Google Cloud
Data Warehouse Modernization with BigQuery
We re-platform legacy warehouses onto BigQuery and redesign them as governed, cost-efficient lakehouse platforms that serve BI, data science and AI from one place.
From Legacy Warehouse to Modern Data Platform
Many enterprise warehouses were designed for nightly batch loads, a fixed set of reports and a single hardware appliance. Today the same organization needs streaming data, self-service exploration, machine learning, generative AI and data sharing with partners. Scaling the old platform means larger appliances, longer batch windows and specialist administrators. BigQuery replaces that model with serverless storage and compute, open table formats through BigLake and Apache Iceberg, built-in ML and tight integration with Looker and Gemini.
Hanoi Technology modernizes warehouses on BigQuery from Oracle, SQL Server, Netezza, Teradata, Amazon Redshift, Snowflake and Hadoop environments. Modernization goes beyond moving tables: we rationalize data marts, rebuild pipelines as tested, version-controlled transformations in Dataform or dbt, introduce layered data architecture, and set up governance with Knowledge Catalog and policy tags. The result is a platform that is faster to change, cheaper to run and ready for AI workloads.
The Challenge
Signs Your Warehouse Needs Modernizing
Clients usually come to us when several of these symptoms appear at once.
Batch Windows That No Longer Fit
Nightly loads overrun into business hours as data volumes grow. Reports are late or stale, and any failure means waiting another day for correct numbers. Scaling the hardware is slow and expensive.
Transformation Logic Nobody Understands
Years of stored procedures, ETL mappings and undocumented views hold critical business rules. Changes take weeks because nobody knows what will break, and only a few people can make them safely.
Data Silos and Copies
Data science teams export extracts to their own environments, departments maintain separate marts and partners receive manual file drops. Each copy is a security risk and a reconciliation problem.
Not Ready for AI
Generative AI and ML projects need clean, documented, governed data with clear lineage. Legacy warehouses often lack metadata, unstructured data support and the elastic compute those projects require.
Business Value
Why Modernize on BigQuery
Elastic, Serverless Performance
Storage and compute scale independently, with autoscaling slots in BigQuery editions so peak workloads no longer need permanent capacity.
Open Lakehouse Architecture
BigLake tables over Cloud Storage and Apache Iceberg support let BigQuery and engines such as Spark work on the same data.
Analytics and AI in One Place
BigQuery ML, vector search and Gemini functions let analysts build models and AI features with SQL, next to governed data.
Real-Time Data
Streaming ingestion, change data capture with Datastream and continuous queries make fresh data available within seconds to minutes.
Governance Built In
Knowledge Catalog, data lineage, policy tags, row-level security and dynamic data masking support regulated use cases.
Secure Data Sharing
BigQuery sharing and data clean rooms let you share live datasets with partners or business units without copying them.
Modernization Is a Chance to Simplify
The fastest path is not always to migrate every object. Most legacy warehouses carry tables nobody queries and pipelines nobody owns. Using query logs and usage data, we identify what to migrate, what to redesign and what to retire, so you pay only for the platform you actually need.
Retiring unused content early also shortens testing, reduces the number of reports to reconcile and lowers the storage and compute footprint of the new platform from the first day.
Our Services
Hanoi Technology’s End-to-End DWH Modernization with BigQuery Services
Services spanning strategy, migration, re-architecture and operation of BigQuery platforms.
Modernization Assessment and Roadmap
We analyze query logs, object inventories, pipeline dependencies and costs, then propose a target architecture, a migration or rebuild strategy per workload and a business case.
- Workload inventory and usage analysis
- Target architecture
- Phased roadmap and business case
Warehouse Migration
We migrate schemas, data and SQL from Oracle, SQL Server, Netezza, Teradata, Redshift, Snowflake or Hive using BigQuery Migration Service translation and the BigQuery Data Transfer Service where supported.
- Translated SQL and DDL
- Historical data load
- Validation reports
Data Architecture Redesign
We introduce layered raw, curated and consumption datasets, partitioning and clustering strategies, and dimensional or data vault models suited to your reporting and AI needs.
- Layered data architecture
- Modeling standards
- Performance design
Pipeline Modernization
We rebuild ETL as ELT in Dataform or dbt with tests and CI/CD, use Datastream for change data capture and Dataflow for streaming, orchestrated with Cloud Composer.
- Version-controlled transformations
- CDC and streaming pipelines
- Orchestration and alerting
Governance and Security
We configure Knowledge Catalog, lineage, data quality scans, policy tags, row-level policies and IAM so data is discoverable, trusted and protected.
- Data catalog and glossary
- Access control model
- Data quality rules
Platform Operations and Optimization
We manage BigQuery reservations, monitor job performance and cost, tune heavy queries and evolve the platform as needs change.
- Reservation strategy
- Performance and cost dashboards
- Managed support
Our Approach
Our Modernization Approach
A wave-based program that delivers usable results early.
