{"id":14158,"date":"2024-11-26T14:27:00","date_gmt":"2024-11-26T13:27:00","guid":{"rendered":"https:\/\/wearekemb.com\/?p=14158"},"modified":"2025-02-06T22:08:53","modified_gmt":"2025-02-06T21:08:53","slug":"snowflake-vs-databricks","status":"publish","type":"post","link":"https:\/\/wearekemb.com\/en\/snowflake-vs-databricks\/","title":{"rendered":"Snowflake vs Databricks"},"content":{"rendered":"<p>[et_pb_section fb_built=&#8221;1&#8243; custom_padding_last_edited=&#8221;on|phone&#8221; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; background_color=&#8221;#FFFFFF&#8221; custom_padding=&#8221;30px||30px||true|false&#8221; custom_padding_tablet=&#8221;||30px||false|false&#8221; custom_padding_phone=&#8221;&#8221; hover_enabled=&#8221;0&#8243; saved_tabs=&#8221;all&#8221; global_colors_info=&#8221;{}&#8221; theme_builder_area=&#8221;post_content&#8221; sticky_enabled=&#8221;0&#8243;][et_pb_row _builder_version=&#8221;4.19.4&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221; theme_builder_area=&#8221;post_content&#8221;][et_pb_column type=&#8221;4_4&#8243; _builder_version=&#8221;4.19.4&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221; theme_builder_area=&#8221;post_content&#8221;][et_pb_text _builder_version=&#8221;4.27.2&#8243; _module_preset=&#8221;default&#8221; custom_margin=&#8221;||10px||false|false&#8221; animation_direction=&#8221;left&#8221; global_colors_info=&#8221;{}&#8221; theme_builder_area=&#8221;post_content&#8221;]<\/p><div id=\"ez-toc-container\" class=\"ez-toc-v2_0_84 counter-hierarchy ez-toc-counter ez-toc-custom ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/wearekemb.com\/en\/snowflake-vs-databricks\/#Choosing_Between_Snowflake_and_Databricks_for_Data_Storage_in_Your_Data_Pipeline\" >Choosing Between Snowflake and Databricks for Data Storage in Your Data Pipeline<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/wearekemb.com\/en\/snowflake-vs-databricks\/#Pipeline_Overview_dbt_Fivetran_PowerBI_and_Data_Storage\" >Pipeline Overview: dbt, Fivetran, PowerBI, and Data Storage<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/wearekemb.com\/en\/snowflake-vs-databricks\/#Snowflake_as_Data_Storage\" >Snowflake as Data Storage<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/wearekemb.com\/en\/snowflake-vs-databricks\/#Databricks_as_Data_Storage\" >Databricks as Data Storage<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/wearekemb.com\/en\/snowflake-vs-databricks\/#Choosing_Between_Snowflake_and_Databricks_for_Storage\" >Choosing Between Snowflake and Databricks for Storage<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/wearekemb.com\/en\/snowflake-vs-databricks\/#Conclusion_Which_Platform_Fits_Your_Pipeline_Best\" >Conclusion: Which Platform Fits Your Pipeline Best?<\/a><\/li><\/ul><\/nav><\/div>\n\n<h2><span class=\"ez-toc-section\" id=\"Choosing_Between_Snowflake_and_Databricks_for_Data_Storage_in_Your_Data_Pipeline\"><\/span><b>Choosing Between Snowflake and Databricks for Data Storage in Your Data Pipeline<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">When building a data pipeline that includes tools like <\/span><b>dbt<\/b><span style=\"font-weight: 400;\">, <\/span><b>Fivetran<\/b><span style=\"font-weight: 400;\">, and <\/span><b>PowerBI<\/b><span style=\"font-weight: 400;\"> for data integration, transformation, and visualization, the choice of data storage becomes crucial. In this article, we\u2019ll focus on using <\/span><b>Snowflake<\/b><span style=\"font-weight: 400;\"> and <\/span><b>Databricks<\/b><span style=\"font-weight: 400;\"> solely as data storage solutions within your pipeline. We\u2019ll examine how each platform supports your storage needs, the trade-offs in performance, and explore the storage cost structures to help you make an informed decision.<\/span><\/p>\n<p>[\/et_pb_text][et_pb_text _builder_version=&#8221;4.27.2&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221; theme_builder_area=&#8221;post_content&#8221;]<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Pipeline_Overview_dbt_Fivetran_PowerBI_and_Data_Storage\"><\/span><b>Pipeline Overview: dbt, Fivetran, PowerBI, and Data Storage<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Before we dive into the specifics of Snowflake and Databricks, let\u2019s establish the role of each component in your pipeline:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">\n<p><b>Fivetran<\/b><span style=\"font-weight: 400;\">: Automates data extraction and ingestion from various sources into your chosen data storage platform (Snowflake or Databricks).<\/span><\/p>\n<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">\n<p><b>dbt<\/b><span style=\"font-weight: 400;\">: Performs transformations on raw data stored in the data platform to make it analytics-ready.<\/span><\/p>\n<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">\n<p><b>PowerBI<\/b>: Visualizes the transformed data, generating insights and reports for business decision-making.<\/p>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">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&#8217;s performance, cost, and integration with these tools.<\/span><\/p>\n<p>[\/et_pb_text][et_pb_text _builder_version=&#8221;4.27.2&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221; theme_builder_area=&#8221;post_content&#8221;]<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Snowflake_as_Data_Storage\"><\/span><b>Snowflake as Data Storage<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3><b>Optimized for SQL Queries and Data Warehousing<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Snowflake is designed to efficiently store <\/span><b>structured and semi-structured data<\/b><span style=\"font-weight: 400;\"> such as JSON, Avro, and Parquet. Its architecture allows Fivetran to load data seamlessly into Snowflake\u2019s virtual warehouses, where dbt can then run SQL-based transformations.