Why Databricks vs Snowflake?

Kevin Neal, one of our friends at P3iD Technologies, once said, “Data is the new oil”, and those words ring truer each and every day.

2.5 quintillion bytes of data are generated daily, and while that statistics matters in itself, how we interact with said data matters even more. Over the past decade, dozens of household names have popped up within the data space, offering new solutions in storing, analyzing, and utilizing the data that drives important business decisions. Two of those household names are Databricks and Snowflake, but why is the topic of Databricks vs Snowflake so prevalent? 

Although they are two seemingly different data solutions with widely different use cases, the Internet seems split on which data solution is truly the best (spoiler alert: they are BOTH the best in their respective fields). The debate has sparked because of certain overlapping aspects between both solutions, but to understand that you’ll first need to know what Snowflake and Databricks are. 

What are Snowflake and Databricks?

Understanding the Data Lake and Data Warehouse

The companies Snowflake and Databricks may be familiar to you, but what are the basis of their solutions? 

At the core of Snowflake and Databricks are data lakes and data warehouses, two very different means of storing and processing data. Both are extremely scalable data storage repositories, but the main difference comes from the inputted data structure: data lakes are able to store any form of data in a flat architecture whereas data warehouses prefer structured data for fast querying in a highly structured environment. 

Data lakes better suit the needs of companies that don’t need to act on data right away, with their storage and processing capabilities being decentralized for stronger scaling and lower costs. Data warehouses meet the needs of companies that prefer quick data analysis by offering highly structured processes, such as storing data via SQL queries, in order to provide advanced processing, while centralizing their storage and processing actions.

Data lakes offer cost effective solutions, data warehouses provide performance-heavy solutions.

Snowflake

Snowflake, one of the leaders in data storage, is a SaaS data warehouse that is built on top of cloud storage infrastructure, typically supported by major hyperscalers like Google Cloud, Azure, and AWS. Snowflake is known for its ability to store and analyze your data within a single interface, providing scalable services to enhance your experience.

The solution’s uniqueness in the space revolves around the Snowflake architectures; shared data architectures that provide a central data repository (similar to shared-disk models) as well as compute clusters that store data portions within nodes (similar to shared-nothing models). The result is an innovative take towards scalable yet simple data management, being further specialized by its three layers: cloud storage, compute resources and query processing, and cloud services.

The separation of layers allows Snowflake to achieve a variety of things: running a seemingly unlimited number of concurrent workloads based on the same data, enabling simultaneous query execution, and hosting independent auto scaling and auto suspend features are just a few of the actions. 

On top of its acclaim for hosting extremely fast data querying, Snowflake is also extremely user friendly. The service caters to users without a technical background through its offer of business intelligence (BI) and data visualization tools, integrating seamlessly with other tools and platforms of the same realm. In addition, Snowflake hosts stronger data sharing capabilities, breaking down data silos for both internal and external teams. Despite an offering of AI tools, the data warehouse itself is reportedly easy to use, with users finding that Snowflake is ready to use right out of the box.

This scalable data storage and querying also come in handy when analyzing and reporting on data, with Snowflake highly emphasizing the growth of business intelligence (BI) through its various tools and documentation.

Companies like Capital One, Bumble, and Siemens have chosen the data warehousing service as their main data driver, and for good reason too. Promoting a traditional data warehouse build as a scalable, less labor-intensive SaaS, Snowflake is extremely valuable in the data storage and analytics fields. Scalable data storage is one thing, but a cost-effective solution with strong security certification is another- Snowflake provides both. 

Databricks

On the other side of Snowflake’s data warehouse is Databricks, a data lake house analytics platform excelling in the “building, deploying, sharing, and maintaining” of data, analytics, and artificial intelligence. Easily integrated into any cloud provider, Databricks provides tools to better service your data within a single interface, offering tooling and capabilities like:

• Dashboards and visualizations
• Machine learning (ML) modeling
• Generative AI solutions
• Data ETL processing
• Data engineering 

And so on!

Databricks’ data lake house prides itself on their three core values: unification, open source, and scalability. Firstly, Databricks provides a unified, single pane of glass interface that hosts all of your data processes as well as AI capabilities, letting users interact with their data without having to jump between platforms and tools. Secondly, Databricks and their lake house are based on open source foundations, allowing for community developments and collaboration without the bounds of proprietary licenses. This open source approach injects tons of customizability into Databricks’ most revered products and tools, letting developers create their own unique approach to data. Thirdly, Databricks is extremely scalable, ghosting automatic optimization capabilities amongst its other features. 

One point to note is how Databricks utilizes its artificial intelligence advancements, Databricks uses their generative AI to better understand what makes your data unique, providing optimization automations to push for performance while adjusting their infrastructure to mold to your needs. Something exciting within Databricks’ AI realm is their recent release of DBRX, Databricks’ large language model (LLM). Surpassing the capabilities of GPT 3.5 and competing with Gemini 1.0 Pro, DBRX was built to foster open source community advancements of LLMs, as well as provide enterprises the opportunity to build their own LLMs.

Companies like AT&T, Rivian, and Jetblue have found immense value using Databricks because of its unique data lake house take. Combining the cost effectiveness of a data lake with the performance of a data warehouse, Databricks provides tons of utility across different disciplines and use cases. From pushing our interactions with AI/ML, to evolving our BI interactions by allowing data scientists to create fruitful data science environments and workspaces and how we visualize data, to progressing data analytics further, Databricks represents a unique take on data interactions, incorporating much needed flexibility to the space.