- 01
Assess
We collect metadata and query logs, map dependencies and decide per workload whether to migrate, redesign or retire.
Deliverables: Assessment report · Disposition per workload
- 02
Architect
We design the BigQuery landing zone, data layers, security, reservations and tooling, and agree on modeling and naming standards.
Deliverables: Reference architecture · Standards and conventions
- 03
Build the First Wave
We move or rebuild a high-value subject area end to end, including pipelines, models, reports and validation.
Deliverables: Production subject area · Lessons learned
- 04
Scale in Waves
We repeat the pattern for remaining subject areas, running legacy and new platforms in parallel until each is signed off.
Deliverables: Migrated waves · Reconciliation sign-offs
- 05
Decommission and Optimize
We retire legacy systems, tune cost and performance, and hand over runbooks or continue as a managed service.
Deliverables: Decommission plan · Optimization report
Use Cases
Modernization Use Cases
Examples of what modernized BigQuery platforms enable.
Near-Real-Time Operational Reporting
A logistics firm replicates its order and shipment databases with Datastream, giving dispatchers dashboards that update within minutes instead of the next morning.
Hadoop Cluster Retirement
An organization moves Hive tables to BigLake Iceberg tables and Spark jobs to Dataproc Serverless, removing cluster administration entirely.
Oracle Warehouse Exit
A manufacturer migrates an Oracle warehouse and its PL/SQL procedures to BigQuery, retiring licenses and redesigning heavy reports for partitioned tables.
Data Products for Business Domains
Domain teams publish certified datasets with owners, descriptions and quality checks, discovered through Knowledge Catalog.
Generative AI on Enterprise Data
Analysts use Gemini functions in BigQuery to classify support tickets and summarize feedback directly in SQL.
Partner Data Sharing
A distributor shares live sales and inventory datasets with suppliers through BigQuery sharing instead of weekly file exports.
Technology
Modern BigQuery Platform Stack
Core services and tools in our BigQuery modernization programs.
- BigQuery
- BigQuery Editions
- BigLake
- Apache Iceberg
- BigQuery Migration Service
- Datastream
- Dataflow
- Dataproc Serverless
- Dataform
- dbt
- Cloud Composer
- Knowledge Catalog
- BigQuery ML
- BigQuery sharing
- Looker
- Terraform
Platforms
Deliver DWH Modernization with BigQuery on Your Preferred Platform
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 · Logistics
Route optimization and fleet visibility for a regional logistics company
A regional delivery company handling thousands of shipments a day was planning routes and dispatching by hand. We built dynamic route optimization, a fleet dashboard and a driver app.
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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.
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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.
Resources
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Modernize Your Data Warehouse
We can assess your current warehouse and outline a practical path to BigQuery. Book a free consultation and get a response within 24 hours.
DWH Modernization with BigQuery FAQ
Should we lift and shift or redesign during the migration?
Usually a mix. Lifting and shifting reduces risk and time for stable, well-designed workloads, while redesign pays off where the legacy design causes cost, performance or maintenance problems. We use usage and complexity data from the assessment to decide per workload. A common pattern is to migrate the core quickly to exit the legacy contract, then redesign the highest-value areas in later waves.
Which legacy platforms can you migrate from?
We regularly plan migrations from Oracle, SQL Server, Teradata, Netezza, Amazon Redshift, Snowflake and Hadoop or Hive. BigQuery Migration Service supports SQL translation from many of these dialects, which accelerates the code migration. For data movement we use the BigQuery Data Transfer Service where it supports the source, and Datastream, Dataflow or bulk export through Cloud Storage otherwise.
How do you control BigQuery costs after modernization?
We choose between on-demand and edition-based capacity per workload, design partitioned and clustered tables, use incremental transformations, and set quotas and custom cost controls. Dashboards built on INFORMATION_SCHEMA and billing export show the most expensive queries and users. Monthly reviews keep costs aligned with value, and we document the guardrails so new teams follow them from the start.
How long does a modernization program take?
Assessment and architecture typically take four to six weeks. The first production wave often follows within two to three months. Full programs run from several months to more than a year depending on the number of subject areas, pipeline complexity and parallel running requirements. We plan early waves to show tangible value, such as a faster critical report, rather than waiting for a big-bang cutover.
Will our BI tools and data scientists still work?
Yes. Looker, Power BI, Tableau and most other BI tools connect directly to BigQuery, and data scientists can use notebooks, Python libraries and Spark against the same data. We repoint and test reports during each wave. Often user experience improves, because extracts and copies are replaced by direct queries on governed datasets.
How do you ensure data quality and trust after the move?
We validate migrated data against the source with automated row counts, aggregates and checksums, and keep reconciliation reports for audit. After cutover, Dataform or dbt tests and Knowledge Catalog data quality scans run with every pipeline. Owners, descriptions and lineage are recorded for key datasets so users can see where numbers come from and who to contact.