Do Snowflake Implementation Partners Also Work with Databricks?

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In today’s fast-evolving data landscape, enterprises increasingly embrace both Snowflake and Databricks to power their analytics and machine learning workloads. This combination reflects a growing trend toward lakehouse delivery models that blend the best of data warehouse and data lake paradigms. Naturally, organizations contemplating a multi platform data team wonder if Snowflake implementation partners also have expertise in Databricks — a crucial factor when selecting an implementation partner in 2026.

The Growing Demand for Multi-Platform Expertise

Snowflake has cemented itself as a leader in cloud data warehousing, offering elasticity, simplicity, and integration with tools like Snowpark ML. Meanwhile, Databricks champions the open lakehouse approach, enabling unified data engineering, data science, and machine learning on massive scale. Given that both platforms can coexist — with Snowflake handling governed, performant analytics and Databricks supporting advanced data science workflows — enterprises want implementation partners who can bridge these environments flawlessly.

In 2026, partner firms must deliver end-to-end migration strategies and architectural designs spanning both Snowflake and Databricks environments. This includes robust governance, security configuration, and tooling automation that work seamlessly across the lakehouse landscape.

Snowflake Partner Tiers and Recognition

Ask yourself this: snowflake’s partner ecosystem is structured into tiers based on expertise, certifications, and customer success. These tiers include:

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Registered Partners – Early stage integration and consulting capabilities Silver Partners – Demonstrated project delivery with Snowflake implementations Gold Partners – Proven success in complex enterprise projects with architects and certified engineers Platinum Partners – Elite tier with extensive experience, strategic cloud partnerships, and advanced service offerings

Especially in 2026, clients should prioritize Gold and Platinum Snowflake partners who can prove their ability to integrate Snowflake with other cloud-native tools like Databricks. These partners typically have deep knowledge of Snowpark ML, Snowflake’s ML development framework, and can align it with Databricks pipelines for machine learning lifecycle management.

Do Snowflake Implementation Partners Also Work With Databricks?

The short answer is: many leading Snowflake implementation partners also have strong Databricks capabilities. Because modern data delivery models favor flexible platforms over monolithic solutions, partner firms focus on hybrid expertise. Let’s look at a few examples:

STX Next: Originally known for Python engineering, STX Next has expanded into cloud data engineering and analytics. Their consultants deliver projects involving Snowflake data warehouses plus Databricks lakehouse architectures, integrating them into unified pipelines with governance in mind. phData: A recognized leader in data modernization, phData is a Platinum Snowflake partner with extensive Databricks experience. They build multi-platform solutions blending Snowflake’s governed data sharing with Databricks’ open data lake and ML tooling, empowering clients with end-to-end managed migration and analytics delivery. NTT DATA: With a vast global footprint, NTT DATA supports enterprises in implementing Snowflake with complementary Databricks environments. Their delivery model emphasizes compliance, security, and governance, weaving enterprise-grade policies across both platforms while enabling business insights at scale.

These firms demonstrate that Snowflake implementation partners increasingly become multi-platform enablers who understand the nuances of integrating warehouse and lakehouse paradigms.

Partner Selection Criteria for 2026

When assessing partners for your Snowflake and Databricks journey in 2026, consider these key criteria:

Demo of Multi-Platform Delivery: Request real-world case studies or references of projects using both Snowflake and Databricks in a coordinated environment. Certifications & Specializations: Confirm the partner’s tier in Snowflake’s ecosystem as well as partner status or certifications within Databricks. Governance and Security Expertise: The partner must demonstrate capability to implement comprehensive governance frameworks spanning cloud identity, data access policies, encryption, and audit trails across both platforms. End-to-End Migration Capability: From data ingestion, schema design, pipeline orchestration, transformation, integration of Snowpark ML models, to deployment and monitoring, the implementation partner should offer a full delivery methodology. Change Management & Enablement: Multi-platform environments require robust training and enablement programs to create a multi platform data team fluent in both Snowflake analytics and Databricks lakehouse tooling.

End-to-End Migration Delivery Models

Migrating to a hybrid Snowflake and Databricks ecosystem is complex but rewarding. Proven partners follow iterative, phased delivery:

Assessment & Planning: Evaluate current data infrastructure, identify workloads for Snowflake versus Databricks, define target architecture and governance standards. Pilot & Proof of Concept: Demonstrate low-risk migration of key data domains, leveraging Snowpark ML and Databricks MLflow integration to build initial models. Full Migration & Expansion: Migrate data pipelines, legacy datasets, and analytical workloads with automation. Setup continuous integration/deployment capabilities across both platforms. Governance & Security Configuration: Implement unified policies using cloud-native security controls (e.g., AWS IAM, Azure AD), Snowflake masking policies, Databricks cluster security, and metadata catalog governance. Enablement & Support: Provide user training, documentation, and ongoing operational support to foster self-sufficient multi-platform teams.

Governance and Security Configuration in Multi-Platform Environments

Balancing governance and security is paramount. Both Snowflake and Databricks offer robust but different native controls:

Capability Snowflake Databricks Access Control Role-based access control (RBAC), masking policies, secure views AWS IAM or Azure AD integration, cluster-level ACLs, workspace permissions Data Encryption Always-on encryption at rest and in transit, customer-managed keys (CMK) Encryption of data at rest/on-disk and in transit, key management services integration Data Lineage and Auditing Native query history, object change tracking, Snowflake Access History Audit logs, jobs and notebooks execution history, integration with third-party lineage tools

Top implementation partners architect governance as a unified layer on top of both platforms, leveraging data catalogs (e.g., Alation, Collibra) and policy enforcement automation to ensure regulatory compliance (GDPR, HIPAA, etc.) and data quality.

Conclusion

Yes, Snowflake implementation partners today often work hand-in-hand with Databricks, enabling enterprises to adopt hybrid lakehouse delivery models powered by the strengths of both platforms. When selecting a partner in 2026, prioritize firms — such as STX Next, phData, or NTT DATA — that demonstrate multi-platform expertise, solid Snowflake tier recognition, proven end-to-end migration delivery models, and governance frameworks integrating both Snowflake and Databricks.

Equipping your multi platform data team with a partner experienced in both Snowflake and Databricks unlocks greater agility, scalability, and insight from your data investments. With the right partner, you can confidently navigate lakehouse architectures and realize your data-driven business goals.

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