Sphere Partners

Snowflake Consulting & Data Cloud Services

Sphere designs and delivers Snowflake Data Cloud solutions — from initial warehouse migration to Snowpark ML workloads, Cortex AI, and governed data sharing at enterprise scale. Snowflake Partner with FinOps and AI/ML experience.

Snowflake

Organizations around the world trust us

ideel
JFrog
Clearcover
91 Seconds
PHC
NextCapital
DigitalOcean
Enova
bp
Groupon
CreditNinja
Navy Pier
DoorDash
Gett
Experify
ideel
JFrog
Clearcover
91 Seconds
PHC
NextCapital
DigitalOcean
Enova
bp
Groupon
CreditNinja
Navy Pier
DoorDash
Gett
Experify

What's Included in Sphere's Snowflake Services

Sphere covers the Snowflake Data Cloud end to end: account architecture, Snowpark, Cortex AI, data sharing, migration from legacy warehouses, and credit-aware FinOps.

Snowflake Data Cloud Architecture

Design enterprise Snowflake environments: account topology, database and schema hierarchy, warehouse sizing strategy, dynamic tables, streams and tasks for CDC pipelines, and a role-based security model.

Snowpark & Python Development

Build data engineering pipelines, ML preprocessing workflows, and custom functions in Python, Java, or Scala using the Snowpark API — processing data at scale without moving it outside Snowflake.

Cortex AI & LLM Functions

Implement Snowflake Cortex AI features including COMPLETE, SUMMARIZE, SENTIMENT, and TRANSLATE functions — embedding LLM capabilities directly into SQL workflows and Streamlit applications.

Data Sharing & Marketplace

Configure Snowflake Secure Data Sharing to exchange live data sets with partners and customers without data movement — or publish data products to the Snowflake Marketplace for commercialization.

Migration to Snowflake

Migrate from Teradata, Redshift, Synapse, BigQuery, or on-premises databases to Snowflake — including SQL translation, data type mapping, stored procedure conversion, and pipeline rewrite in dbt or Snowpark.

FinOps & Credit Optimization

Reduce Snowflake credit consumption through warehouse right-sizing, query optimization, result cache utilization, search optimization tuning, and resource monitor alerting — typically reducing costs 20-40%.

Why Sphere for Snowflake.

Snowflake is the cloud data platform of choice for analytics-first organizations — but achieving optimal performance, cost efficiency, and AI capability requires experienced architects who understand Snowflake's unique multi-cluster warehouse model, micro-partition optimization, and Snowpark ecosystem.

Why enterprises choose Sphere to implement and optimize Snowflake:

Architecture-First Delivery

We design Snowflake environments for scale from the start — warehouse sizing, clustering strategies, dynamic tables, data vault modeling, and role hierarchies that prevent costly rework as your data volumes grow.

Snowflake AI & ML Expertise

We develop Snowpark applications, Snowpark ML pipelines, and Cortex AI LLM functions — enabling your data teams to run Python and ML workloads directly in Snowflake without moving data.

FinOps for Snowflake

Snowflake credit consumption can grow quickly without proper governance. Our FinOps practice implements warehouse auto-suspend, query result caching, resource monitors, and cost attribution tagging to keep Snowflake costs predictable.

Track record across migration, Cortex AI, and FinOps engagements.

Teradata Migration Timeline

Wealth management firm — Teradata → Snowflake migration. 50TB, 2,000 SQL objects translated, ETL rebuilt in dbt, 60% infrastructure cost reduction.

Documents Classified

Healthcare insurer — Snowflake Cortex AI pipeline classifying, summarizing, and routing member documents directly in SQL. 85% processing time reduction.

Credit Cost Reduction

High-growth e-commerce — Snowflake FinOps review eliminating warehouse over-provisioning, data duplication, and missing caching. $420K annualized savings.

The Snowflake Stack Behind It

Sphere implements and operates the Snowflake capabilities that modern data platforms actually run on.

Service AreaKey Technologies

Generative AI

Snowflake Cortex (COMPLETE, SUMMARIZE, SENTIMENT, TRANSLATE), Cortex Search, Cortex Analyst, Document AI

Machine Learning

Snowpark ML, Feature Store, Model Registry, Container Runtime

Data Engineering

Snowpark for Python/Scala/Java, Streams, Tasks, Dynamic Tables

Data & Analytics

Iceberg Tables, Hybrid Tables, External Tables, Time Travel, Cloning

Apps & Sharing

Snowflake Native Apps, Marketplace, Secure Data Sharing, Streamlit in Snowflake

Governance & Security

Horizon Catalog, Data Lineage, Tag-based masking, Row Access Policies

Infrastructure

Terraform Snowflake provider, dbt, Schemachange, Fivetran, Snowpipe

Let's scope your Snowflake engagement

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Running on Snowflake?
Here's Where Sphere Fits.

