Data lakehouses have evolved as a hybrid of data warehouses and data lakes to optimize and deliver flexibility of data storage and data access. In the journey of utilizing data to drive economic value for the enterprise, you have many choices on how to organize and manage data. Let’s take a quick look at a couple of industry leaders, Snowflake and Databricks, to better understand how and when to leverage their strengths.
What’s the difference between Databricks and Snowflake?
Snowflake is a next generation enterprise data warehouse designed and built in the cloud. The architecture is optimized for cloud infrastructure consumption while enabling ubiquitous access to multi modal access to data. Databricks is an advanced data management technology designed on open source-based technology with optimized data access for ML and AI automation. Although Snowflake and Databricks have similarities, there are a few key differences which need to be considered for your specific use cases.
With Snowflake designed and built for the cloud, it’s optimized for a consumption-based model that triggers the use of cloud compute and storage only when activated. Snowflake controls the data and runs on a hyperscale infrastructure such as AWS, Azure and GCP, thereby touting the unlimited access to compute and storage.
Databricks on the other hand, segregates the data from technology and process. With its open-source architecture, Databricks can access data wherever it resides. This provides enterprises with an advantage for use in a hybrid deployment mode. Hence with Databricks, in addition to the hyperscale, you can leverage the technology across data that may be resident on legacy infrastructure.
Snowflake is built on a proprietary software model based on SQL. This is an advantage with flexibility and reduced technical complexity for data management. It provides a significant advantage for end user processing using SQL, and is generally better suited for data intelligence, visualization, and other analytical processing.
Databricks has a more complex software architecture that includes R, Python and SQL. Although offering more options in terms of technology to access data, it is generally better suited for advanced processing for ML and AI. For example, Databricks can access data on Snowflake and manipulate that data with ML, AI and return the results to Snowflake for visualization. This would be a logical form of adopting a best of breed approach with both technologies since they have their independent strengths.
Deciding on which technology has many variables. However, one aspect to consider is what it will take to maintain and manage the chosen technology from a long-term total cost of ownership perspective. With Snowflake, the core skills required are advanced version of the SQL. Although a proprietary version, knowledge of SQL is adequate in terms of adopting and working with data cloud solution. Generally, these skills are more user friendly and don’t require significant integration expertise.
Databricks supports a range of technical components and programming languages including R, SQL and Python. Depending on the use cases, you can mix and match these technologies to achieve the result. Typically, this requires more advanced skills for technology utilization and integration.
Data lakehouses have the advantages of efficient and organized data storage, which is typical to Data warehouses but also have the data lake structure and data management features. This approach gives enterprises the benefits of cost efficiency in terms of data organization but also the flexibility and speed to leverage advanced automation from ML and AI against a more typical data lake architecture. Understanding the use cases, the business benefits from delivering on the use cases and the overall total cost of ownership are all key elements before you decide on how to address your data management requirements.