Databricks vs Snowflake: Which platform is best for you?
As more and more companies turn to the cloud for their data processing needs, choosing the right platform can be a crucial decision. Two of the most popular cloud-based data platforms are Snowflake and Databricks, and understanding the differences between them can be challenging. However, by closely examining the features and advantages of each platform, you can make an informed decision about which one suits your business best. In this article, we’ll explore the key differences between Databricks and Snowflake, and help you decide which platform is right for your data processing needs.
What is Snowflake?
Snowflake is a comprehensive, fully-managed software as a service (SaaS) platform that offers a unified solution for a variety of data-related tasks such as data warehousing, data lakes, data engineering, data science, and data application development, as well as secure sharing and consumption of real-time or shared data. It provides a range of out-of-the-box features like separation of storage and computes, on-the-fly scalable computing, data sharing, data cloning, and third-party tool support to meet the diverse needs of growing enterprises.
Read more: Snowflake Architecture
Snowflake is a self-managed service which means:
- No virtual or physical hardware to select, install, configure, or manage.
- No software to install.
- Snowflake handles maintenance, scale-up/scale-down, and tuning.
Snowflake runs completely on cloud infrastructure and uses virtual compute instances for its compute needs and storage service for persistent storage of data.
Advantages of Snowflake:
- Significant investment in an ecosystem rich with partnerships and integrations for ongoing extensibility potential.
- The fixed pricing model for predictable costs.
- Simplified administration tasks.
- It is a good Data Warehouse.
Disadvantages of Snowflake:
- It does not provide direct support for all AI/Ml use cases and using third-party apps to augment it can decrease the ease of configuration and management of the required functionality.
- Snowflake is not best suited for streaming and real-time use cases.
- Out-of-the-box administration functionality cannot always be modified or fine-tuned for specific needs.
- Performance issues may arise when dealing with large data volumes.
- Snowflake has Proprietary technology and does not support Open-source based technology. Once the customer chooses it, he is locked into the snowflake Proprietary technology without much flexibility.
- Snowflakes incur heavy costs for processing the data.
Read More: Snowflake Query Optimization
What is Databricks?
Databricks is a cloud-based platform that helps you to process, transform and make available huge amounts of data to multiple user personas for many use cases, including BI, Data Warehousing, Data Engineering, Data Streaming, Data Science, and ML. It is also a one-stop product for all data requirements such as storage and analysis.
People use Databricks to process, store, clean, share, analyze, model, and monetize the database with solutions from BI to machine learning. Also, Use the Databricks platform to build and deploy data engineering workflows, analytics dashboards, and more.
Advantages of Databricks:
- Databricks is built on top of an open-source Apache spark framework so no vendor lock-in.
- Databricks allow for the analysis of structured, unstructured, and semi-structured data; it works with batch or streaming use cases.
- The original Lakehouse platform delivers the best of both Data Lakes and Data Warehouses. Otherwise, you need to invest in two different platforms.
- Databricks has support for advanced AI capabilities including machine learning, Data science, and serverless model serving. Databricks supports serving the model via Kubernetes or other model-serving platforms as well.
Disadvantages of Databricks:
- Databricks require a certain level of technical expertise and familiarity with Spark and other big data tools even though Databricks supports ANSI-based SQL which is used widely in the industry.
- You need back up your data files or use Databricks Repos to save them in a project folder.
Read More: How to Boost Databricks Performance for Maximum Results
Databricks vs Snowflake. Which is best for Data Analysis?
In the above section, you came to know what are Snowflake and Databricks. You have also learned how they both work. Both are cloud-based platforms that are used to manage the data, but that doesn’t mean they are the same. They are different. So, let’s see how they are different from each other based on these categories.
