Databricks vs. Snowflake: Which One Fits Your Data Strategy?

Presently many of us are of the opinion that data is the new oil. However, available data needs to be skillfully collected, organized, as well as leveraged for maximum benefit. For your information, Databricks and Snowflake are two of the prominent data management platforms. Databricks' revenue for the fiscal year ending January 31, 2024 was over $1.6 billion, which is a 50% year-over-year increase. Snowflake's revenue for the financial year ending January 31, 2024 was over $2.8 billion, which is up from almost $2.1 billion the previous year.

In this blog post, we learn what Databricks and Snowflake are, their respective benefits, as well as when to utilize the two popular data management platforms.

What is Databricks?

Databricks includes the novel Lakehouse architecture that contains the best aspects of data lake as well as data warehouse. The former utilizes Apache Spark. For your information, Spark is the dominant big data processing tool. Databricks is strong in advanced analytics as well as machine learning. Many organizations leverage Databricks for real-time data processing. The architecture of Databricks is more complex than Snowflake’s. Did you know that Databricks is able to process data around twelve times faster when compared to other data management platforms? Databricks comes with functionality including integrated development environments as well as version control enabling different data professionals to work together on projects. The platform is designed as a PaaS (Platform as a Service).

Databricks has the seamless scalability of data lakes as well as the superior performance of data warehouses. The platform handles AI-related workloads at competitive rates. Founded in 2013, Databricks facilitates data professionals to collaborate efficiently as well as effectively. The platform provides support for different coding languages including R, SQL, and Python. Databricks can handle structured, semi-structured, or unstructured data in an efficient as well as effective way. Note that Databricks is excellent for applications such as recommendation engines as well as predictive analytics. The platform has the facility for custom configurations as well as optimizations. This platform has inbuilt machine learning libraries. There exists support for powerful ML frameworks including TensorFlow and PyTorch. Databricks integrates seamlessly with prominent BI tools including Tableau as well as Looker.

Advantages of Databricks

• No Responsibility of Data Storage: Databricks focuses on data processing. It does not need to do data storage. Data can be stored on cloud platforms or even on-premises.

• Comprehensive Documentation: The documentation of Databricks is well detailed. Users can refer to it for resolving their doubts as well as queries.

• Active Community: Users of Databricks can approach the community for support. If they run into challenges or issues, there will surely be somebody to help them out.

• Collaboration Functionality: There exist shared notebooks as well as dashboards in Databricks for users to collaborate effectively.

• Highly Secure: Databricks boasts of enterprise-grade security which makes the platform resilient to threats such as cyberattacks and data breaches.

• Superior Integration: Databricks can seamlessly integrate with open-source tools and technologies as well as do away with vendor lock-in.

• Single Platform: The platform has a single environment catering to activities such as machine learning, data engineering, as well as data science.

What is Snowflake?

Snowflake is a leading cloud-based data warehouse technology. The storage module and compute module are separated in Snowflake’s architecture. The technology operates as a SaaS (Software as a Service). Snowflake is highly popular among organizations belonging to the financial sector. The platform is known for its cloud-agnostic nature. Snowflake has a simple as well as predictable cost structure. The processing module is made up of virtual warehouses. The latter are independent compute clusters capable of scaling dynamically as per workload requirements. Note that the architecture of Snowflake is simpler than that of Databricks.

Snowflake is ideal for SQL-based analytics offering superior performance for the same. The platform does data warehousing at competitive rates. Snowflake comes with a powerful SQL engine. For your information, the platform was founded in 2012. Snowflake includes role-based access control functionality for security purposes. Snowflake is ideal for organizations with data predominantly in organized formats. The popular platform boasts of automatic scaling as well as resource management.

Advantages of Snowflake

• Faster Query Response: Thanks to intelligent caching, database queries have low latency, delivering faster results.

• User Friendly: Snowflake boasts of an intuitive UI. The former utilizes SQL, which most users are familiar with.

• Convenient Pricing Model: Users pay only for storage and compute resources used, billed separately. Resources can be scaled as per requirements.

• Time-travel Facility: Users can revert to older saved versions of data, a unique feature offered by Snowflake.

• Micro-partitioning Feature: Data is partitioned into separate units enabling faster performance. Smaller units reduce latency.

• Superior Security and Compliance: Snowflake keeps data safe and secure and ensures regulatory compliance.

• Robust Data Recovery: Multiple copies are stored in different locations preventing data loss and business downtime.

When to Utilize Databricks

• If your organization has individuals with technical knowledge to fully leverage Databricks.

• If there is raw data present.

• Utilize Databricks for complex data science as well as machine learning.

• Utilize Databricks for research & development activities.

• For retail, media, or sectors with diverse data sources.

• Choose Databricks for machine learning, predictive analytics, and AI-related demands.

• For entities managing vast datasets requiring complex transformation or analysis.

• When there is requirement for multiple language support.

When to Utilize Snowflake

• If your organization has members with limited technical knowledge.

• For sectors where departments and external partners need access to specific datasets.

• In scenarios involving high concurrency.

• When data is well-structured.

• For real-time analytics, dashboards, and data visualization.

• If your organization’s priority is ease of use.

• Choose Snowflake for batch-processing demands.

A Final Note

The best choice depends upon long-term data strategies. It is possible to utilize both Databricks and Snowflake, benefiting from their synergy. Remember that while they have differences, certain features overlap.

How to Go About Choosing between Databricks and Snowflake

• Use in-house expertise if available.

• Otherwise, consider outsourcing to a reputed firm.

• Conduct due diligence and research.

• Engage stakeholders before making the final decision.

With relevant experience and expertise in Databricks and Snowflake, CoffeeBeans can help your organization integrate these platforms into existing systems. Our clients value our transparency, real-time response, and adherence to deadlines. Reach out at enquiries@coffeebeans.io for customized solutions.