code becomes a governed definition, so "net revenue" or "active customer" means one thing everywhere.
The ontology sits in the semantic layer of Fabric, Databricks or Snowflake, not in a separate tool you have to maintain.
AI agents and copilots know what each table means, how it connects and which policies apply, so their answers can be trusted.
The same model serves Power BI, decision intelligence and AI agents, and the next domain you migrate.
Explore our ontology and knowledge graph services
Beyond modernization: Data Governance & Quality · BI & Analytics · Decision Intelligence · Agentic AI
We don't sell platform licenses, so our advice follows your estate, skills and AI plans. If you've already chosen, we build on your choice.
One platform for data engineering, warehousing, Power BI and AI on OneLake. A natural fit if you're standardized on Microsoft or moving off Azure Synapse.
A lakehouse built for heavy data engineering, streaming and machine learning, with strong open formats. A fit for engineering-led teams and large data volumes.
A SQL-first cloud data platform with simple scaling and secure data sharing across clouds and partners. A fit for SQL-heavy teams and multi-cloud estates.
Tell us what you run today and where you want to go. We’ll walk you through the options and what each one involves.
The routes we run most often. If yours isn’t here, we have probably done it: we have modernized legacy platforms for 14 years.
Azure SynapseMicrosoft Fabric
Dedicated SQL pools, Synapse pipelines and Power BI models onto OneLake.
TeradataFabric, Databricks or Snowflake
Including industry data models such as FSLDM, rebuilt on the new platform.
NetezzaFabric, Databricks or Snowflake
Appliance workloads moved off before end of support.
Oracle & ExadataFabric or Databricks
PL/SQL logic converted and proven equal.
SQL ServerMicrosoft Fabric
Warehouses, SSIS, SSAS and SSRS moved together.
SAP BWFabric or Databricks
BW models and extracts rebuilt for analytics and AI.
Hadoop & ClouderaDatabricks
Hive, Spark jobs and HDFS data onto Delta Lake.
SSIS, Informatica & ODIFabric pipelines or dbt
Jobs rebuilt, tested against the original runs.
Cognos, Business Objects, OBIEE, MicroStrategy & TableauPower BI and Fabric
Reports rationalized onto shared semantic models.
Data from more than 18 source systems brought onto one governed platform, with reporting rebuilt on shared models and system performance improved by more than half.
An enterprise data warehouse on the Teradata FSLDM banking data model, with analytical models and APIs serving the bank's reporting and analytics.
A multi-cloud data estate consolidated onto Microsoft Fabric, cutting cloud spend in half and simplifying operations.
No big-bang cutover. Each domain moves when its numbers are proven, and a typical program takes 3 to 4 months after the assessment.
The Data Platform Assessment inventories workloads, costs, dependencies and rules, and recommends what to migrate, optimize, keep or retire.
Move or optimize one subject area, such as finance or sales, end to end, with parity proven on real numbers.
Remaining domains move in controlled releases on the same mappings, tests and rule library.
Switch off the old platform and let us run and improve the new one through Managed Data & BI Services.
You may not need a new platform. You may need to simplify workloads, modernize one critical domain, cut running cost or make your data reliable enough for AI. The assessment tells you which, with a business case.
Our architects, with AI-assisted tooling, review your current platform against what your business and AI agents need, and hand you a costed plan, whether the target is Microsoft Fabric, Databricks, Snowflake or the platform you already run.
Our engineers deliver the migration, with AI throughout, domain by domain, then run and improve the new platform as a managed service.
Choosing a path
Because most modern platforms still carry a dashboard-era design: business meaning hidden in report measures, nightly loads, access set up for people and output that ends in a chart. Data Semantics redesigns the platform for AI agents to act, with shared business meaning in an ontology, data as fresh as decisions need, per-agent access, governed write-back to your systems and continuous quality monitoring. This usually needs no new migration.
It depends on your existing estate, skills and AI plans. Fabric suits organizations standardized on Microsoft and Power BI. Databricks suits heavy data engineering and machine learning. Snowflake suits multi-cloud data sharing and SQL-first teams. We deliver on all three and help you choose before you commit.
Not always. Many AI use cases can start with targeted integration, data quality fixes, a semantic model and governed access to the systems you have. We modernize the part of the foundation the use case needs, rather than rebuilding everything first.
How it works
Automated parity checks compare old and new outputs at every level, from row counts and totals to KPIs and full reports, after every build. Each difference is traced to a defect to fix or a deliberate improvement that a business owner approved.
Data Semantics assess usage, dependencies, business rules and design before anything moves. Objects nobody uses are retired, outdated patterns are redesigned, and critical calculations are carried over through documented, approved validation, so the new platform starts clean.
Migration routes
Inventory the Synapse workloads, decide what moves, what changes and what retires, then rebuild SQL pools, pipelines and notebooks in Fabric and move the Power BI semantic models alongside. We prove parity between old and new before switching Synapse off.
Timeline and cost
A typical Data Semantics program takes 3 to 4 months after the assessment, moving one subject area at a time. Larger estates are phased by domain, so each part goes live as soon as its numbers are proven.
Yes, for as short a time as possible. Each subject area runs on both platforms only until parity is proven and signed off, then the old version is switched off, so you avoid paying for two platforms for years.
The Data Platform Assessment is Data Semantics’ fixed-price review of your current platform: architecture and workload inventory with usage, dependencies and technical debt, pipeline reliability, data quality and reconciliation gaps, cost and performance opportunities, and AI readiness. You get recommended priorities, an indicative investment and a roadmap, whether the target is Fabric, Databricks, Snowflake or the platform you already run.
It starts with the fixed-price Data Platform Assessment, which inventories your estate and gives you a costed plan. The program is then priced against that plan, so you know the cost before committing to the move.
Whether you’re leaving a legacy warehouse, want to get more from modern platforms, or preparing your data for AI agents, we’ll show you the practical path, with the numbers to back it.
