Snowflake vs Vertica: What are the differences?

Introduction:

Snowflake and Vertica are both popular cloud-based data warehouses that are designed to handle large volumes of data and perform analytics at scale. While both platforms offer similar functionalities, there are key differences that set them apart from each other.

1. Architecture: Snowflake is built on a multi-cluster shared data architecture, which means it separates compute and storage resources. This allows users to scale compute and storage independently and provides more flexibility in managing workloads. On the other hand, Vertica follows a shared nothing architecture, where each node in the cluster has its own storage and computing resources. This architecture offers high parallelism and performance for many workloads.

2. Scalability: Snowflake provides virtually unlimited scalability as it can automatically scale up or down compute resources based on the workload demand. It also supports automatic data distribution and parallel query execution across multiple clusters. Vertica, on the other hand, allows scaling up by adding more nodes to the cluster but does not support automatic scaling. It requires manual intervention to add or remove nodes as per workload requirements.

3. Query Execution: Snowflake uses a unique approach called query optimization layer, which compiles queries into an optimized execution plan based on statistics and metadata. It performs optimizations such as predicate filtering, join reordering, and materialized view selection to enhance query performance. Vertica, on the other hand, uses a query optimizer that pushes computation down to storage nodes and utilizes techniques like columnar storage and projection to improve query execution.

4. Concurrency: Snowflake offers built-in support for high concurrency workloads. It uses multi-cluster shared data architecture to handle thousands of concurrent queries without performance degradation. Vertica also supports concurrent queries but has limitations on the number of concurrent queries that can be executed at the same time depending on the cluster configuration.

5. Data Loading: Snowflake provides various options for data loading, including bulk loading, streaming, and external data sources. It allows parallel loading from multiple sources, enabling efficient data ingestion. Vertica also supports bulk loading and streaming, but it lacks built-in support for external data sources. Data loading in Vertica may require additional steps and custom solutions for integrating with external data sources.

6. Data Types and Functions: Snowflake offers a comprehensive set of built-in data types and functions for various data processing and analytics tasks, including support for semi-structured data types like JSON and variant. Vertica also provides a wide range of data types and functions but may have some limitations in handling semi-structured data types compared to Snowflake.

In summary, Snowflake and Vertica differ in their architecture, scalability capabilities, query execution approaches, concurrency handling, data loading options, and support for data types and functions. These differences should be considered when choosing a data warehouse platform that best aligns with specific requirements and business needs.