Databricks vs Snowflake: Which Platform Excels in Data Engineering?

In the world of big data and analytics, two platforms have emerged as front runners: Databricks and Snowflake. These powerhouses are transforming how businesses handle, process, and gain insights from vast amounts of data. The debate of Databricks vs Snowflake has become a hot topic among data professionals, as both platforms offer unique strengths in areas such as machine learning, ETL pipelines, and overall data engineering capabilities.

As organizations strive to make data-driven decisions, choosing the right platform is crucial. This article delves into the key differences between Databricks and Snowflake, examining their architectures, performance, scalability, and data engineering features. By comparing these aspects, readers will gain a clear understanding of which platform might be better suited to their specific needs, whether they’re looking to implement advanced analytics, streamline data workflows, or build robust data infrastructures.

Architecture and Data Handling

Data Lake vs Data Warehouse

Databricks and Snowflake offer distinct approaches to data architecture, each with its own strengths. Databricks is built on a data lake architecture, which allows for storing vast amounts of raw, unprocessed data in its native format 1. This approach provides flexibility and scalability, making it ideal for organizations dealing with large volumes of diverse data types.

Snowflake, on the other hand, combines elements of both shared disk and shared nothing architectures 2. Its storage layer uses centralized cloud storage, while the compute layer employs independent Virtual Warehouses for parallel query processing. This hybrid approach enables Snowflake to offer the benefits of a traditional data warehouse with the scalability of cloud computing.

Support for Structured and Unstructured Data

Both platforms have evolved to support various data types, but their approaches differ. Databricks, with its data lake foundation, excels in handling unstructured data. It allows users to store and access all data types in their original format, including text, images, and video 3. This capability is particularly valuable for organizations dealing with diverse data sources and formats.

Snowflake also supports structured and semi-structured data, but it transforms the data into its native format upon ingestion 4. While this approach ensures consistency, it may require additional processing for unstructured data. Snowflake has been working to improve its unstructured data capabilities, with the introduction of the Snowpark API in 2022 4.

Real-time Data Processing Capabilities

In terms of real-time data processing, Databricks has a slight edge. Built on Apache Spark, Databricks offers robust streaming capabilities, allowing for continuous data processing 1. This makes it well-suited for applications requiring real-time analytics and decision-making.

Snowflake, while primarily designed for batch processing, has been enhancing its real-time capabilities. Its architecture allows for rapid query processing, which can support near real-time analytics in many scenarios 2.

Both platforms continue to innovate and improve their capabilities, pushing the boundaries of data warehouse technology. The choice between Databricks and Snowflake ultimately depends on an organization’s specific needs, data types, and processing requirements.

Performance and Scalability

Query Performance Benchmarks

Both Databricks and Snowflake have demonstrated impressive query performance in various benchmarks. Databricks has shown a 2–4x acceleration of SparkSQL for deployments and claims up to 60x performance improvements for specific queries 4. The introduction of Delta Engine and Photon, a C++ execution engine, has further boosted Databricks’ performance for large jobs 4.

Snowflake, on the other hand, excels in delivering consistent performance with automatic tuning and optimization 6. Its architecture supports high levels of concurrency without manual intervention, ensuring smooth operation even during peak data loads 6.

In terms of specific benchmarks, Databricks has claimed superiority in the TPC-DS benchmark wars. According to a ZDNet article, the lakehouse architecture of Databricks has proven capable of handling BI workloads effectively, a domain traditionally dominated by data warehouse systems 4.

Handling Large-Scale Data

Both platforms have shown remarkable capabilities in handling large-scale data, but with different strengths. Databricks is well-known for its high performance in managing big data and machine learning workloads 6. According to Gartner, users have successfully run Databricks on extremely challenging workloads, processing up to petabytes of data in their systems 4.

Snowflake’s architecture, which separates storage and compute resources, allows it to scale infinitely without slowing down 7. It has been shown to process up to 60 million rows in under 10 seconds 7. Snowflake’s ability to handle complex queries and data workloads while maintaining performance stability under varying conditions has been consistently praised by users 6.

Cost-Efficiency at Scale

The cost-efficiency of these platforms at scale has been a topic of debate. In a TPC-DS benchmark test, running the workload on Databricks reportedly cost $256 compared to $1,791 on Snowflake 8. However, this claim has been contested, with a user reporting that running the same test on Snowflake consumed only 161 credits, translating to $322 for the Standard Edition 8.

It’s important to note that cost comparisons are complex and depend on various factors:

1. Auto Resume and Auto Suspend: Snowflake’s ability to resume instantly and suspend after 1 minute of idle time, compared to Databricks’ minimum 10-minute auto-suspend, can significantly impact costs 8.
2. Usage Patterns: The cost-efficiency can vary based on how the platforms are used. For example, in extract mode BI tool scenarios, Snowflake could be up to 30% cheaper due to more efficient idle time management 8.
3. Workload Type: Snowflake is often considered more cost-effective for BI (smaller) workloads and dashboard production. For big data (50 GB+) and intense computing, Databricks is reported to scale better in both performance and cost 4.

Both platforms continue to innovate and improve their offerings, with Databricks recently announcing a “serverless SQL” option with faster start-up times 8. As the landscape evolves, organizations need to carefully evaluate their specific needs and usage patterns to determine the most cost-effective solution at scale.

