Databricks vs. Snowflake: A Unified Orchestration Perspective

TL;DR — Databricks is a unified data, analytics, and AI platform built on a lakehouse architecture, excelling at large-scale data engineering and machine learning with open formats. Snowflake is a cloud data warehouse optimized for SQL-based business intelligence and analytics, prioritizing ease of use and managed services.

Choosing between Databricks and Snowflake is a common challenge for data-driven organizations. Both platforms are cornerstones of the modern data stack, each offering powerful capabilities for data processing, analytics, and AI. However, their underlying architectures and philosophical approaches differ significantly, leading to distinct strengths and ideal use cases.

This guide provides an in-depth comparison to help you navigate their differences, understand where each excels, and explore how a unified orchestration platform like Kestra can seamlessly integrate both for a hybrid data strategy. We’ll delve into their core functionalities, discuss their respective advantages, and show how to build robust workflows that leverage the best of both worlds.

How Databricks and Snowflake Work

Understanding the architectural foundations of Databricks and Snowflake is the first step to choosing the right tool. They represent two different paradigms for building a big data platform: the lakehouse and the cloud data warehouse.

Databricks: The Unified Lakehouse for Data, Analytics, and AI

Databricks champions the “lakehouse” architecture, a hybrid model that combines the flexibility and low cost of a data lake with the performance and reliability of a data warehouse. At its core is Delta Lake, an open-source storage layer that brings ACID transactions, schema enforcement, and time travel capabilities to data stored in cloud object storage (like S3, GCS, or Azure Blob Storage).

This architecture allows Databricks to handle structured, semi-structured, and unstructured data in a single system. It’s built around Apache Spark, providing a powerful, distributed engine for large-scale data processing and machine learning. This makes Databricks a strong choice for data engineering and data science teams who need granular control over their environment and want to work with open data formats like Parquet.

Snowflake: The Cloud Data Warehouse for Business Intelligence

Snowflake pioneered the modern cloud data warehouse. Its patented multi-cluster, shared data architecture completely separates compute from storage. This allows independent scaling of resources; you can run multiple compute clusters (called “virtual warehouses”) against the same data without performance contention.

Snowflake is primarily designed for structured and semi-structured data (like JSON, Avro, and Parquet). It abstracts away much of the underlying complexity, providing a highly managed, SQL-native experience that is particularly well-suited for business intelligence, analytics, and data sharing. Its focus on simplicity and performance for analytical queries makes it a favorite among data analysts and business users.

Why Orchestration is Key for Both Platforms

Neither Databricks nor Snowflake operates in a vacuum. A typical data workflow involves multiple steps across various systems: ingesting raw data from APIs or databases, cleaning and transforming it, running ML models, loading it into analytical tables, and sending notifications.

This is where data orchestration becomes critical. An orchestration tool is needed to:

• Coordinate Dependencies: Ensure that a data loading job in Snowflake only runs after a complex transformation in a Databricks notebook is complete.
• Automate Complex Processes: Schedule and manage multi-step pipelines that involve data quality checks, model training, and integration with external systems.
• Handle Failures and Retries: Automatically retry failed tasks, route errors to an alerting system, and provide a centralized view for debugging.
• Provide Visibility and Governance: Offer a single control plane to monitor, manage, and audit all data workflows, regardless of where they run.

Without a dedicated orchestration layer, teams are left managing a brittle collection of cron jobs, custom scripts, and platform-specific schedulers like Snowflake Tasks or Databricks Workflows, which can quickly become unmanageable at scale.

Orchestrating Databricks and Snowflake with Kestra: A Hybrid ETL Scenario

Many organizations find that the best approach is not to choose one platform over the other, but to use both for what they do best. A common pattern is to use Databricks for heavy data engineering and ML model preparation, then load the curated, high-value data into Snowflake for enterprise-wide BI and analytics.

Kestra’s declarative nature makes it ideal for managing these hybrid workflows. A single YAML file can define a pipeline that coordinates tasks across both platforms, complete with scheduling, error handling, and data passing.

Here is a runnable example of a daily ETL process that extracts user activity data with a Databricks query, runs dbt transformations, and then loads the aggregated results into Snowflake.

id: hybrid-databricks-snowflake-etl
namespace: company.team.analytics

description: >
  A daily workflow that extracts user data from Databricks,
  runs dbt transformations, and loads the aggregated results into Snowflake.

tasks:
  - id: extract-from-databricks
    type: io.kestra.plugin.databricks.sql.Query
    description: Fetch raw user activity data from the last day.
    host: "{{ secret('DATABRICKS_HOST') }}"
    token: "{{ secret('DATABRICKS_TOKEN') }}"
    sql: |
      SELECT user_id, event_type, event_timestamp, payload
      FROM raw_events.user_activity
      WHERE event_date = CURRENT_DATE() - INTERVAL 1 DAY;
    store: true

