Modern data platforms now support far more than traditional warehousing, spanning data engineering, analytics, AI and machine learning. Databricks and Snowflake are two of the most widely considered platforms in this space, but they have evolved considerably and are best compared against the workloads, skills and architecture an organisation actually needs.

Databricks and Snowflake are two popular data warehousing service solutions that are being used by companies such as Apple, Disney, and HSBC (Databricks), as well as Microsoft, Amazon, and Google (Snowflake).

Each platform has areas at which it excels, and determining which tool is best for your needs will help you to make the right decision for your business as digital data usage continues to grow and become ever more important in daily operations.

The benefits and drawbacks of Databricks

Databricks was founded in 2013 and combines data warehouses and data lakes into a ‘lakehouse’ architecture. The platform also provides a unified set of tools for the building, development, deployment, sharing, and maintenance of enterprise-grade data at scalable levels.

Pros of Databricks

• Designed to make big data analytics easier for enterprise usage.

• Platform is built around Spark to process large amounts of information in batches and micro-batches to provide near-real-time computation.

• Pre-integrated with other popular data engineering and data science tools.

• Integrations and assets can be accessed from a unified workspace.

• Platform supports SQL, R, Python, and Scala and can be switched between or even used all in the same script.

• Data does not need to be accessed or moved to a propriety system for use (it can be connected to a cloud environment of the user’s choice).

• Offers multi-level data security.

• Comprehensive documentation and knowledge base for troubleshooting.

Cons of Databricks

• Complex setup process and steep initial learning curve.

• Breadth of functionality can create a steeper learning curve, particularly where teams are adopting Databricks across engineering, SQL analytics, streaming and AI workloads rather than for a single use case.

• Costs can be expensive depending on usage and setup.

The benefits and drawbacks of Snowflake

Snowflake was founded in 2012 and launched in 2014, and is a multi-cluster shared data architecture provided as a Software-as-a-Service (SaaS) solution offering a hybrid of traditional shared-disk and shared-nothing database architectures.

The platform is often used for data ingestion, business intelligence and analytics, machine learning, data sharing and collaboration.

The platform can be used for cloud data warehousing services and to analyse the data records in a single location, with automatic scalability (upwards and downwards) for computing resources to load, integrate, and analyse the data.

Pros of Snowflake

• Performance and scalability are enhanced due to separated storage and compute functionality, which allows for unlimited concurrent workloads to be conducted against a single copy of data.

• High levels of data security.

• Faster speeds in queries due to data caching in different compute clusters.

• Micro-partitions allow for data (between 50 to 500mb before compression) to be formed into blocks for improved compression and efficient access.

• Snowflake is relatively easy to learn and use.

• Serverless experience require less management.

• Connective tools and integrations allow for improved access and user experience.

• Backed up with extensive documentation and resources for troubleshooting.

Cons of Snowflake

• Snowflake is a cloud-native service and is not installed as an on-premises platform, so organisations with strict on-premises requirements need to account for that in their architecture.

• Costs for on-demand access and pay-as-you-use can be expensive.

• Cloud-agnostic approach means that although there is no vendor login, you need to source a cloud-service that is appropriate to your needs and works with the platform.

Comparing Databricks and Snowflake

The key similarities between Databricks and Snowflake

Both Databricks and Snowflake address modern analytics, data engineering and AI workloads, and both separate storage from compute so resources can scale independently. Their architectures and operating models differ, so the practical fit depends on how the organisation wants to store, govern, process and consume data.

Both platforms decouple storage and computing options, making them both upwardly and downwardly scalable as required; and both options have dashboards which can be customised (to varying degrees) for reporting and analytic usage.

The key differences between Databricks and Snowflake

• Service type: Databricks is a Platform as a Service (PaaS) which was initially primarily for data scientists and engineers and expanded to cover analysts, and Snowflake is a Software as a Service (SaaS) that is primarily for data analysts.

• Platform focus: Databricks brings data engineering, warehousing, streaming and AI/ML together around a lakehouse architecture. Snowflake provides a managed cloud data platform spanning warehousing, analytics, data engineering, application and AI workloads.

• Scalability options: Both platforms provide elastic compute and managed scaling options. The more important distinction is how workloads are organised, governed and costed across each platform rather than whether either platform can scale.

• Data storage: Databricks works directly with cloud object storage and open table formats such as Delta Lake, while Snowflake manages storage within its cloud platform and also supports structured, semi-structured and unstructured data, including open table formats such as Apache Iceberg. Snowflake accounts can run on AWS, Microsoft Azure or Google Cloud.

• Set-up for data engineering:Databricks makes use of auto-scaling clusters and has a steep learning curve to fine tune the platform. Snowflake utilises an intuitive SQL interface and provides a lot of automation features to facilitate easier usage.

Making an informed choice with your technology partners

It is important to carefully assess the needs of your business, both in the present, and where you expect it to be in the future, and whether you are working with an in-house team who are accustomed to particular programs, languages, and applications – or whether you are bringing in expert third-party assistance to help with your data science and data engineering needs.

Both Databricks and Snowflake are mature platforms with significant overlap. Databricks is particularly strong where organisations want to bring engineering, analytics, streaming and AI workloads together on an open lakehouse foundation, while Snowflake is attractive where a highly managed cloud data platform and SQL-led analytics experience are priorities. The stronger choice depends on workload, architecture, existing skills, governance requirements and cost model.

If you are not sure what platform you should be using, and where you should be taking your storage needs, we are happy to provide you with advice, assistance, and our expert team can support your growing needs as you develop.

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