Snowflake vs Databricks

Pipeline Overview: dbt, Fivetran, PowerBI, and Data Storage

Before we dive into the specifics of Snowflake and Databricks, let’s establish the role of each component in your pipeline:

• Fivetran: Automates data extraction and ingestion from various sources into your chosen data storage platform (Snowflake or Databricks).

• dbt: Performs transformations on raw data stored in the data platform to make it analytics-ready.

• PowerBI: Visualizes the transformed data, generating insights and reports for business decision-making.

In this setup, Snowflake and Databricks act as the foundational layer, storing data that Fivetran ingests and dbt transforms. The efficiency of your entire pipeline depends heavily on the storage platform’s performance, cost, and integration with these tools.

Snowflake as Data Storage

Optimized for SQL Queries and Data Warehousing

Snowflake is designed to efficiently store structured and semi-structured data such as JSON, Avro, and Parquet. Its architecture allows Fivetran to load data seamlessly into Snowflake’s virtual warehouses, where dbt can then run SQL-based transformations.

Since Snowflake scales compute and storage independently, you only pay for the storage capacity and the compute resources you use during data transformations with dbt. For example, Fivetran can continuously load data without requiring dedicated compute, while dbt transformations only consume resources when necessary, optimizing cost efficiency.

Storage Costs in Snowflake

Snowflake offers a pay-as-you-go pricing model, but users can now leverage multi-cluster warehouses to handle large query loads without increasing costs for inactive periods. Additionally, Snowflake’s compaction features help reduce the overall footprint of storage for large-scale workloads.

• Pay-As-You-Go Pricing: Snowflake charges you based on the amount of data stored and the compute used. Storage costs are typically low, at around $23 per terabyte per month, and are further reduced if you use compressed formats like Parquet. However, storage costs can rise depending on the volume of data and the duration for which it is stored.

• Compute Costs: While storage is affordable, Snowflake charges for compute separately. If your pipeline requires frequent dbt transformations, you’ll need to factor in compute costs, which can add up depending on how often transformations run.

With Snowflake, you can optimize storage costs by compressing large datasets, using partitioning, or leveraging Snowflake’s Time Travel feature to reduce the need for maintaining multiple copies of the data.

Seamless Integration with PowerBI

Snowflake’s robust SQL capabilities and fast query response times make it an excellent choice for integrating with PowerBI. Once dbt transforms your data, PowerBI can easily access it through direct queries, giving your users real-time insights with minimal latency.

Databricks as Data Storage

Built for Data Lakes and Flexibility

While Databricks is often associated with big data processing and machine learning, its Delta Lake architecture also makes it a powerful storage solution. Databricks allows you to store structured, semi-structured, and unstructured data in cloud object storage (e.g., AWS S3, Azure Blob Storage), with an added layer of data reliability and governance through Delta Lake.

Databricks’ storage flexibility is beneficial if you’re dealing with a variety of data formats or require the ability to store raw, untransformed data for future use cases. This flexibility can be an advantage for pipelines that require storing large datasets before transforming them with dbt.

Storage Costs in Databricks

uses cloud storage (like AWS S3, Azure Blob), but with Delta Lake’s optimization features like data skipping and Z-order indexing, you can further reduce storage and query costs by organizing data more efficiently for faster retrieval.

• Cloud Object Storage Pricing: Databricks relies on your cloud provider’s object storage (e.g., AWS S3, Azure Data Lake) to store data. This means that Databricks storage costs are closely tied to the storage costs of your chosen cloud provider. For example, storing data in AWS S3 can cost approximately $23 per terabyte per month for standard storage.

• Delta Lake Overhead: Storing data in Delta Lake adds extra benefits like ACID transactions, versioning, and data lineage. While this provides higher reliability, the extra overhead can result in slightly higher costs compared to raw object storage alone.

Databricks storage costs remain relatively low, especially for high volumes of unstructured or semi-structured data, but compute costs for running dbt transformations or querying data in PowerBI can be higher than in Snowflake, particularly for real-time analytics.

Choosing Between Snowflake and Databricks for Storage

Storage Efficiency and Scaling

Both Snowflake and Databricks provide efficient and scalable storage, but they differ in how they handle large volumes and types of data:

• Snowflake is ideal if your pipeline primarily deals with structured data, and you prefer SQL-based transformations. It is highly efficient for storage compression and performance with SQL queries.

• Databricks shines when dealing with unstructured or large-scale data lakes. If you need flexibility in storing raw or complex data types for future analytics or machine learning tasks, Databricks is a good option.

Cost Considerations

• Snowflake storage is generally straightforward, with costs dependent on data size and usage duration. Its advantage lies in separating compute from storage, so you only pay for compute when running dbt transformations or querying data with PowerBI.

• Databricks can offer slightly lower costs for massive datasets in object storage, but compute costs can escalate if real-time data queries or frequent dbt transformations are required.

It’s essential to evaluate not just the storage cost, but also the total cost of ownership that includes compute, especially when using tools like PowerBI for near real-time reporting.

Integration with dbt, Fivetran, and PowerBI

• Snowflake integrates seamlessly with dbt and Fivetran, making it a great choice if you’re heavily relying on SQL-based workflows and analytics. It’s also tightly integrated with PowerBI for data visualization, making real-time insights smoother and faster.

• Databricks, with its Delta Lake storage, integrates well with dbt for ETL/ELT workflows, and its support for multiple data formats can add flexibility. However, the query performance in PowerBI might not be as fast as Snowflake’s for structured data reporting.

Conclusion: Which Platform Fits Your Pipeline Best?

Choosing between Snowflake and Databricks for data storage in your pipeline depends on your specific data needs:

• Choose Snowflake if your pipeline handles structured data, and you want cost-effective, scalable storage with powerful SQL querying capabilities. Its integration with dbt, Fivetran, and PowerBI is streamlined, making it a good choice for businesses that focus on SQL-based transformations and reporting.

• Choose Databricks if you need a storage solution that offers flexibility for handling a wider variety of data types. If your pipeline includes large-scale data lakes and unstructured data, Databricks’ Delta Lake offers robust features for managing complex data environments, but compute costs might rise for real-time queries and frequent transformations.

In summary, Snowflake offers cost-effective storage with strong integration for structured data pipelines, while Databricks provides greater flexibility for complex, unstructured data storage but may come with higher overall compute costs for certain workloads. The right choice ultimately depends on the type of data you manage and your cost management priorities. Let us know if you need further support in your decision making process, we would be happy to hop on a call and discuss your needs!