Databricks vs Snowflake A Side By Side Comparison
Databricks vs Snowflake are two of the most popular data cloud platforms of today. They started by tackling two fairly different use cases: Snowflake, a SQL data warehouse, and Databricks, a managed Apache Spark service. They were even partners in the beginning! And they are now both multifaceted data cloud systems that can service a tremendous variety of use cases, so they are direct rivals.
Stories of Origin
Snowflake vs DataBricks started as partners, but each focused on one aspect of data management. Snowflake had focused on data warehousing, whereas DataBricks had found itself in managed Spark and quickly branched out into ML tasks. They are even interested in the fact that they used to recommend each other to clients.
Both systems have undergone considerable change by today’s standards. While as of 27 February 2024, DataBricks refers to itself as the “data intelligence platform”, and Snowflake is known as the “data cloud” on the company’s Websites.
Ultimately, they are both complete, all-in-one platforms for data clouds that support a wide range of data use cases.
Even still, learning about their beginnings is still fascinating since it clarifies the relative advantages and disadvantages of each platform in the modern era.
Data warehousing specialists from Oracle and VectorWise, another data warehousing business, established Snowflake in 2012. Snowflake’s primary data warehousing solution, which they frequently called the “elastic data warehouse” because of its distinctive design that enabled it to grow computation and storage independently, was introduced to the market ten years ago in 2014.
Databricks introduced their first solution in a new market shortly after Snowflake. The founders of Apache Spark, who were all Berkeley professors working in the high-performance computing research field, formed Databricks. A managed version of Apache Spark and a notebook interface for interactively running operations in these computer clusters were their first offerings.
In 2017, Snowflake began to grow by adding data-sharing features, and in 2019, it launched a marketplace where users could purchase datasets from one another.
By introducing their managed MLFlow product in 2019 and the MLFlow model in 2020, Databricks began delving deeper into the ML field around the same year.
Evolution of the Company
The observance of each business’s challenge for consumer requests, and their responses with sets of functionality, is fascinating.
Snowflake’s Snowpark came out of the box to enable moving Spark workloads but then morphed into a platform for executing the same machine-learning set of tasks using Python. They’ve also made massive investments in integrating Apache Iceberg support so their clients can just manage and use their data lakes on Snowflake.
In the meantime, DataBricks made a push into the data warehousing space with the release of Photon and DataBricks SQL.
It’s especially apparent now when looking at the interfaces for how to build a “SQL warehouse” in Databricks and a virtual warehouse in Snowflake. As you can see, Databricks has essentially replicated the layout and configuration of Snowflake’s virtual warehouses:
Every Platform’s Benefits and Important Differences
Knowing each company’s history is crucial since it clarifies the relative advantages and disadvantages of each platform.
Snowflake offers a significantly more robust and feature-rich SQL data warehousing offering because of its origins in data warehousing. Since the majority of the value produced by data strategies will come from a well-managed data warehouse that can support essential business intelligence use cases, this will be the most significant and often utilized component for the majority of businesses.
Very few businesses use Databricks like a “data warehouse.” Instead, they count on Databricks because of the robust support for data science workloads and robust Python notebooks for it. Probably the first data transformation choice for big business businesses employing a large number of technical information engineers who should work with Python and Apache Spark. One benefit to Databricks for use cases with ETL is the adaptability and customization of Spark. It’s sometimes preferable to work with Spark for analytical work that works on huge datasets, as it enables us to tweak more settings and for the operation to go faster. In my experience, if the compute side cost benefits of these data pipelines aren’t a significant amount, then the human cost of maintaining and creating them will outweigh them and usually makes sense as a consideration only for workloads spending more than tens of thousands of dollars a year.
One of the main ways it differs from Snowflake in terms of future roadmaps and product evolution is its emphasis on platforms. Customers may now execute containerized apps on Snowflake thanks to Snowpark Container Services, which was launched by Snowflake in late 2023. When combined with their in-house application marketplace, Snowflake is undoubtedly preparing for a time when partners and customers will be able to run any kind of data application within Snowflake.
For each use case, Databricks seems to be pursuing a managed solution one chance-in-a-box approach. Two obvious examples of this are their data catalog and dashboard features. Most Snowflake customers will then purchase an external BI/dashboarding product to use with Snowflake. They will additionally buy another data catalog solution to handle and monitor all of their datasets. Read between the lines, it’s obvious that Databricks wants to break and do away with the need for users to buy additional tools. In 2020, they acquired Redash which they subsequently transformed to a powerful, innovative dashboard solution. At the same time, they are doing some serious work on Unity Catalog and intend to displace third-party data catalog suppliers.
Use Cases and Comparing Key Features
A data cloud platform’s primary use cases were covered in the webinar, after which we enumerated the capabilities offered by Databricks and Snowflake for each use case. The main use cases that were discussed included:
- Ingestion of Data
- Transformations of Data
- Reporting and Analysis
- ML/AI
- Applications of Data
- The marketplace
- Data Management & Governance
Data Intake
Data must initially be loaded, or “exposed,” to the underlying system to be interacted with. To put the data into a database that Snowflake can query, you often need to use a COPY INTO command. Additionally, Snowflake provides tools like Snowpipe, which loads data into Snowflake automatically.
