Why Companies Use Both Databricks and Snowflake

Companies Use Both Databricks and Snowflake Because:

1. Different Architectural Strengths

Big Data Processing

• Databricks: Built on Apache Spark, ideal for massive-scale parallel data pipelines, streaming, and batch processing.
• Snowflake: Limited native big data processing; focused on SQL-based analytics.

Data Warehousing & BI

• Databricks: Supports SQL, but primarily designed for engineers and data scientists.
• Snowflake: Best-in-class SQL data warehouse for business users and BI tools.

AI/ML Workloads

• Databricks: Superior support for machine learning, deep learning, and large language model development.
• Snowflake: Limited native AI/ML; relies on integrations like Snowpark.

Lakehouse Support

• Databricks: Full Lakehouse platform—combines structured/unstructured data for unified analytics.
• Snowflake: Traditionally warehouse-centric; adding Lakehouse-like features recently (e.g., Iceberg tables).

Ease of Use (Non-Technical)

• Databricks: Requires engineering expertise; notebook-driven UI.
• Snowflake: Abstracted complexity; easy for analysts and business teams to use.

2. Typical Real-World Workflow

Step 1: Raw data lands in cloud object storage (e.g., S3, ADLS). Step 2: Databricks processes, transforms, cleanses, and enriches the data (ETL/ELT pipelines). Step 3: Final structured data gets written to Snowflake for high-performance analytics, dashboards, and business reporting. Step 4: Advanced AI/ML models are trained in Databricks, often leveraging the same raw or processed data.

3. They Interoperate

• With Delta Lake UniForm or Iceberg External Tables, Snowflake can query data processed by Databricks without duplicating storage.
• Databricks can read Snowflake-managed tables or contribute to shared data lakes.
• Enterprises value this interoperability, especially for large, complex, multi-team environments.

4. Business-Driven Perspective

• Data Scientists & Engineers love Databricks for flexibility and ML.
• Analysts & Business Users love Snowflake for ease of SQL, BI tools, and governance.

By using both, companies ensure:

• ML teams aren't bottlenecked by SQL-focused warehouses.
• Business teams get easy, governed access to reliable data.
• Data is processed once, reused across platforms.

5. Businesses Cannot Keep Up

Many organizations find that their implementation of Databricks and Snowflake struggles to keep pace with the rapid innovation happening within both platforms. As Databricks strengthens its SQL and BI capabilities and Snowflake expands into AI, data science, and lakehouse architectures, the functional gap between them continues to narrow.

Yet, most customer environments remain rooted in older architectures, integration patterns, and data silos designed for a clear separation of roles. The result? Businesses risk missing out on performance gains, cost efficiencies, and unified data strategies—not because the technology isn't ready, but because their implementations aren't evolving fast enough to leverage the convergence.

6. Enterprise Structure and M&A Drive Dual Adoption

It's common for large enterprises with mostly independent business units to adopt different data platforms based on their specific needs. One division may standardize on Snowflake for its ease of use, governed analytics, and strong BI integration, while another division, focused on AI, machine learning, or large-scale data engineering, may implement Databricks.

Over time, mergers and acquisitions further drive this reality, bringing together organizations with distinct technology stacks. The result is a company that operates both Snowflake and Databricks—not by design, but by necessity. As these platforms evolve and their capabilities increasingly overlap, organizations face the challenge—and opportunity—of finding ways to integrate and optimize their multi-platform data strategy.

If Architected Properly Databricks and Snowflake Are Highly Interoperable:

To view a comprehensive discussion of interoperability read this blog. Full interoperability” means using data in Snowflake or Databricks interchangeably, but today it’s closer than ever—yet not seamless:

1. Shared Open Formats

Using Delta or Iceberg on a common object store (e.g., S3) lets both platforms query the same tables, avoiding duplicate storage and ensuring consistency. Snowflake’s Iceberg feature and Databricks’ Delta Lake exemplify this approach, granting flexibility and cost savings.

2. Remaining Hurdles

• Feature mismatches & write conflicts: Not every table feature or transactional pattern is identically supported, so two-way writes can require careful coordination.
• Operational complexity: Teams must manage the external storage layer, handle schema evolution, and monitor performance.

Despite these gaps, the benefits—avoiding vendor lock-in, leveraging each engine’s strengths, and reducing ETL and security risk—far outweigh the extra effort. Ongoing standards work (e.g., Iceberg REST API) promises to make near-plug-and-play interoperability the industry norm soon.

Bottom Line

Enterprises often pair Databricks and Snowflake to leverage each platform’s distinct strengths. Databricks excels at large-scale data processing, streaming, and advanced AI/ML workloads through its Apache Spark–based Lakehouse, while Snowflake provides a best-in-class, SQL-centric data warehouse that’s easy for analysts and BI tools to adopt. A common pattern sees raw data ingested into cloud object storage, transformed and enriched in Databricks, and then loaded into Snowflake for high-performance analytics and governed reporting. This division of labor lets engineering and data science teams use Databricks’ flexible pipelines and ML capabilities, while business users enjoy Snowflake’s simplicity, strong governance, and built-in performance.

Over time, open formats like Delta Lake and Apache Iceberg have enabled much closer interoperability: both platforms can read and write the same tables on shared storage, reducing duplication and ensuring consistency. Yet full “plug-and-play” interoperability still requires careful management of feature gaps (e.g., transactional semantics, schema evolution) and operational complexity in coordinating writes. Nonetheless, avoiding vendor lock-in, cutting ETL costs, and playing to each engine’s strengths make the extra effort worthwhile. As both vendors continue to align on standards (such as the Iceberg REST API), this multi-platform strategy is set to become even more seamless—and increasingly common across diverse business units and M&A–driven tech stacks.

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