Data Engineering Capabilities

ETL/ELT Processes

Both Databricks and Snowflake offer robust capabilities for ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) processes, but with different approaches. Databricks leverages Apache Spark for big data processing, providing powerful capabilities for batch and stream processing 9. This makes it particularly well-suited for complex ETL jobs that require rapid computations and handling of large-scale data.

Snowflake, on the other hand, uses a SQL-based approach to data processing. Its architecture allows for efficient, on-the-fly query execution without the need for data transformation 9. This makes Snowflake particularly strong in ELT processes, where data is loaded into the platform and then transformed as needed.

Data Transformation Tools

Databricks offers a rich set of tools for data transformation. Its unified workspace supports multiple programming languages, including Python, R, Scala, and SQL, allowing data engineers to use their preferred language for data manipulation 2. The platform’s integration with Delta Lake brings ACID transactions and data quality enforcement to data lakes, enhancing reliability in data transformations 2.

Snowflake’s strength lies in its simplicity and SQL-based approach. It provides native support for semi-structured data formats like JSON, eliminating the need for complex transformations 9. This feature makes Snowflake particularly accessible to users with SQL backgrounds, enabling them to perform sophisticated data transformations without learning new programming languages.

Integration with Other Tools

Both platforms excel in their integration capabilities with other tools in the data ecosystem. Databricks boasts a rich ecosystem with integrations across a wide range of tools and platforms, including BI tools, ETL solutions, and ML frameworks 9. Its support for Delta Lake enhances data reliability and simplifies data pipeline construction.

Snowflake, similarly, has a vast partner network and native integrations with leading data tools 9. It offers a marketplace with products spanning various categories and business needs 10. Snowflake’s Data Clean Rooms feature provides granular control over data access, making it easier to manage data sharing and collaboration 10.

For specific integrations, Databricks provides a Snowflake connector in its runtime, allowing users to read and write data between the two platforms seamlessly 11. This integration enables organizations to leverage the strengths of both platforms, using Databricks for complex transformations and machine learning, and Snowflake for downstream analytics or BI applications 12.

Both platforms continue to evolve their capabilities, with Databricks introducing features like Unity Catalog for unified governance 10, and Snowflake developing Snowpark for containerized application development and deployment 10. These advancements further enhance the data engineering capabilities of both platforms, providing users with increasingly sophisticated tools for managing and processing data at scale.

Conclusion

To wrap up, the comparison between Databricks and Snowflake reveals two powerful platforms with unique strengths in data engineering. Databricks excels in handling unstructured data and real-time processing, making it a strong choice for organizations dealing with diverse data types and requiring immediate insights. Snowflake, with its hybrid architecture, offers impressive performance for structured data analytics and has a user-friendly approach to data transformation.

The choice between these platforms ultimately depends on an organization’s specific needs and data landscape. Both Databricks and Snowflake continue to innovate, pushing the boundaries of what’s possible in data engineering. As the field evolves, organizations should carefully consider their data types, processing requirements, and long-term goals to select the platform that best aligns with their vision for data-driven decision-making.

FAQs

1. Which platform is more effective, Databricks or Snowflake? Snowflake excels in interactive queries and optimizes storage during data ingestion, making it ideal for business intelligence tasks, reports, and dashboard production involving smaller workloads. On the other hand, Databricks is preferable for handling big data and intense computing tasks as it offers faster performance and better scalability in terms of both efficiency and cost.

2. Is it possible for Snowflake and Databricks to be integrated? Yes, Databricks and Snowflake can be integrated. Databricks includes a Snowflake connector within its runtime environment that facilitates both reading from and writing to Snowflake.

3. What makes Databricks a superior choice? Databricks is considered superior due to its comprehensive platform that supports effective data utilization. It features excellent scalability, performance, and integration capabilities with artificial intelligence and machine learning, positioning it as a top choice for organizations aiming to leverage data for business success.

4. Does Databricks qualify as a big data technology? Yes, Databricks is a big data technology. It is built on Apache Spark and offers sophisticated tools for managing and processing large datasets. Databricks is highly suitable for applying big data analytics and machine learning within any organization.

References

[1] — https://www.macrometa.com/event-stream-processing/databricks-vs-snowflake [2] — https://www.chaosgenius.io/blog/snowflake-vs-databricks/ [3] — https://www.thoughtspot.com/data-trends/business-analytics/databricks-vs-snowflake [4] — https://bpcs.com/blog/databricks-vs-snowflake [5] — https://www.graphable.ai/blog/databricks-vs-snowflake/ [6] — https://www.integrate.io/blog/databricks-vs-snowflake-a-comparative-analysis/ [7] — https://synccomputing.com/databricks-vs-snowflake-a-complete-2024-comparison/ [8] — https://www.dhirubhai.net/pulse/databricks-12-times-cheaper-than-snowflake-so-fast-ilan-zaitoun [9] — https://ashamaei.medium.com/data-platforms-comparison-2024-databricks-vs-snowflake-4ec5fe9f9623 [10] — https://www.eweek.com/big-data-and-analytics/snowflake-vs-databricks/ [11] — https://docs.databricks.com/en/connect/external-systems/snowflake.html [12] — https://www.databricks.com/blog/2018/08/27/by-customer-demand-databricks-and-snowflake-integration.html