As businesses continue to embrace cloud computing, choosing a cloud platform that can scale, provide ongoing flexibility, and support business operations, is critical. Databricks and Snowflake are leaders in the cloud platform market, allowing organizations to consume, analyze, and manage large volumes of data.
Equipped with an understanding of current and future business needs, it is important to evaluate cloud platforms based on their provision of data security, customer service, data structure, collaboration, automation, and support for machine learning.
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|Big Data, Analytics, Machine Learning, Data Engineering, Data Science, Data Warehousing
|Data Warehousing, Data Management, Data Collaboration
|Handles all data types in their original form
|SQL, Koalas, Spark Dataframe
|Scaling with limitations
|Batch or Stream
|Steep learning curve
|Easier to learn
In this article...
What is Databricks?
Building on expertise with academia and the open-source community, Databricks began in San Francisco in 2013. Ten years later, Databricks boasts a client list that includes more than 7,000 organizations.
Combining the seemingly limitless storage capabilities of data lakes with structured data warehouse storage, Databricks Lakehouse is a powerful and flexible hybrid cloud platform solution compatible with major cloud providers including Alibaba Cloud, AWS, Azure, and Google Cloud.
What is Snowflake?
Founded in 2012 with headquarters in Montana, Snowflake became a cloud-based powerhouse after a remarkable $3.4B IPO. Snowflake currently manages over 250PB of data for more than 1,300 partners and 6,800 customers.
Snowflake boasts being a centralized cloud platform solution with unparalleled ease of use and speed of implementation. Snowflake’s platform includes support for data warehousing, data lakes, data engineering, data science, data application development, and data sharing that can be integrated with AWS, Azure, and Google Cloud.
Read more: Top Big Data Tools & Software Buyer’s Guide
Databricks vs. Snowflake: Data security
Databricks provides data encryption, isolation, and auditing, both at rest and in motion.
Additional security features include isolation at multiple levels:
- Workspace level: so each team or department can use a separate workspace.
- Cluster ACLs: to restrict users who can attach notebooks to a given cluster.
- High concurrency clusters: including process isolation, JVM whitelisting, language limitations (SQL, Python, etc), and the safe coexistence of uses with varying privilege levels.
- Single-user cluster: private, dedicated cluster.
Databricks user activities are logged and preserved in cloud storage.
Snowflake customer data is always encrypted in flight and remains encrypted at rest.
A series of security controls ensure network communications are secure, identity and access are controlled and monitored, and data-level recovery and failover can be managed without risk to data safety.
Which to choose for data security?
Both Databricks and Snowflake are GDPR-compliant organizations. This commitment demonstrates an understanding of the need for rigid data security that includes attention to lawfulness, fairness, transparency, purpose limitation, data minimization, accuracy, storage limitation, integrity, confidentiality, and accountability.
Both cloud platforms deliver similar and reasonable security features and functionality. For basic data security, this results in a tie.
For organizations that employ ADS or AMS teams, Databricks provides workload security that includes code repository management, built-in secret management, hardening with security monitoring and vulnerability reports, and the ability to enforce security and validation requirements.
Databricks vs. Snowflake: Customer service and ease of use
Though some self-management is possible, Databricks is aimed at a more technical audience and comes with a steeper learning curve that often requires manual input.
Snowflake has paid particular attention to the development of an intelligent infrastructure as part of its cloud platform. As a fully managed service, transparent improvements and automations are added regularly with no action required, reducing risks, and improving efficiencies.
A comprehensive self-management dashboard further reduces the ongoing need for support.
Without the need for manual management, organizations can operate at scale, optimize costs, and minimize downtime, while maintaining high levels of data security, availability, and data resiliency.
Which to choose for customer service and ease of use?
Both cloud platforms offer online support, comprehensive documentation, online communities, and training resources.
Snowflake comes out ahead in this category with the provision of a more user friendly interface together with 24/7 live support as compared to Databricks only offering live support during business hours.
Databricks vs. Snowflake: Data structure
Databricks will consume all data types in their original format.
Snowflake stores data in an internal, structured, format. Data can be uploaded in semi-structured and structured files, which will be automatically transformed by Snowflake prior to storage.
Which to choose for data structure?
This is one category where there isn’t a single right answer. Organizations need to evaluate the types of data that will be consumed, the need for sharing and retrieval, and the requirements of ancillary systems.
Databricks vs. Snowflake: Collaboration
Using the Databricks Delta Sharing tool, data engineers, scientists, analysts, suppliers, and developers can exercise controlled, platform-agnostic, data access. Collaboration efforts can be supplemented with highly secure and predefined templates, notebooks, and dashboards, each with the ability to run complex computations and workloads in a variety of development languages.
Databricks integrates with major data platforms including Tableau and PowerBI.
Snowflake demonstrates a commitment to collaboration with Data Sharing. By creating business assets from data, Snowflake enables sharing data and database objects effectively and efficiently, with monetization options that provide potential revenue opportunities. Data can be shared using controlled and customized views to partners, vendors, and customers.
Which to choose for collaboration?
There is little question that Databricks offers a much more comprehensive suite of collaboration tools, but whether they are a clear winner in this category depends on whether those bells and whistles are helpful and necessary. For simple and secure data sharing, look to Snowflake.
Databricks vs. Snowflake: Automation
Databricks uses Delta Live Tables to apply useful BI, data science, and machine learning to the consumption of data. By performing validation and integrity checks, Databricks can prevent bad data from flowing into database tables, monitor data quality trends over time to provide actionable insight, handle streaming workloads to protect SLAs by scaling nodes up and down as required, handle errors without intervention and with easy replay, and maintain data dependencies across the pipeline.
Snowflake automation is rudimentary with basic features like Snowpipe that allow for continual data loading and database updates.
Built-in automation functions available with Snowflake are focused on minimizing administration functions and processing steps more than data management.
Which to choose for automation?
Databricks specializes in consuming and analyzing data at any scale while extracting and acting on business intelligence. It’s the obvious choice for advanced automation.
Databricks vs. Snowflake: Machine learning
Databricks provides ML environments with the assistance of frameworks like Tensorflow, Scikit-Learn, and Pytorch. Experiments, models, and runs can be shared, tracked, and managed using a built-in, central repository.
While data science is a core competency within Snowflake, providing data silos for access to actionable insights and helping to better understand user behavior and product usage, true ML requires the use of third-party ML tools like Spark, Alteryx, Qubole, and Databricks.
Which to choose for machine learning?
Unless your organization prefers a particular third-party ML tool, Databricks is the clear winner in this category.
Is Databricks right for you?
- An open-source foundation allows the procurement of storage from any chosen cloud vendor.
- Databricks allows for the analysis of unstructured data.
- Not being a fully managed service means needing to evaluate, deploy, and manage services from additional providers.
Is Snowflake right for you?
- Considerable investment into an ecosystem rich with partnerships and integrations that help to future-proof a cloud platform investment with ongoing extensibility potential.
- A fixed pricing model keeps costs predictable.
- Administration tasks are simplified.
- Ease of configuration and management is diminished when third-party apps are required to achieve the required functionality.
- Administration functionality cannot always be modified or fine-tuned if features and tasks aren’t ideal out of the box.
- Performance struggles with large data volumes.
Making the right choice
When comparing Databricks vs. Snowflake for your company, the best answer depends on your needs. If you have an on-staff development team capable of maximizing all of Databricks’ potential, then it’s a solid choice.
However, if you’re operating a smaller business without a robust dev team, Snowflake might be the better choice for you. Its more user-friendly operation will likely make it a more welcome addition to your technology stack.