1. AI/ML and Data Science
Here we will compare both platforms for their AI/ML capabilities.
|ML Training Options||1. Built-in Model serving capabilities with MLflow integration for MLOps services.|
2. Single and multi-node options for training.
3. Supports SparkML or Horovod versions of learning libraries. 4. Full integration with MLflow for end-to-end machine learning lifecycle management. 5. Built-in tools and libraries for data preprocessing, feature engineering, model training, and evaluation. 6. Built-in support for deep learning workloads with tools and libraries such as TensorFlow and Keras.
|1. Two methods for training:
a. Snowflake computing is limited to a single node
b.Third-party computing using platforms such as Sagemaker, Dataiku, Databricks
2.MLOps services must be found externally. 3. Limited integration with MLflow. 4. Requires use of third-party tools and libraries for ML, AI and deep learning.
|ML Serving Options||1. Natively integrated MLflow for model serving.|
2. Supports batch inference (UDF to wrap your model prediction into your ETL pipelines), and serverless API endpoint configurations for realtime serving.
|1. Two methods for model serving: In-Platform Serving and Model API a. In-Platform Serving supports loading models from the Snowflake staging area or external model registry.
b.Model API supports hosting models in inference clusters outside of the Snowflake environment.
2. Snowpark UDFs can be written to call the trained model's API and send data for inference
|Compute environment||Multi-node by default.||Single-node recommended, external compute required for multi-node workloads and workloads requiring specific libraries.|
|Library installation||The user fully controls library installation.||Limited installation options within the warehouse.|
|Scalability||Easy to scale to single-node or Multi-node.||Limited scalability for single-node workflows and limited multi-node capability requiring external compute integration.|
|Data Flow||All data flow, data transformation, model training, and model serving occur within Databricks’ computing environment.||Data may flow outside the Snowflake warehouse during training and across cloud vendors and geographical regions introducing risks.|
|Workload performance||Succeeds in all use cases.||Fails for tabular data over 1M rows and workloads requiring specific libraries or multi-node capability.|
|Cost||Cheaper and more dependable for single-node and multi-node workflow.||Requires external compute integration and may be more expensive for multi-node workflows. It has memory and time limits and it is known to be less dependable.|
|Ease of use||Workspace set up to mimic Jupyter notebooks, natively supported MLOps tools like MLFlow.Easier, intuitive workspace and natively supported tools.||No native MLOps tools may require external compute integration, and limitations on user control. Not as easy, with limited MLOps tools and a less intuitive workspace.|
|Training speed for single-node workflows||Faster, becomes more pronounced with larger datasets.||Slower, especially for larger datasets.|
|Deployment and maintenance||Simpler, with only one compute cluster to manage.||More complex, and requires external computing integration.|
|Large Language Model Support||YES (Dolly and MLflow 2.3)||NO|
2. Use Case support
Let’s see what use cases are supported by both platforms:
|Data Processing||Databricks is built on top of Apache Spark, which provides a powerful engine for data processing workloads. Databricks can handle large-scale data processing tasks, including ETL, data cleaning, and data transformation.||Snowflake also provides support for data processing workloads, but its focus is primarily on data warehousing and analytics. Snowflake's cloud-based architecture provides a highly scalable solution for processing large volumes of data.|
|Data Engineering||Databricks provides support for data engineering workloads, including building data pipelines and managing data workflows. Databricks makes it easy to build scalable data engineering solutions using Apache Spark.||Snowflake provides a fully-managed solution for data warehousing and data engineering workloads. Snowflake's architecture provides a scalable solution for building data pipelines and managing data workflows.|
|Machine Learning||Databricks is designed for machine learning workloads and provides a collaborative workspace for data scientists to build and deploy machine learning models. Databricks provides built-in support for machine learning frameworks like TensorFlow, PyTorch, and Scikit-learn.||Snowflake does not provide built-in support for machine learning, but it can be used in conjunction with machine learning platforms like Databricks or SageMaker. Snowflake's data warehousing capabilities make it easy to store and analyze large volumes of data for use in machine learning models.|
|Data Warehousing||While Databricks can be used for data warehousing, it is primarily designed for data processing and machine learning workloads.||Snowflake provides a fully-managed solution for data warehousing and analytics. Its cloud-based architecture provides a highly scalable solution for storing and analyzing large volumes of data.|
|Real-time Analytics||Databricks provides support for real-time analytics using Apache Spark's streaming capabilities.||Snowflake does not provide built-in support for real-time analytics, but it can be used in conjunction with real-time streaming platforms like Kafka or Kinesis.|
|Collaboration||Databricks provides a collaborative workspace for teams to work together on data processing and machine learning projects. It includes features like shared notebooks, version control, and project management.||Snowflake does not provide built-in collaboration features, but it can be used in conjunction with collaboration platforms like Slack or Jira.|
Snowflake use cases are limited to core Data Warehouse and BI use cases while Databricks use case cases range from Data warehouse /BI to Data Engineering and encompass Data Science /Machine learning/Artificial intelligence use cases.
