Making the Move to Databricks? Here’s How to Do It Right
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Modern businesses are powered by data. Whether it’s understanding customer behavior, improving operations, or building intelligent products, data is at the center of every decision. But the real challenge isn’t just collecting data, it’s turning that data into action quickly, efficiently, and at scale.
That’s where platforms like Databricks come in.
As data continues to grow in volume and complexity, most companies are realizing that traditional tools and fragmented systems just aren’t enough. They need platforms that unify everything, data storage, analytics, machine learning, and AI, in one place. Currently, Databricks is becoming the go-to platform for organizations that want to stay ahead.
As an official Databricks consulting partner, Arbisoft helps businesses navigate this shift with confidence.
This newsletter looks at why more businesses are choosing Databricks as a long-term data solution and how they can make the switch from Snowflake smoothly and without any downtime. It brings together both the strategic and practical sides of adopting a modern, unified data platform.
Why Databricks Is the Smart Business Move in 2025
The concept of a Data Lakehouse, introduced by Databricks, blends the structured storage of data warehouses with the flexibility and scale of data lakes. But Databricks goes beyond just storage, it supports data engineering, business intelligence (BI), machine learning (ML), and generative AI all within a single platform.
In 2025, where agility, scale, and intelligent data use are key, Databricks is not just another platform, it’s a business strategy. Let’s discuss some of the advantages:
1. Unified Platform, Unified Results
One of the most significant advantages of Databricks is that it removes the need for multiple data platforms. Instead of juggling several tools for data pipelines, ML models, BI dashboards, and data governance, companies can do it all on Databricks. This not only streamlines workflows but also reduces the need for large, specialized teams.
Imagine a retail brand managing data from in-store systems, online sales, marketing platforms, and internal departments like HR and finance. Usually, this data lives in silos, accessed by different tools and teams. With Databricks, all of this can be unified, reducing complexity and facilitating better collaboration.
2. Open Ecosystem, Flexible Integrations
Databricks is built on open standards. It supports widely used data formats like Parquet and Delta, and integrates easily with popular tools in the modern data stack. This openness ensures that companies aren’t locked into proprietary formats or closed ecosystems.
3. Cost Efficiency and Smarter Resource Use
Companies that switch from platforms like Snowflake to Databricks often experience a significant drop in monthly costs. With Databricks, storage is optimized, compute resources are used more efficiently, and there’s no need for separate licenses for analytics, ML, or AI tools.
Databricks uses a pay-as-you-go pricing model with no upfront fees. Snowflake, on the other hand, uses a credit-based system where the cost depends on the edition: Standard is $2.00 per credit, Enterprise is $3.00, and Business Critical is $4.00 per credit.
To give you an example, one client with 5TB of data, over 200 tables, 10-15 ETL jobs running at the same time, and more than 30 BI dashboard users saw their monthly cost go from around $25,000 on Snowflake to $15,000 on Databricks after making some improvements, a 40% saving.
P.S. If you're still exploring the differences between data lakes, warehouses, and lakehouses, check out our blog on Data Lake vs. Data Lakehouse vs. Data Warehouse to understand which model best suits your goals.
Considering a Migration from Snowflake? You're Not Alone.
Snowflake is a well-known cloud data warehouse, but as businesses scale and adopt AI, many are looking for more flexible and cost-effective platforms. Databricks, with its Lakehouse and AI-first capabilities, is becoming the natural next step.
But migrating from one platform to another isn’t just about shifting data. It’s about ensuring your operations continue smoothly during and after the move. That’s why a zero-downtime migration strategy is essential.
We’ve covered the complete, step-by-step technical guide in our blog: How to Migrate from Snowflake to Databricks: A Zero-Downtime Guide for Data Engineers
Here’s a high-level overview of the process:
Step 1: Assess Your Current Setup
Before migrating, audit your Snowflake environment:
This helps you decide what to migrate first and what can wait.
Step 2: Plan Your Migration Strategy
A phased migration is usually the safest choice. You move data and workloads in chunks, test performance at every stage, and gradually switch users over. This ensures you maintain operational continuity and reduce risk.
Step 3: Extract, Transform, and Load Data
Export your Snowflake data into cloud storage using efficient file formats like Parquet. Make sure you transform data to match Databricks’ formats and optimize for performance before loading.
Step 4: Enable Ongoing Operations Without Downtime
Set up dual-write pipelines so both Snowflake and Databricks are updated in real time during the transition period. This ensures no data is missed, and both platforms stay in sync.
Validate results regularly. Use reconciliation checks to compare row counts and content between systems until the full cutover is complete.
The Real Strategic Value of Databricks
While technical migration is important, it’s equally critical to understand what you’re gaining. Databricks isn’t just a technical upgrade, it’s a business transformation engine.
Here’s what companies gain after switching:
1. End-to-End AI amp; ML Enablement
Databricks supports machine learning and AI across the entire lifecycle, from exploration to training, deployment, and monitoring. With MLflow and LLM integration, your team can build and deploy advanced AI models, including Generative AI, in one place.
2. Self-Service Analytics with GenAI
Databricks' Genie feature allows non-technical users to ask questions in natural language and get back data visualizations or SQL queries, democratizing analytics across the organization.
3. Scalable, Secure, and Governed
Databricks ensures centralized governance through Unity Catalog, which gives teams detailed control over data access, compliance, and security, without compromising speed.
4. Built for 2025 and Beyond
The gaining popularity of AI, big data, and real-time decision-making demands tools that are fast, unified, and intelligent. Databricks is designed for this future, with capabilities that traditional data platforms can’t match.
To Wrap Up
Moving to Databricks is more than just a migration, it’s a transformation. With the ability to handle everything from BI to AI on a single platform, Databricks helps reduce cost, eliminate silos, and future-proof your data strategy.
By following a well-planned, phased migration approach, you can transition from Snowflake to Databricks with zero downtime while gaining all the strategic benefits the Lakehouse offers.
For technical guidance, visit: How to Migrate from Snowflake to Databricks: A Zero-Downtime Guide for Data Engineers
For strategic insights, read our full blog here: Why Your Business Should Consider Databricks in 2025
Now is the time to make your data work smarter, not harder.
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