  - id: run-dbt-transformations
    type: io.kestra.plugin.dbt.cli.commands.DbtCli
    description: Run dbt models to aggregate and clean the data.
    containerImage: ghcr.io/kestra-io/dbt-postgres:latest
    commands:
      - dbt run --models tag:daily_aggregates
    inputFiles:
      data.csv: "{{ outputs['extract-from-databricks'].uri }}"

  - id: load-to-snowflake
    type: io.kestra.plugin.jdbc.snowflake.Query
    description: Load the transformed data into the Snowflake analytics table.
    url: "{{ secret('SNOWFLAKE_URL') }}"
    user: "{{ secret('SNOWFLAKE_USER') }}"
    password: "{{ secret('SNOWFLAKE_PASSWORD') }}"
    sql: |
      COPY INTO analytics.daily_user_summary
      FROM @kestra_stage/{{ outputs['run-dbt-transformations'].outputFiles['target/results.csv'] }}
      FILE_FORMAT = (TYPE = CSV);

triggers:
  - id: daily-schedule
    type: io.kestra.plugin.core.trigger.Schedule
    cron: "0 5 * * *" # Runs daily at 5 AM UTC

errors:
  - id: send-failure-alert
    type: io.kestra.plugin.notifications.slack.SlackExecution
    message: "Failed ETL from Databricks to Snowflake for execution {{ execution.id }}"
    channel: "#data-alerts"
    url: "{{ secret('SLACK_WEBHOOK_URL') }}"

A few things are worth noticing in this workflow:

• Declarative & Unified: The entire cross-platform pipeline is defined in one simple YAML file. There’s no need for Python glue code or separate schedulers.
• Secret Management: Credentials for both Databricks and Snowflake are securely managed using {{ secret(...) }} expressions, not hardcoded in the workflow.
• Data Passing: Kestra’s internal storage transparently handles passing the data extracted from Databricks to the dbt task for transformation, and then to the Snowflake loading task.
• Built-in Error Handling: The errors block provides a simple, reliable way to send alerts to Slack if any task in the workflow fails, ensuring the data team is notified immediately.

Making the Right Choice: Databricks vs. Snowflake

The decision ultimately comes down to your team’s primary use cases, existing skill sets, and strategic priorities.

Databricks’ Strengths:

• Openness: Built on open-source technologies like Apache Spark and Delta Lake, avoiding vendor lock-in.
• AI & Machine Learning: Superior capabilities for the end-to-end ML lifecycle, from data preparation and model training to deployment.
• Flexibility: Handles all data types (structured, semi-structured, unstructured) and supports multiple programming languages (Python, Scala, R, SQL).
• Advanced Data Engineering: Provides granular control for complex ETL/ELT pipelines and streaming workloads.

Snowflake’s Strengths:

• Simplicity & Ease of Use: Fully managed platform with a familiar SQL interface, making it highly accessible to data analysts.
• Performance for BI: Optimized for fast, concurrent analytical queries, ideal for business intelligence and reporting.
• Data Sharing: Secure and seamless data sharing capabilities allow organizations to easily share live data with partners and customers.
• Ecosystem: A mature ecosystem of BI and ETL tools with native connectors.

For a detailed breakdown, see our guides on Databricks alternatives and Snowflake Tasks alternatives.

Where Databricks and Snowflake Excel

Your choice will be guided by the problems you need to solve.

Databricks Use Cases:

• AI and Machine Learning: Building, training, and deploying ML models at scale.
• Large-Scale ETL: Processing and transforming petabytes of raw data in various formats.
• Real-time Analytics: Implementing streaming applications with Spark Structured Streaming.
• Data Science Collaboration: Providing interactive notebooks for exploratory data analysis and collaboration.

Snowflake Use Cases:

• Enterprise Data Warehousing: Serving as the central repository for curated business data.
• Business Intelligence and Reporting: Powering dashboards and analytical tools like Tableau or Power BI.
• Secure Data Sharing: Creating a data marketplace or sharing governed datasets with external partners.
• Ad-hoc Analytics: Enabling business users to explore data and answer questions using SQL.

Hybrid Approaches:

The most sophisticated data teams often don’t see this as an either/or decision. They implement a hybrid strategy, orchestrating workflows that span both platforms. For example, you can leverage Databricks for its powerful data processing and ML capabilities to refine raw data, and then use an orchestration tool like Kestra to automatically load the resulting datasets into Snowflake, where they can be easily accessed by business analysts. This approach is superior to relying solely on native schedulers like Databricks Workflows or replacing tools like Snowpipe.

Related concepts

• ETL vs. ELT: Key Differences
• What is a Data Pipeline?
• Data Quality: A Practical Guide
• Understanding Data Observability
• dbt Integrations for Orchestration
• Alternatives to Microsoft Fabric

Ready to unify your data and AI workflows across platforms like Databricks and Snowflake? Explore how Kestra can provide a single, declarative control plane for your data and AI automation needs.