A Practitioner's Guide to Databricks vs Snowflake
May 8, 2023
When comparing Databricks and Snowflake across various features and capabilities, it is evident that Databricks holds a competitive edge for TCO sensitive organisations seeking a unified analytics platform that supports all their data, users and use cases. Databricks offers a comprehensive solution for data-driven organisations and offers superior performance in:
● ETL workloads;
● processing different data types;
● cataloguing and lineage;
● AI/ML ecosystem integration; and
● real-time data processing.
Databrick’s innovative Delta Share feature through the Unity Catalog enables seamless and secure data sharing without relying on traditional connectivity patterns. While Snowflake excels in specific areas such as data warehousing and ease of deployment, Databricks emerges as the better platform for organisations looking to unlock the full potential of their data and drive impactful business decisions.
Introduction
Fujitsu Data & AI, a specialist division of Fujitsu within the APAC region, works with enterprise organisations and governments to find, interrogate, and help solve the most complex data problems across Australia, New Zealand, and Asia. Our purpose is to accelerate the growth of our customers' Data Analytics and Artificial Intelligence capabilities to unlock the value within their data. We have one of the largest data engineering capabilities in Australia and are backed by Fujitsu, one of the largest ICT service providers in the world. Using industry leading specialists, we offer full breadth, end-to-end Advanced Analytics, Business Intelligence and AI capabilities. We are a Premium Databricks delivery partner and have global partnerships with Microsoft, AWS, and Google. Our strong Databricks partnership is shown through our continued success with our clients, and we have won Databricks Regional Systems Integration Partner Asia Pacific and Japan in 2021 and 2022. Utilising our extensive specialist experience, we in collaboration with Databricks, have created this article to provide a practical perspective on the differences between Databricks and Snowflake.
Macro trends and why should you care?
In today's rapidly evolving digital landscape, businesses are inundated with a deluge of data, as the proliferation of devices, applications, and networks generates an ever-growing volume of information. As a result, companies are grappling with the challenges of efficiently managing, processing, and harnessing this massive influx of data to unlock its full potential and drive strategic decision-making.
How does a company determine the most suitable tools for addressing the mounting challenges of safeguarding sensitive data, managing the ever-increasing volume, velocity, and variety of data, and fulfilling the endless requirements of data-savvy users? Ensuring the efficient handling of the diverse sets of data and optimising their outputs necessitates complete data unification in a single location, managed by a centralised team.
Numerous innovative vendors, such as Databricks and Snowflake, have emerged in response to these real-world challenges, leveraging their extensive experience and cutting-edge technologies to assist companies in effectively managing and navigating the complexities of today's data-driven landscape. Both vendors offer data platform solutions across the three main cloud providers: AWS, Microsoft Azure, and GCP.
Before we can understand the differences between these two products, we need to understand the changing needs of companies and the new landscape in which they are competing. Companies are faced with an ever-changing data landscape as data capture, analysis, and solutions drive a competitive edge. Companies need to analyse and act on the insights produced by data solutions faster to stay competitive. The speed and types of data - unstructured; semi structured; and structured data sources, need to be processed by data platforms in a secure manner to produce the results required by the business.