Snowflake vs Databricks

With the base of these solutions being completely different in themselves, Snowflake and Databricks differ on a variety of aspects, from architecture and scaling, to use cases and data structure. Although the differences are glaring, Snowflake and Databricks serve their own purposes and find themselves as top competitors in their niche markets- here are just a few of the ways these solutions differ from one another:

Architecture and Scaling

Snowflake’s architecture combines the models of shared-disk and shared-nothing architectures, creating a hybrid architecture that provides a central data repository while utilizing compute clusters. Within this architecture are three separate layers: storage, compute, and cloud services, all of which perform certain tasks in data processing. The separate storage and compute layers, in particular, provide tons of flexibility within Snowflake, allowing for independent auto scaling and auto suspend actions in clusters.

Databricks’ serverless architecture, on the other hand, offers a different approach on whether you’re operating from a cloud provider or not. For the Databricks platform, the architecture is composed of three layers:

• Delta Lake: Databricks’ storage layer
• Delta Engine: Databricks’ query engine and processing layer
• Built-in Tools: Data tools for customers

Adding Databricks on top of a cloud provider makes the platform consists of two layers - a control plane and compute plane. Databricks customers are able to manage the backend through their control plane, while data computation and handling are operated within the compute plane, usually housed within a cloud provider such as AWS and Azure.

Both architectures sound pretty similar for the most part: layered architectures, separation between storage and compute, etc. Where the difference applies is with regards to scaling power.

In itself, Snowflake is built for performance, with the automatic scaling of storage and compute resources independently pushing that message. Scaling is as efficient and beginner-friendly as ever with Snowflake, though there are restraints to this process. Snowflake clusters are limited to 128 nodes maximum, as well as limited to fixed data warehouse sizes. The solution also can also be run on three major cloud platforms: AWS, GCP, and Azure.

Like Snowflake, Databricks enjoys the benefits of auto scaling on independent resources and layers, letting the efficiency speak for itself. While Databricks does have a technical learning curve, the platform offers a higher degree of customization and flexibility when it comes to nodes and clusters, limited only by infrastructure and costs.

Data Structures and Servicing

In the aforementioned section, we noted the distinction between data lakes and data warehouses and the type of data they’re able to service.

Snowflake, the data warehouse proponent, services only structured or semi structured data, providing high performing processing and querying at the expense of data flexibility. Databricks, the data lake house representative, is able to service all data types, providing a cost effective and flexible solution for companies that don’t need to service data urgently.

Where this really matters is with regards to how these solutions use your data in their core competencies.

The structured data of Snowflake translates well into their use cases of data storage, data reporting, and data analytics. Offering a user-friendly approach to data analytics, structured data as stored by Snowflake is easily able to be translated for data reporting and business intelligence reasons, with restructuring and scaling being possible as you see fit. Working with semi and structured data also emphasizes Snowflake’s mission of data efficiency, letting anyone work with the data at hand regardless of technical experience.

All forms of data certainly have its merits when it comes to Databricks operations. Notable use cases of the data platform include AI/ML, big data analytics, data exploration, data security and governance, and so on. The possibilities are seemingly endless when it comes to Databricks, which is why they support all data types, from structured to unstructured. Processes that use tons of data like AI/ML find value in unstructured data, letting Databricks turn something that might otherwise be unfeasible into something understandable and storable. The only constraint within working with all types of data is the degree of technical expertise recommended, making Databricks not exactly beginner friendly. 

Similarities Between Snowflake and Databricks

The two services host a plethora of differences outside of the aforementioned ones, however, they also share certain aspects.

For one, the preferred pricing model of both Snowflake and Databricks is pay by usage, letting users customize their approach and scale their allocation up and down accordingly. Both solutions use SQL as their query interface, as well, though Databricks is able to use Spark Dataframe and Koalas on top of SQL. Snowflake and Databricks also have a slight reliance on cloud platforms: Snowflake’s architecture is deployed and managed on said platform while Databricks similarly integrates with cloud platforms to strengthen their data offerings.

Conclusions

Whether you’re looking for extremely fast data processing and a structured environment, or a customizable data experience that offers you strong competencies in multiple cutting-edge practices, both Snowflake and Databricks will be able to meet your needs.

Snowflake represents a beginner-friendly approach to data storage and processing, offering solutions to service your structured and semi-structured data while offering an environment catered to your support. On the other hand, Databricks provides an experience geared towards those possessing technical skills, rewarding users with extremely customizable solutions that are easily integrable to any cloud provider and competent in spaces like AI/ML and big data. 

If you’re looking for a data solution that combines the best of both Snowflake and Databricks, check out Lyrid Object Storage and Lyrid Managed Databases. Providing capabilities such as data backups and replication, role-based access control, serverless data management, and more. Our data and storage options excel in easy data storage and accessible sharing, using content delivery networks (CDNs) for fast data querying that’s cost effective. Managing unstructured data, including content assets and storage, has never been easier and more tailored to you: you’re able to manage seamless client connections, data center hosting locations, database analytics, and more within a single platform. The best part is, our solutions are designed to provide the customizability of Databricks with the ease-of-use of Snowflake, emulating the performance of both titans to give you an optimized yet headache-free data experience- all with an effective cost.

To learn more about how our solutions can benefit your business needs, book a call with one of our product specialists!