Legacy Warehouse → Snowflake

Legacy Warehouse → Snowflake

Teradata, Redshift, Synapse, BigQuery, and on-premises database migrations to Snowflake — SQL translation, data type mapping, stored procedure conversion, and pipeline rewrite in dbt or Snowpark.

Cortex AI Document Processing

Cortex AI Document Processing

Snowflake Cortex AI pipelines classifying, summarizing, and routing documents using LLM functions directly within SQL — for healthcare, insurance, and high-volume document workflows.

Snowflake FinOps & Credit Optimization

Snowflake FinOps & Credit Optimization

Warehouse right-sizing, query optimization, result cache utilization, search optimization tuning, and resource monitor alerting — typically delivering 20–40% credit consumption reduction.

Snowpark & Python ML

Snowpark & Python ML

Snowpark applications and Snowpark ML pipelines — Python, Java, and Scala data engineering, feature pipelines, and model training that runs directly in Snowflake without moving data.

Data Sharing & Marketplace

Data Sharing & Marketplace

Secure Data Sharing for live data exchange with partners and customers without data movement — and Snowflake Marketplace publishing for monetized data products.

Governance & Compliance

Governance & Compliance

Horizon Catalog, Data Lineage, Tag-based masking, and Row Access Policies — keeping Snowflake environments audit-ready for HIPAA, PCI DSS, SOC 2, and regulated finance/healthcare workloads.

Hear from

our clients
Lee Ebreo

Lee Ebreo

VP of Engineering at Credit Ninja

These things would not have been achievable if we did not build our own in-house system and if we did not partner with Sphere to help us achieve our goals.

Selah Ben-Haim

Selah Ben-Haim

VP of Engineering at Prominence Advisors

Our experience with Sphere and their team has been and continues to be fantastic. We keep throwing new projects at them, and they keep knocking them out of the park (including the rescue of a project that was previously bungled by another vendor).

Ben Crawford

Ben Crawford

Senior Product Manager at Enova Financial

I would expect to be delighted. It's been a really positive experience, working with Sphere, and I would expect you to have the same.

Mark Friedgan

Mark Friedgan

CEO at CreditNinja

Sphere consistently prioritizes the needs of their clients, demonstrating both agility and teamwork. As an offshore team, they have been an integral part of our organization and we plan to continue growing with them.

René Pfitzner

René Pfitzner

Co-Founder at Experify

Sphere provided excellent full-stack development manpower to augment our team and help push our product forward. They are easy to work with, tech-savvy and proactive.

Bruce Burdick

Bruce Burdick

Chief Information Officer at Integra Credit

We've been working with Sphere and its excellent consultants since our founding. I've found that they are true partners in the success of our business.

Jemal Swoboda

Jemal Swoboda

CEO at Dabble

The resources and developers that Sphere Software provides are skilled and have the required technical expertise, but more importantly, they have helped us build a culture of excellence within our team.

Arthur Tretyak

Arthur Tretyak

Founder and CEO at IntegraCredit

With Sphere, we were able to migrate in half the time it would take to train an additional FTE… and for a fraction of the cost. Our experience with Sphere has been exceptional.

Lee Ebreo

Lee Ebreo

VP of Engineering at Credit Ninja

These things would not have been achievable if we did not build our own in-house system and if we did not partner with Sphere to help us achieve our goals.

Selah Ben-Haim

Selah Ben-Haim

VP of Engineering at Prominence Advisors

Our experience with Sphere and their team has been and continues to be fantastic. We keep throwing new projects at them, and they keep knocking them out of the park (including the rescue of a project that was previously bungled by another vendor).

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Sphere in Numbers

We understand that actions speak louder than words and numbers but here are some key facts about us.

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Years of Excellence

0+

Projects Delivered

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Countries

Globally diverse, community-focused

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Clients

top 20 average 8+ years

Frequently Asked Questions

Snowflake separates compute and storage, allowing you to scale them independently. Its multi-cluster shared data architecture means different workloads can run simultaneously without resource contention.
Yes. We implement Snowpark ML for in-warehouse Python model training and inference, build feature engineering pipelines using Snowflake Streams and Dynamic Tables, and integrate Snowflake with MLflow or MLOps platforms where needed.
Snowflake Cortex AI provides access to large language models directly within Snowflake SQL and Snowpark — including functions for text summarization, sentiment analysis, translation, classification, and completion.
We implement a FinOps framework covering warehouse auto-suspend and auto-resume configuration, resource monitors with credit limit alerts, clustering key optimization, and search optimization. Typical engagements achieve 20-40% credit reduction.
Yes. We configure Snowflake native connectors for Power BI, Tableau, Looker, Qlik, and MicroStrategy — including performance-optimized partner connect setups, Snowflake BI Engine acceleration, and row-level security policies.

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