What is the major challenge enterprises face when working with massive quantities of data? It is the challenge of extensive data silos scattered throughout the organization. Finding and consolidating all this data in a useful way can make a lot of difference. It is no wonder that organizations such as Adobe, Columbia, Shell, Burberry, Bayer, and thousands of other enterprises leverage Databricks as their platform to make data-driven strategic business decisions. All these enterprises, from the computer software giant to the premier outdoor brand, the gas and oil corporation, the luxury fashion house, and the multinational pharmaceutical enterprise, are determined to make the most of their data, whether unstructured, siloed, or structured. In this blog, we will highlight the key benefits of Databricks and also compare them with Snowflake.
Powered by Apache Spark, Databricks is a cloud-agnostic platform focusing on Big Data Analytics and Collaboration. The platform provides an integrated Data Science workspace for Data Scientists, Business Analysts, and Data Engineers. Databricks’ Machine Learning Runtime, controlled ML Flow, and Collaborative Notebooks further enrich the collaborative environment. Thus, a diverse range of enterprise customers use this Databricks platform to run large-scale production operations across various use cases and industries. The expansive list covers healthcare, media, retail, finance, entertainment, and many more.
Why is Databricks important?
Databricks makes using Apache Spark much easier. Rather than dealing with technical complexities, Databricks provides an accessible and user-friendly interface for using Spark. It takes care of the complex setup and management aspects while freeing users to concentrate on working with data and engaging in analytics tasks. Moreover, the collaborative features of Databricks, such as shared notebooks- allow developers to write and run code for data analysis, share it, and work together. It resembles a virtual team room with everyone’s best collaboration that expedites data-driven solutions. (Breaking down the silos!)
Furthermore, Databricks seamlessly integrates with various data sources, from files and databases to data from live streams, and also connects with cloud services and tools. This adaptability is truly powerful as it consolidates all the high-octane technologies (for data science and ML) into a single platform. Thus, Databricks is a uniform platform that is highly flexible, adaptable, scalable, and can connect with anything to process your data.
What about Snowflake?
Another major cloud company, Snowflake, emphasizes data-as-a-service features and functions for its big data operations. The core platform can seamlessly integrate data from various business apps and formats into a unified data store. As a result, it eliminates the typical extract, transform, and load (ETL) processes to achieve desired data integration outcomes. It is also compatible with a range of business workloads- such as AI, ML, data lakes, data warehouses, and cybersecurity. The Snowflake platform is created for organizations dealing with large data volumes that need accurate data governance and management systems.
Databricks vs Snowflake
Key features comparison
Snowflake serves as both a relational database management system and an analytics data warehouse. It supports structured and semi-structured data. Snowflake’s offering comes in the SaaS model, as it uses an SQL database engine to manage how the information is stored in the database. It handles queries against virtual warehouses within the overall warehouse, each housed in its own cluster node, independent of others, and prevents the sharing of compute resources. The cloud services sit on top of that database engine, which performs authentication, infrastructure management, queries, and access controls. Users can analyze and store data using Azure or Amazon S3 resources.
Databricks is also cloud-based but leverages Apache Spark. The management layer, built around Spark’s distributed computing framework, makes infrastructure management much easier. Unlike Snowflake, Databricks is a data lake, not a data warehouse. As a result, it emphasizes streaming, machine learning, and data science analytics. It also comes as a SaaS offering on Azure, AWS, and Google Cloud; Databricks is excellent at handling massive volumes of raw data. It offers a data plane and control plane for backend services, delivering instant computing. Its query engine also achieves high performance through a caching layer.
Snowflake has a storage layer, whereas Databricks utilizes storage on Azure Blob Storage, AWS S3, and Google Cloud Storage.
Verdict: For enterprises seeking robust ELT, data science, and machine features, Databricks is the clear winner. For businesses requiring a good data warehouse, Snowflake suffices.
Databricks vs Snowflake: Comparison of ‘Support and ease of use’
Both Databricks and Snowflake focus on ease of use in specific capacities. Databricks has auto-scaling options for clusters, similar to Snowflake. Databricks SQL Warehouse has a user-friendly solution for its clusters, like Snowflake. Databricks and Snowflake both provide 24/7, online support and have received good praise (in this regard) from their customers.
Verdict: Both are top players with democratized features.
Security features comparison
Databricks and Snowflake both offer role-based access control (RBAC) and automatic encryption. Snowflake enhances security with network isolation and tiered features, with higher tiers incurring additional costs. However, the benefit lies in avoiding payments for unnecessary security features. Databricks also incorporates robust security measures that align with compliance standards such as SOC 2 Type II, ISO 27001, HIPAA, GDPR, and others.
Verdict: This category has no distinct winner, as both platforms prioritize and provide substantial security features.
Integration-wise comparison
While Snowflake is available on the AWS Marketplace, its integration within the AWS ecosystem is not extensive, presenting occasional challenges when pairing with other tools. However, Snowflake excels in integration with specific tools like Apache Spark, IBM Cognos, Tableau, and Qlik, ensuring seamless analysis for users of these platforms.
Both Snowflake and Databricks support structured and semi-structured data, but Databricks offers greater versatility by accommodating any data format, including unstructured data. Although Snowflake is gradually adding support for unstructured data, Databricks is the winner in this category, providing more comprehensive integration capabilities.
Verdict: Databricks is the clear winner.
Artificial Intelligence features comparison
Both Snowflake and Databricks offer expanding portfolios of AI and machine learning (ML) features, embracing generative AI and advanced capabilities. Snowflake introduces Snowpark and Streamlit, providing libraries, runtimes, and APIs for ML training and operations. Streamlit, in public preview, facilitates model development with Snowflake data and Python practices.
Databricks has much more AI integrated across all of its products and services since a long time. It features accessible ML runtime clusters, autoML, MLflow, model monitoring, AI governance, and tools for generative AI and large language models. In the AI arena, Databricks emerges as the preferred choice.
Verdict: Databricks is the winner.
Price comparison
While Databricks is generally priced higher than Snowflake (at around $99 a month with a free version available, Snowflake’s pricing is approximately $40 a month), it is more complex than that. Snowflake separates computing and storage in its pricing structure, offering five editions with tiered prices.
Databricks, with its tiered compute pricing and additional charges for processing units, may be more cost-effective for some users, especially as storage is not included in its pricing. The comparison is nuanced and depends on factors like storage usage frequency and processing needs. We advise users to evaluate their specific data volume, processing, and analysis requirements to determine the most cost-efficient option. The choice between Databricks and Snowflake varies based on individual use cases.
Verdict: It varies from use case to use case.
Conclusion
Snowflake excels in standard data transformation and analysis, particularly for users familiar with SQL. Recently adding support for Python, Java, and Scala, it competes with Databricks but struggles with massive data volumes in streaming workloads. As a data warehouse, Snowflake offers good performance.
Databricks is not just a data warehouse; it is much broader in scope. It has robust capabilities (than Snowflake) for ELT, data science, and machine learning. With managed object storage and a focus on data lakes and processing, it targets data scientists and professional analysts. Databricks is high-end and designed for complex data engineering, ETL, data science, and streaming workloads. On the other hand, Snowflake serves as a production data warehouse for analytics, accessible to beginners and those starting small and scaling gradually. The choice is yours.
How can we help?
If you are an enterprise looking to solve your key data challenges and overcome data silos and leverage the maximum potential of all your data, Databricks is the answer. You can book a call with our experts here at Saxon AI, and we can help you with a holistic approach for seamless implementation.