Data Engineering
Data Warehouse Modernization
Move from aging, expensive on-premises warehouses to elastic cloud platforms, with every table, query and report validated before the old system is switched off.
Modernize Your Warehouse Without Disrupting the Business
Legacy warehouses on Teradata, Netezza, Oracle Exadata, SQL Server or SAP BW still run critical reports, but they come with hardware refresh cycles, licensing renewals, fixed capacity and long queues for new data. Cloud platforms such as Snowflake, Databricks SQL, Google BigQuery, Amazon Redshift and Microsoft Fabric separate storage from compute, scale on demand and open the warehouse to data science and AI.
Hanoi Technology plans and executes warehouse modernization with a strong bias for evidence. We inventory and convert schemas, SQL, stored procedures and ETL jobs, reconcile results between old and new platforms, and repoint BI tools only when numbers match. Where legacy logic is poor, we redesign it instead of carrying technical debt into the cloud.
The Challenge
Why Warehouse Modernization Is Hard
The technology move is the easy part; these are the risks that derail programs.
Decades of Hidden Business Logic
Thousands of stored procedures, BTEQ or PL/SQL scripts, views and ETL mappings encode rules that nobody has documented. Missing one silently changes a number in a board report.
Dialect and Feature Differences
Proprietary SQL syntax, data types, indexes, workload management and procedural code do not translate one to one. Manual rewrites are slow and error-prone at scale.
Downstream Dependencies
Reports, extracts, spreadsheets and applications connect directly to the old warehouse. Each must be found, repointed and tested, or users lose access on cutover day.
Proving the New Platform Is Right
Business users will not abandon a system they trust until the new one produces the same answers. Without systematic reconciliation, parallel runs drag on for months.
Business Value
What a Modern Cloud Warehouse Gives You
Elastic Performance on Demand
Separate compute clusters for ETL, BI and data science prevent workloads from competing. Month-end peaks get extra capacity without slowing everyone else.
Lower Total Cost of Ownership
No hardware refreshes or capacity planned years ahead, and consumption-based pricing aligned to use. Idle compute suspends automatically.
Faster Time to New Data
Modern ingestion and transformation tooling lets engineers add sources and models in days. The backlog of data requests shrinks.
Ready for Semi-Structured and AI Workloads
JSON, events and documents sit beside relational data, and the same platform feeds machine learning and generative AI. Analytics and AI share one governed source.
Modernize, Don’t Just Relocate
A pure lift and shift moves cost and complexity to a new address. A good program migrates stable logic quickly with automation, redesigns the parts that perform poorly or no longer reflect the business, and retires what nobody uses. Usage analysis often shows that a large share of legacy objects can be retired rather than migrated.
Our Services
Hanoi Technology’s End-to-End Data Warehouse Modernization Services
End-to-end services for moving from a legacy warehouse to a modern cloud platform.
Modernization Assessment and Business Case
We analyze query logs, object usage, data volumes and ETL dependencies to size the migration, find unused objects and compare target platforms on cost and fit.
- Object and usage inventory
- Target platform comparison
- Migration business case and roadmap
Schema and Code Conversion
We convert DDL, views, SQL scripts and stored procedures using automated translation tools where suitable, then review and test converted code by hand where logic is complex.
- Converted DDL and data types
- Translated SQL and procedures
- Conversion exception log
ETL and Pipeline Re-Platforming
We move legacy ETL from tools such as Informatica PowerCenter, SSIS, DataStage or scripts to modern ELT with dbt, cloud-native services or Spark, keeping or improving schedules and SLAs.
- Pipeline migration plan
- Re-engineered ELT pipelines
- Orchestration and scheduling
Historical Data Migration
We extract and transfer historical data efficiently, using bulk exports, cloud transfer services or appliances for very large volumes, and load it with checksums and row counts.
- Bulk extract and load
- Incremental sync until cutover
- Load validation reports
Reconciliation and Parallel Run
We automate comparison of row counts, aggregates and sampled records between platforms for every migrated table and report, so sign-off is based on evidence.
- Automated reconciliation framework
- Discrepancy tracking
- Business sign-off packs
BI Repointing and Decommissioning
We repoint Power BI, Tableau, Looker or other BI tools and extracts, train users, and plan the controlled shutdown of the legacy warehouse.
- Repointed reports and extracts
- User communication plan
- Decommissioning checklist
Our Approach
Our Warehouse Modernization Method
Phased waves that de-risk the program and deliver value before the final cutover.
- 01
Discover and Size
We mine query history and metadata to learn what is used, by whom and how often, and map dependencies from sources through ETL to reports.
Deliverables: Usage and dependency map · Migration scope and waves
- 02
Design the Target Platform
We define the cloud architecture, security model, environment strategy and modeling standards, and decide what to migrate as-is, redesign or retire.
Deliverables: Target architecture · Migrate, redesign or retire decisions
- 03
Pilot a Representative Wave
We migrate one subject area end to end, including data, code, pipelines and reports, to validate tooling, estimates and reconciliation before scaling.
Deliverables: Pilot subject area in production · Refined estimates and playbook
- 04
Migrate in Waves with Reconciliation
Remaining subject areas move in planned waves. Each wave runs in parallel with the legacy system until automated reconciliation and business users confirm results.
Deliverables: Migrated subject areas · Reconciliation sign-offs
- 05
Cut Over and Decommission
We switch consumers to the new platform, monitor closely through the first reporting cycles and decommission legacy infrastructure and licenses.
