Snowflake’s values of speed, scalability, and sharing are built throughout. For a rapidly expanding organization that needs to handle a significant number of concurrent workloads and share data across multiple partners efficiently and securely, Snowflake is a strong choice.
For Databricks, the foundation of data science is evident in the platform’s value pillars. Databricks is suited for a wide-variety of machine learning cases. Organizations focused on scalable data engineering, collaborative data science, and transforming large volumes of unstructured data should be intrigued by Databricks.
Both of the platforms can be spun up on AWS, Azure, and GCP platforms. Snowflake does not require any pre-planning or maintenance to start, eliminating the need for a database administrator in many cases. It automatically runs across three availability zones, allowing for replication to an alternate cloud.
Fully elastic autoscaling, a hallmark feature of Snowflake, means increasing or decreasing the size of an instance can be completed easily.
For creating a Databricks cluster, there’s three different cluster modes: Standard, High Concurrency, and Single Node. For the user, deciding which cluster mode to use can be a challenge but is the key to managing cost and performance.
Databricks also features autoscaling by leveraging reporting statistics to scale up, or, remove workers in the cluster. To use and maintain Databricks, users need to have some level of knowledge surrounding cloud infrastructure components and how they work together.
Snowflake’s architecture means a rapid rollout to start, with levels of automation. This makes it a great choice for an organization that may not have the initial bandwidth or expertise in the platform.
The customizable options of clustering for Databricks is a very attractive feature but requires strong competency in the platform and users must choose between cost and performance during configuration.