Snowflake and Databricks are two powerful platforms that are often compared when it comes to data and analytics needs. However, it’s important to approach this decision with clarity and thoughtfulness to ensure that the platform you choose is the right one for your organization.
The first key area to consider is the use case. Snowflake is a great option for handling extremely large data sets and has built-in data governance features, while Databricks is better suited for real-time data processing and machine learning. It’s important to understand your organization’s mission and specific data needs before making a decision. Many data leaders tell me they spend months understanding the business requirements before even entertaining the idea of what technology to bring in to solve the pain points.
Scalability is another crucial factor to consider. Both platforms have the ability to scale, but it’s important to understand the limits of each and how they align with your organization’s growth goals. It should be obvious that another critical area is pricing. While both platforms offer cost-effective solutions, it’s important to weigh the costs against the benefits and the long-term ROI.
Integration from both a technical perspective and also a governance one can feel like a bit of an afterthought, but it’s crucial, especially if your organization is already using certain tools or software. It’s important to understand how easily the platform integrates with your existing tech stack. Snowflake has built-in data governance features, while Databricks offers granular access control and auditing features.
In a recent LinkedIn post, I asked my network which platform they preferred and why. The majority (71%) chose Snowflake, citing its cost-effectiveness, ease of management, and ease of maintenance. However, it’s important to note that Databricks has its own strengths and advantages, such as its real-time data processing capabilities.
It’s important to remember that choosing a platform is not a one-size-fits-all decision. Your organization’s specific needs and goals should guide your decision-making process. Additionally, as technology continues to evolve, it’s important to stay informed and adaptable to changes in the data platform space. Who knows, there may be a new platform in the next 5 years that will revolutionize the way we think about data and analytics.
This is a really interesting space, and I am enjoying watching it evolve. We currently have several live Data Engineer roles available at top-performing organizations, please reach out if you’d like to know more.
Natalia Kurasinski
Natalia is a highly skilled talent partner. With a passion for identifying top talent and a strong commitment to customer service, she has established herself as a valuable resource to both clients and candidates in the data & analytics industry.
Natalia is highly skilled in all aspects of the talent solution process, including sourcing and screening candidates, conducting interviews, negotiating offers, and onboarding. She is able to quickly identify top talent using Read more.