The majority of Snowflake users will additionally usually load data into Snowflake from a variety of sources (such as application databases, external APIs, etc.) using a third-party solution like Fivetran, Stitch, or Airbyte.
Instead, the majority of clients use Databricks to work directly with the data in cloud storage. However, the idea behind managed volumes is similar to that of snowflake tables, in which Databricks takes care of the table management.
Similar to the Databricks concept, more customers will put their data in cloud storage and interact with it there thanks to Snowflake’s investments in supporting Apache Iceberg.
Data Conversions
But you often want to change or improve your data as it’s available on the cloud platform. On both platforms, it is possible to do it in several ways.
However, since Snowflake is an SQL-based data warehouse, most users depend on tasks, stored procedures, or third-party transformation and orchestration tools such as DBT to do their data transformations in pure SQL. All SQL workloads land on Virtual Warehouses under the Snowflake schema.
However, most Databricks users run Jobs to send a Spark task to a cluster running in cloud computing instances. Databrick’s recent progress in its serverless SQL warehousing offering means SQL data transformations done completely in pure SQL – using something like dbt – are becoming more prevalent.
Evaluation and Reporting
Neither Snowflake nor Databricks offer a variety of tools to their users for analysis and reporting. Lightweight dashboards may be made using Snowflake directly in Snowsight, or you can use Streamlit to construct bespoke data applications.
Instead of a third-party BI solution, several businesses employ Databricks’ excellent dashboarding offering.
ML/AI
Both businesses are making significant investments in ML and AI capabilities, as was previously noted. Databricks includes several more advanced machine learning tools, such as managed MLflow and Model Serving, as a result of its prior emphasis on this.
You expect a large number of Snowflake customers to be able to start hosting machine learning models directly within Snowflake as soon as Snowpark Container Services is live.
Data-Based Applications
It’s also interesting to think of building “data applications” as a way to contrast Snowflake with Databricks. This is an inherently ambiguous term, and I will explain what a “data application” is: a feature or product used to provide real-time data or insights to consumers that are not inside of the organization. It is not an application that the business is internally using.
The marketplace
As a client, you frequently wish to purchase extra datasets or apps for your data cloud platform. The obvious winner in this case is Snowflake, which has a well-developed marketplace with native apps and datasets that you can use right within your Snowflake account.
Management and Governance of Data
It also has out-of-the-box functionality for both governance and administration, of both systems.
All users can access hundreds of free metadata datasets in the Snowflake account use database. Their extremely sophisticated cost management suite, while including budgets and resource monitors, is merely two of its strong aspects. Just announced is Snowflake Horizon, a new crop of features to help you manage your data assets and humans.
Databricks is offering a powerful data catalog with its Unity Catalog solution to help users manage and understand all of the data in their environment. Databricks is way behind in terms of cost management as well, and only just recently has this data become available in system tables, so their equivalent to Snowflake’s account use views made available.
Cost and Price
Both Snowflake and Databricks offer usage-based pricing (you only pay for usage), so if you cap it out at a certain scale it should cost you less than running Oracle. Here is our post where we explain how Snowflake’s pricing works. Databrick’s price details are available on their website and you may see them. One crucial aspect of Databrick’s price is that there are two different sets of fees:
- Specifically, the platform fees and overhead from Databricks.
- Underlying the cloud expenses of AWS, Azure, and GCP are the servers that Databricks spins up in those accounts.
Costs can rapidly increase if they are not properly managed or tracked, just as with any usage-based cloud platform.
Is Snowflake more expensive than Databricks?
Many people frequently inquire as to whether Databricks is more affordable than Snowflake, in part because to Databricks’ intensive marketing campaign, as is seen on their website below:
There are two crucial elements to take into account when calculating the cost of any data process or application:
- The platform is not free. The amount you pay to your cloud provider, Snowflake, or Databricks.
- The expenses to people. The money you pay your staff to develop and maintain the processes and apps they produce.
Databricks claims that Snowflake offers ETL workloads at a significantly lower cost than Databricks. The fact that Spark tasks are very adjustable makes up this assertion. It takes engineers days (or weeks) to play with a massive variety of factors.
All of this effort has human expenses associated with the effort, and that’s the part that Databrick’s marketing doesn’t consider when drawing those comparisons. In some cases, paying engineers to experiment with task optimization and tuning may be reasonable, but in most cases, the costs of paying human overhead will be so much more expensive than what ETL workloads cost that employing humans will add insult to injury.
When deciding on or comparing a platform’s prices, make sure you consider the overall cost of ownership from (a) the platform provider and (b) those who are doing the task.
Conclusion
Overall, Databrick and Snowflake provide excellent solutions for modern data analytics with their mounds of strengths. With its integration with machine learning and real-time data processing, Databricks shines through its big data real-time data processing ability perfect for workload teams that want to harness big data to do advanced analytics. However, whereas Snowflake offers excellent data-sharing capabilities at a price point that is easily accessible by organizations focused on simple data warehousing and collaboration, those capabilities come with a service stitched on that is otherwise largely unattractive.
In the end, you will need to choose whether or not Databricks or Snowflake works best for your business, data strategy, and the skill sets of your team. When thinking about these platforms, see what features they can best support your goals, and see how they can bring synergies that create innovation in your data practices. Technology and strategic vision often come together to create strengths that, when made wisely, enable your data journey.
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