Deliverables: Cutover runbook · Decommissioning report
Use Cases
Modernization Scenarios We Handle
Common starting points and target platforms for our clients.
Teradata to Snowflake or Databricks
Replace a capacity-constrained appliance before its hardware refresh. BTEQ scripts and stored procedures are converted and validated wave by wave.
Netezza Exit
Move off end-of-life Netezza hardware to a cloud warehouse. Distribution keys and zone maps give way to cloud-native clustering and micro-partitions.
Oracle and SQL Server Warehouses to the Cloud
Migrate departmental warehouses built on relational databases to scalable platforms. PL/SQL and T-SQL logic is refactored into maintainable ELT models.
SAP BW Reporting Modernization
Extract SAP data into a cloud lakehouse or warehouse for broader analytics. Finance keeps its reports while data science gains access to detailed history.
Consolidating Multiple Data Marts
Merge overlapping marts built by different teams into one governed warehouse. Conflicting metric definitions are resolved once and documented.
Hybrid Transition for Regulated Data
Keep selected sensitive data on-premises while analytics move to the cloud in stages. Security controls and data residency requirements are honored throughout.
Technology
Platforms and Tools
Source and target technologies our engineers work with on modernization programs.
- Snowflake
- Databricks SQL
- Google BigQuery
- Amazon Redshift
- Microsoft Fabric
- Azure Synapse
- Teradata
- IBM Netezza
- Oracle
- SQL Server
- SAP BW
- Informatica
- dbt
- SnowConvert
- Power BI
- Tableau
Platforms
Deliver Data Warehouse Modernization on Your Preferred Platform
- SnowflakeElastic cloud data platform for analytics and data sharing.Explore
- DatabricksUnified lakehouse for data engineering, analytics and AI.Explore
- Microsoft AzureModernize data, analytics and AI within your Azure tenant.Explore
- AWSBuild, migrate and run data and AI workloads on Amazon Web Services.Explore
- Google CloudBigQuery, Looker and Vertex AI for data-driven organizations.Explore
- InformaticaEnterprise data integration, quality and governance.Explore
Why Hanoi Technology
Why Organizations Choose Hanoi Technology
Senior, Cross-Functional Teams
Strategy, data, AI, cloud and engineering specialists work as one team, so nothing is lost between hand-offs.
Proven Delivery
More than 50 projects delivered for clients in North America, Europe and Asia-Pacific.
Radical Transparency
A written update every week and a demo of working software every sprint, so you always know where things stand.
Cost-Effective Global Delivery
Experienced engineers from our Hanoi delivery hub, with working-hour overlap for every major time zone.
Success Story · Fintech
All-in-one digital banking app for a traditional bank
A traditional financial institution wanted to compete with neobanks for mobile-first customers. We replaced a set of separate banking, investing and payment apps with one product that people use every day.
Read the Full StoryMore Success Stories
Real estate
90%pricing accuracyTransparent AI property valuation for a multi-city marketplace
A growing real estate marketplace wanted buyers to trust its prices. We built a valuation engine and property pages that compare the estimated value with the asking price.
Resources
Featured Resources
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Plan a Low-Risk Path Off Your Legacy Warehouse
Tell us which warehouse you run today and what is pushing you to change. We will reply within 24 hours and set up a free consultation to sketch scope, waves and a realistic timeline.
Data Warehouse Modernization FAQ
Should we lift and shift or redesign during migration?
Usually a mix. Stable, well-performing logic is migrated largely as-is, using automated conversion to save time. Processes that perform poorly, reflect outdated business rules or block new requirements are redesigned. Objects that usage analysis shows nobody queries are retired. Making these decisions per subject area, early in the program, keeps scope manageable and avoids carrying technical debt into the new platform.
How long does a warehouse modernization take?
It depends on the number of objects, the volume of procedural code, ETL complexity and how many reports depend on the warehouse. A single subject area pilot can often be completed in a couple of months, while enterprise programs are delivered in waves over a longer period. After the assessment we provide a wave plan with estimates, and we refine it based on actual pilot results.
How do you make sure the numbers match after migration?
We build an automated reconciliation framework that compares row counts, column aggregates, checksums and sampled records between the legacy and new platforms for every migrated table and key report. Discrepancies are logged, investigated and either fixed or explained. Business owners sign off each wave based on these reports, and both systems run in parallel until sign-off is complete.
Which target platform do you recommend?
We compare Snowflake, Databricks, BigQuery, Redshift and Microsoft Fabric against your workloads, existing cloud agreements, team skills, BI tools and AI ambitions. Each has strengths: Snowflake for SQL analytics and sharing, Databricks for unified data and AI, BigQuery for serverless analytics in Google Cloud, Fabric for Microsoft-centric estates. We can run a proof of concept with your queries before you decide.
What happens to our existing BI reports?
We inventory every report, dashboard and extract connected to the legacy warehouse, then repoint them to the new platform in the same wave as their underlying data. Where the data model changes, we adjust the semantic layer so report users see minimal difference. Key reports are validated by their owners before cutover, and we provide user guidance for any changes.
How do you control cloud costs after migration?
Cost control is designed in: separate compute for different workloads, auto-suspend and auto-resume, right-sized warehouses or clusters, clustering and partitioning for large tables, and incremental transformations instead of full reloads. We set up budgets, alerts and dashboards that attribute spend to teams and pipelines, and review consumption regularly in the first months after go-live.