Why You Should Choose Databricks Over Snowflake to Achieve Your Data Vision

Csanád Bánhegyi
Csanád Bánhegyi
13 Jun 2023 · 4 min read

In today’s data-driven world, organizations constantly seek ways to optimize their data architecture to unlock valuable insights and drive business growth. As a data engineering and data science consulting company, we recently came across a fintech company seeking to improve its data infrastructure, from architecture to how it worked with data.

What are their requirements for a cloud data platform?

  • Lower TCO (total cost of ownership)
  • Potential for machine learning applications
  • Extensive data engineering functionality

The company had initially experimented with Snowflake, yet, looking at their priorities and expectations, DATAPAO recommended they use Databricks instead as the best way to reach their vision. 

Why? Read on to find out why it’s an excellent choice for such data architecture needs and use cases.

The Benefits of Databricks

Databricks is the originator of the Lakehouse platform. It provides an environment for data engineering, data science, and collaborative analytics, enabling organizations to streamline their data workflows and drive innovation.



Low Cost and Scalability

A key advantage of Databricks is its simplicity, cost-effectiveness, and scalability. Customers can leverage the data stored in their accounts using open standards and the Photon engine with a cloud-based infrastructure to scale their data processing resources per their needs. The consumption-based pricing model enables customers to allocate resources efficiently while avoiding unnecessary and unexpected expenses.

Advanced Analytics and Machine Learning

Databricks empowers ML teams to prepare and process data, streamlines cross-team collaboration, and standardizes the entire ML lifecycle from experimentation to production. It enables customers to use popular machine learning libraries and frameworks, including but not limited to Apache Spark MLlib, TensorFlow, and PyTorch, so they can build sophisticated models, perform advanced analytics, and conduct data-driven decision-making.

Collaborative and Interactive Workspace 

Databricks provides collaborative and interactive workspaces that enable data engineers, data scientists, and business analysts to work together seamlessly. With features like notebook sharing, version control, and real-time collaboration, users can easily share code, insights, and best practices, fostering a culture of cooperation and accelerating project timelines.

Ecosystem Integration

Databricks integrates seamlessly with various data storage systems, including Azure Data Lake Storage, Amazon S3, and Google Cloud Storage. This allows customers to leverage their existing data infrastructure investments and avoid vendor lock-in. Additionally, Databricks supports popular business intelligence tools and data visualization frameworks, making it easier for customers to gain actionable insights and present data in a meaningful way.

Streamlined Data Engineering

Databricks simplifies the data engineering process with robust ETL capabilities. With built-in support for data ingestion, data transformation, and data quality management, customers can ensure the reliability and accuracy of their data pipelines. Apache Spark and Photon enable users to do data engineering at scale. The support for various languages (Python, Scala, Java, R) caters to different data engineers while the unified platform reduces the complexity of managing multiple tools and enables efficient data engineering workflows. 

Advanced Security and Governance

Databricks prioritizes data security and governance with Unity Catalog, a unified governance solution for all data and AI assets, including files, tables, and machine learning models. Unity catalog lets users centrally manage and govern all data assets, manage fine-grained access controls, enjoy unified and secure data search capability, get enhanced query performance at any scale with low latency metadata serving, receive automated and real-time data lineage for audits, and secure data sharing across organizations with Delta Sharing.

Should You Choose Databricks over Snowflake?

Of course, there is no one-size-fits-all answer. The best solution for one company may not be ideal for another. However, for the requirements we have outlined above, the benefits of reduced total cost of ownership, simplicity for multiple personas, use of open standards, and ability to govern the lakehouse with Databricks outweigh what Snowflake provides – especially so when we consider the plan to integrate machine learning, which is not natively supported in Snowflake.

After thoroughly evaluating this particular company’s requirements and considering their need for optimized costs, scalability, machine learning capabilities, and extensive data engineering features, DATAPAO recommended Databricks as the preferred data architecture solution for this fintech company. 

And indeed, the fintech company in our use case reached the same conclusion. Today, they’re benefiting from a powerful cloud data platform. With its powerful analytics capabilities, collaborative workspace, various integrations, and robust security features, Databricks empowers organizations to harness the full potential of their data and drive business growth. By choosing Databricks over Snowflake, the customer can embark on a data-driven journey to unlock valuable insights and revolutionize their fintech solution.

Helping Organizations Realize Their Data Vision

The above is one example of how DATAPAO’s data transformation expertise can help companies make the right decision.

As part of our board-level consulting and advisory services, DATAPAO’s team of experts will explore in depth your current and future needs and vision, as well as assess current infrastructure and its limitations and compatibility. 

Once we recommend a specific solution, you will be presented with a detailed breakdown of the benefits you will gain from it and the reasoning behind that choice – similar to what you’ve read above. From there, we remain at your disposal to implement the migration, provide experts to support the transition, as well as enable the new technology with trainings and documentation.

As a result of our in-depth discovery of the goals and the vision, this fintech startup was able to choose the data architecture solution that matches their needs. Do you also want a solution that empowers your growth with data? Contact DATAPAO to book your free 30-minute consulting session today.