Move the warehouse. Keep every number.
Sphere modernizes legacy warehouses and builds data lakes on Snowflake, Databricks, Azure, Google Cloud or AWS, migrating in waves and reconciling every table before anyone switches over. No big-bang cutover.
Book a migration scoping callfact_ordersdim_customerfact_invoicesdim_product
Book a migration scoping call
Tell us what you run today and where you want to be. We'll give you an honest read on the platform, the plan and a fixed price.
Your call is with one of oursenior data architectsTechnology partners
Snowflake
Databricks
AWS
Microsoft Azure
Google Cloud
Copying the old warehouse to the cloud copies its problems too.
A lift-and-shift moves the bill. A modernization moves the data onto a design that's faster, cheaper to run and ready for what the business needs next.
Snowflake, Databricks or your cloud's own?
We implement all of them, so the recommendation follows your data, not a partnership. Here's where each tends to fit.
Simple to run, strong for SQL analytics and sharing.
A good fit when most of the work is analytics and BI, the team is SQL-first and you want compute and storage that scale independently with little to manage.
- BI and reporting at scale
- Data sharing with partners
- Teams that want low operational overhead
One lakehouse for data engineering, streaming and machine learning.
A good fit when you have heavy transformation, streaming or machine learning workloads and want governance across data and AI assets in one place.
- Large or unstructured data
- Streaming and IoT
- ML and AI in production
Stay close to the cloud you already run.
Azure Synapse, BigQuery or Amazon Redshift can be the simplest path when you're committed to one cloud and want native security, billing and integrations.
- Single-cloud strategies
- Tight integration with existing cloud services
- Consolidated billing and security
Migrate in waves. Switch when it matches.
No big-bang weekend. Each wave is reconciled and run in parallel before anyone switches over.
Book a migration scoping call- Step 1
Assess the estate
We inventory tables, pipelines, reports and dependencies, and measure what the current warehouse costs and where it strains.
You getA platform recommendation and a wave plan. - Step 2
Design the target
We design the target models, security and cost controls on the platform you choose, and rebuild pipelines with tests.
You getA target architecture ready for data. - Step 3
Migrate & reconcile
Data, pipelines and reports move wave by wave. Row counts, checksums and key metrics are compared, and both systems run in parallel.
You getWaves that match the source, table by table. - Step 4
Cut over & optimize
Once a wave matches, users switch. Then we tune performance and cost and decommission the old system.
You getA modern warehouse, a smaller bill and the old one retired.
Migrations that landed.
Warehouse, lake and migration work from the published case studies.
infrastructure cost reduction
A wealth management firm left Teradata for Snowflake.
50TB and 2,000 SQL objects were translated and the ETL was rebuilt in dbt, cutting infrastructure cost by 60%.
See Snowflake consultingfaster analytical response times
A national retailer moved from Teradata to a governed lakehouse.
The migration landed on Databricks on Azure with a Unity Catalog-governed medallion architecture.
See Databricks consultingannualized savings
A high-growth e-commerce company stopped overpaying for compute.
A Snowflake FinOps review eliminated warehouse over-provisioning, data duplication and missing caching.
See Snowflake consultingterabyte-scale Oracle databases migrated to Azure
A multinational moved its Oracle estate to Azure without losing reporting.
On-prem Oracle was split into Azure Synapse for analytics and Azure SQL Managed Instance for transactions, kept in sync with Azure Data Factory, with Tableau and Power BI on top.
Read the migration storymarketing ROI
Finance, marketing and sales finally looked at the same numbers.
After an assessment and roadmap for the CFO and CIO, a Google Cloud data lake combined Salesforce CRM, SAP finance and web traffic data behind campaign-effectiveness dashboards.
Read the marketing ROI storyWant results like these?Tell us about your project. A senior architect replies within one business day.
Book a migration scoping callFrom the people who signed off on it.
Leaders who trusted Sphere with the data their business runs on.
