Databricks Vs. Snowflake; What Works Best for You
Astute data management has become a prerequisite for reaping the full benefits of digital transformation and Big data. Snowflake and Databricks have emerged as the two leading modern cloud solutions for the troika of data science, engineering, and analytics. The key characteristics of the two are compared below on the basis of some major parameters i.e.
· Scalability
· Architecture
· Use cases
· Pricing
· Performance
· Security
· Data Structure & Support
· Data Protection
However, before getting into the analysis, it is imperative to briefly describe the platforms.
An Introduction to Snowflake
Snowflake is a Software-as-a-Service cloud-based data warehousing platform that is supported by all Cloud providers. It is a central platform that fuses data organization, collection, and transformation together.
Snowflake entertains multiple, independent clusters that make high scalability possible. Besides zero resource contention, Snowflake also assists the development of efficient data pipelines in the language of one’s choice. Snowflake has an in-built mechanism for data monitoring that ensures error detection and elimination, resource management, authentication, workload regulation, and access control.
An Introduction to Databricks
Just like Snowflake, Databricks is also a virtual data platform. But the most striking difference between the two is that Databricks works as a data lake and not as a data warehouse. It is a cloud-based, end-to-end solution for those who want to secure the benefits of data warehousing while retaining the freedom of data lakes.
Databricks provides a layer of Delta Lake that is capable of working with structured, semi-structured, and unstructured data. Databricks has a peculiar architecture that provides advanced integration and organization to data engineers and analysts.
Weighing up Snowflake & Databricks
The following comparison will help the readers understand what solution works best for them and for what purposes.
Managing Scalability Vis-A-Vis Demand
Changing scalability is easier with Snowflake. However, scalability as a characteristic is offered by both platforms.
Snowflake performs better in this area because its storage and processing layers work individually, i.e., independently from each other. It also separates various workloads from one another on resources. Therefore, it is naturally able to address the varying needs according to the fluctuating workload. While working with Snowflake, a person can also change the cluster size. This becomes a rather difficult task in the case of Databricks as it is relatively more complex.
Databricks does not afford the near-infinite scalability of Snowflake, but it has an in-built auto-scaling feature that keeps on removing idle time according to utilization levels.
A Comparison of Architecture
Snowflake
Snowflake separates compute from storage. It works through three layers that combine the features of shared-disk and shared-nothing architectures. The first layer houses all the data, be it in structured or semi-structured form. The second layer makes possible non-disruptive scalability by setting up independent yet connected virtual data warehouses. Snowflake supports a multi-cluster infrastructure that also benefits from this property. The cloud services layer, aka the executive layer of Snowflake, works on ANSI SQL to infuse coordination into the whole system.
Databricks
Databricks is based on Spark. It supports;
· Data plane
· Control Panel
The two components perform different functions. As depicted by the name, the data plane stores all the data, whereas the control panel constitutes various services provided by Databricks. There is another layer to the architecture in the form of Delta Lake, i.e., Databrick’s version of a data warehouse. It consists of three tables that retain data of different quality. These tables let data engineers segregate their files to separate the refined data from the unrefined one.
Both Snowflake and Databricks can run on AWS, Azure, and Google Cloud Platform.
Usual Use Cases
Snowflake provides drivers like ODBC that make integration easier. This cloud data warehouse is used for the following methodologies:
· Structured Query Language use cases
· Business Intelligence use cases
· Both cases can be utilized for dashboarding and reporting
· Artificial Intelligence and Machine Learning use cases, but only with additional support.
Databricks finds its major employment in:
· Machine Learning and Data Science
· Extract, Transform, Load (ETL)
Neither Snowflake nor Databricks is suitable for use cases that demand streaming data or open systems.
The Difference in Pricing
Snowflake charges users for storage separately. It bases its charges on the consumption levels in terms of warehouse size and the time taken. It offers pre-configured sizes, for example, X-Small, Large, X-Large, etc. Snowflake also takes into account the total load being put forward. As the size of the warehouse increases, the price spikes too.
Databricks proves to be less expensive than Snowflake when it comes to data storage as it provides its customers with individual storage environments that can be customized for unique needs. For computing, Databricks designs its prices according to Databricks processing units. DBUs measure computes time per second. Three tiers have been offered by Databricks, which eventually determine the hourly rate.
Comparing Performance
It is quite unfair to compare the performative attributes of Databricks and Snowflake while using the same yardstick, as both best address different use cases. Databricks is best suited for Data Science/Machine Learning and Analytics use cases. Whereas, Snowflake performs most efficiently for SQL and ETL purposes.
Snowflake improves its performance by automatically spurring into action schemas, and Databricks uses Spark to handle schema-less data. While Snowflake sees through an automatic tuning of queries, one has to tune Delta Lake tables according to a specific format in the case of Databricks.
Security Analysis
Both Snowflake and Databricks see to the security needs of businesses sufficiently. Nonetheless, they differ from each other in how they ensure this security. The biggest difference appears in the encryption area. Snowflake is on always-on encryption mode, but Databricks performs encryption during rest. Both provide role-based access (in accordance with one’s role in an organization and the level of access one might need). The facility of setting up individual customer keys is present on both platforms.
Data Structure and Support
Snowflake is designed to replace data warehouses, but it is not suitable for huge amounts of data. Neither is the ideal option when one’s working with streaming data. Snowflake simplifies the BI-related work of data analysts by providing detail-oriented and micro-managed data columns and rows. However, the departments of data science, AI, and data engineers can benefit the most from Spark-based Databricks.
Databricks caters to the individual needs of data scientists by providing a platform that puts them in charge of their data. Its capabilities surely extend to more aspects than Snowflake’s do. Nevertheless, its architecture is quite complex and cannot be mastered easily.
Both Snowflake and Databricks support structured and unstructured data. They enable data engineers to structure and organize incoming data files according to their preferences.
Data Protection
Both Databricks and Snowflake contain a Time Travel feature that records or keeps all the previous versions of data sets to make them available for future use. Snowflake also has a Fail-safe feature that carries the function of Time Travel further by extending the period for storage of data.
Final Verdict
To conclude, Databricks and Snowflake tend to differ in the way they work and how they are utilized. Databricks lays the foundation for a data lakehouse, while Snowflake provides a ready-made platform for data analytics. Oftentimes, both platforms work to complement each other’s function.
Author bio:
Hassan Sherwani is the Head of Data Analytics and Data Science working at Royal Cyber. He holds a PhD in IT and data analytics and has acquired a decade worth experience in the IT industry, startups and Academia. Hassan is also obtaining hands-on experience in Machine (Deep) learning for energy, retail, banking, law, telecom, and automotive sectors as part of his professional development endeavors.