Frequently Asked Questions (FAQs)
Here, you will find answers to some of the most commonly asked questions about these cutting-edge platforms.
Question 1: What is the difference between Databricks Lakehouse and Snowflake in terms of their platform architecture and support for machine learning and artificial intelligence workloads?
Answer. Databricks Lakehouse is built on top of Apache Spark, providing a more flexible and scalable architecture, and has built-in tools and libraries for machine learning and artificial intelligence workloads. Snowflake, on the other hand, is a cloud-based data warehousing platform that requires third-party tools and libraries to support these AI/ ML and data science workloads.
Question 2: What program language do Databricks and Snowflake support?
Answer: Both platforms support SQL language so if the developer is familiar with SQL it does not take more time to catch up with both platforms. Databricks provides multi-language support (Java, Scala, R, Python, and SQL). Snowflake also supports Python, spark, and Scala but it does not support notebooks where multiple users can edit and work collaboratively at the same time.
Question 3: How do Databricks and Snowflake compare in terms of security features?
Answer: Both Databricks and Snowflake offer robust security features such as encryption and access control. However, Databricks offers more control by providing the ability to deploy the databricks cluster inside Customer provisioned VNet ( this concept is called VNet injection). Please refer to my blog on vNET Injection.
Question 4: Can Databricks be used with Snowflake?
Answer: Yes, Databricks can be used with Snowflake. Databricks provides native integration with Snowflake, allowing users to easily connect to Snowflake and run SQL queries against Snowflake data. This makes it easy to use Databricks for data processing and machine learning workloads and Snowflake for data warehousing and data engineering workloads.
Question 5: Which platform is better for machine learning?
Answer: Databricks is designed for both data engineering and machine learning workloads and it provides a collaborative workspace for data scientists to build and deploy machine learning models. Snowflake, on the other hand, is primarily a data warehousing platform and does not provide built-in support for machine learning.
Question 6: Which platform is more cost-effective?
Answer: The cost of using Databricks and Snowflake will depend on a number of factors, including the size of your data, the number of users, and the specific features and services you require. Both platforms offer pricing models based on usage, so the cost will vary depending on your specific needs. If we perform apple to apple comparison it was observed that the cost of Snowflakes grows exponentially as compared to databricks. Databricks is a more cost-effective choice.
Question 7: Which platform supports lakehouse architecture based on open standards as we do not want to stick to vendor lock-in?
Answer: Snowflake provides external tables, which are read-only copies of data residing in the data lake which violates the basic principle of lakehouse so it may not be considered true lakehouse architecture as it only allows one-way access to the data. Databricks Lakehouse platform is based on open standards, with no vendor lock-in. You can sercurly read from and write into the external locations.
The Databricks Lakehouse Architecture is undoubtedly a winning architecture for modern data platforms. It provides enterprises with a unified platform that enables them to securely access all their data for all use cases. This architecture is designed to simplify the management and processing of large and complex data sets while reducing costs and increasing efficiency. It offers a robust data processing and machine learning platform with a powerful collaborative environment and support for various data sources. With the Databricks Lakehouse Architecture, organizations can benefit from powerful data analytics capabilities, including machine learning and artificial intelligence, and gain insights that can drive business growth and innovation. Databricks supports machine learning and Data engineering use cases under one platform which makes it the best for both worlds. Overall, the Databricks Lakehouse Architecture is a game-changing solution that will continue to revolutionize the world of data management and analytics.
very useful and insightful article. clear and crisp explanations about the use cases…. thanks a lot – Babu Reddy