“Sphere approached AI transformation the right way — starting with workflows, data governance, and operational trust instead of simply deploying another AI tool. Their team built a secure and scalable AI infrastructure that allows us to capture institutional knowledge, protect sensitive data within our environment, and continuously expand our AI capabilities with confidence.”
“Sphere consistently demonstrated a high level of accountability, technical expertise, and executive partnership. Their team remained deeply invested in our business outcomes and successfully supported us through complex technology and operational challenges.”
“Sphere was a valuable partner in helping us advance our AI-native product lifecycle initiatives. Their combination of product thinking, engineering expertise, and execution discipline enabled the delivery of impactful solutions that improved our platform and accelerated innovation.”
Architects who build production data systems.
Your first call is with the people who design Sphere's data and AI platforms, and they stay with the project after you sign.
Built products and led high-scale engineering teams at HP Software, Gett and Google before leading Sphere's technology practices.
Architects enterprise AI platforms that turn complex operations into secure, production-ready systems built to hold up at real-world scale.
Led high-scale operational systems and modernization at Gett and bp pulse. Now builds automation-driven platforms and intelligent workflows.
More than a decade designing and scaling software from first MVP to enterprise-level systems across FinTech, transportation and real-time streaming.
A faster warehouse and a predictable bill.
Warehouses and clusters sized to real workloads, not guesses.
Compute that sleeps when idle and reuses results.
Spend tagged by team and workload, with monitors and alerts.
Snowflake
Databricks
AWS
Microsoft Azure
Google Cloud
Salesforce
SAP
NetSuite
Dynamics 365
HubSpotCloud data platforms and the business systems we connect them to.
21 years of engineering. Our top 20 clients stay 7+ years.
Independent recognition for the AI work Sphere delivers, from Clutch reviewers to global AI award juries.
Top Artificial Intelligence Company · US 2025
Top AI Code Generation Company · US 2025
Top Chatbot Company · US 2025
Top Recommendation Systems Company · US 2025
Top AI Text Generation Company · Florida 2025
Top App Development Company · Manufacturing 2025
Global Generative AI Award · 2026 Winner
Best in AI for Documentation Validation · 2026
Best in AI-Powered Invoice Auditing · 2026
Global Recognition Awards · 2026
Global Tech Award Winner · 2026
Most Innovative Technology Consulting Firm · 2026
Warehouses and migrations, answered directly.
Don't see yours? Ask it on the call.
Book a migration scoping callShould we choose Snowflake, Databricks or our cloud's native warehouse?
It depends on your workloads, team and existing cloud. Snowflake suits analytics-first teams that want simplicity, Databricks suits heavy data engineering and machine learning, and native options like Azure Synapse, BigQuery or Redshift make sense when you're committed to one cloud. We'll recommend one based on your data, not a partnership.
How do you make sure nothing is lost in the migration?
We migrate in waves and reconcile every table, comparing row counts, checksums and key business metrics between the old and new systems, and run both in parallel before cutover.
Do we need a data lake or a data warehouse?
Warehouses are best for structured, governed analytics; lakes and lakehouses handle large, varied or unstructured data and machine learning. Many teams need both, and a lakehouse can give you one platform for each.
Can reports keep running during the migration?
Yes. Reports move with each wave and the old system stays live until the new one is reconciled, so the business doesn't lose visibility.
How do you keep cloud costs under control?
We size compute to the workload and set up auto-suspend, caching, resource monitors and cost attribution, so spend is predictable after go-live.
What does it cost and how long does it take?
Both depend on data volume, the number of objects and pipelines, and the reports that depend on them. We fix the timeline and quote a fixed price at the scoping call.
Modernize the warehouse without losing a report.
Book a scoping call with a Sphere data architect. You'll leave with:
- An honest platform recommendation
- A wave plan for your migration
- A fixed price and a timeline
Book a migration scoping call
Tell us what you run today and where you want to go. We'll reply within one business day.
Your call is with one of oursenior data architects