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">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.<\/span><\/p>\n<p>[\/et_pb_text][et_pb_text _builder_version=&#8221;4.27.2&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221; theme_builder_area=&#8221;post_content&#8221;]<\/p>\n<h3><b>Storage Costs in Snowflake<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Snowflake offers a pay-as-you-go pricing model, but users can now leverage <\/span><b>multi-cluster warehouses<\/b><span style=\"font-weight: 400;\"> to handle large query loads without increasing costs for inactive periods. Additionally, Snowflake&#8217;s <\/span><b>compaction features<\/b><span style=\"font-weight: 400;\"> help reduce the overall footprint of storage for large-scale workloads.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">\n<p><b>Pay-As-You-Go Pricing<\/b><span style=\"font-weight: 400;\">: 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.<\/span><\/p>\n<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">\n<p><b>Compute Costs<\/b><span style=\"font-weight: 400;\">: While storage is affordable, Snowflake charges for compute separately. If your pipeline requires frequent dbt transformations, you\u2019ll need to factor in compute costs, which can add up depending on how often transformations run.<\/span><\/p>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">With Snowflake, you can <\/span><b>optimize storage costs<\/b><span style=\"font-weight: 400;\"> by compressing large datasets, using partitioning, or leveraging Snowflake\u2019s <\/span><b>Time Travel<\/b><span style=\"font-weight: 400;\"> feature to reduce the need for maintaining multiple copies of the data.<\/span><\/p>\n<p>[\/et_pb_text][et_pb_text _builder_version=&#8221;4.27.2&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221; theme_builder_area=&#8221;post_content&#8221;]<\/p>\n<h3><b>Seamless Integration with PowerBI<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Snowflake\u2019s robust SQL capabilities and fast query response times make it an excellent choice for integrating with <\/span><b>PowerBI<\/b><span style=\"font-weight: 400;\">. Once dbt transforms your data, PowerBI can easily access it through direct queries, giving your users real-time insights with minimal latency.<\/span><\/p>\n<p>[\/et_pb_text][et_pb_text _builder_version=&#8221;4.27.2&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221; theme_builder_area=&#8221;post_content&#8221;]<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Databricks_as_Data_Storage\"><\/span><b>Databricks as Data Storage<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3><b>Built for Data Lakes and Flexibility<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">While Databricks is often associated with <\/span><b>big data processing and machine learning<\/b><span style=\"font-weight: 400;\">, its <\/span><b>Delta Lake<\/b><span style=\"font-weight: 400;\"> architecture also makes it a powerful storage solution. Databricks allows you to store <\/span><b>structured, semi-structured, and unstructured data<\/b><span style=\"font-weight: 400;\"> in cloud object storage (e.g., AWS S3, Azure Blob Storage), with an added layer of data reliability and governance through <\/span><b>Delta Lake<\/b><span style=\"font-weight: 400;\">.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Databricks\u2019 storage flexibility is beneficial if you\u2019re dealing with a variety of data formats or require the ability to store <\/span><b>raw, untransformed data<\/b><span style=\"font-weight: 400;\"> for future use cases. This flexibility can be an advantage for pipelines that require storing large datasets before transforming them with dbt.<\/span><\/p>\n<h3><b>Storage Costs in Databricks<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">uses cloud storage (like AWS S3, Azure Blob), but with Delta Lake&#8217;s optimization features like <\/span><b>data skipping<\/b><span style=\"font-weight: 400;\"> and <\/span><b>Z-order indexing<\/b><span style=\"font-weight: 400;\">, you can further reduce storage and query costs by organizing data more efficiently for faster retrieval.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">\n<p><b>Cloud Object Storage Pricing<\/b><span style=\"font-weight: 400;\">: Databricks relies on your cloud provider\u2019s 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.<\/span><\/p>\n<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">\n<p><b>Delta Lake Overhead<\/b><span style=\"font-weight: 400;\">: Storing data in <\/span><b>Delta Lake<\/b><span style=\"font-weight: 400;\"> 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.<\/span><\/p>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">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 <\/span><b>PowerBI<\/b><span style=\"font-weight: 400;\"> can be higher than in Snowflake, particularly for real-time analytics.<\/span><\/p>\n<p>[\/et_pb_text][et_pb_text _builder_version=&#8221;4.27.2&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221; theme_builder_area=&#8221;post_content&#8221;]<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Choosing_Between_Snowflake_and_Databricks_for_Storage\"><\/span><b>Choosing Between Snowflake and Databricks for Storage<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3><b>Storage Efficiency and Scaling<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Both Snowflake and Databricks provide efficient and scalable storage, but they differ in how they handle large volumes and types of data:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">\n<p><b>Snowflake<\/b><span style=\"font-weight: 400;\"> is ideal if your pipeline primarily deals with <\/span><b>structured<\/b><span style=\"font-weight: 400;\"> data, and you prefer SQL-based transformations. It is highly efficient for storage compression and performance with SQL queries.<\/span><\/p>\n<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">\n<p><b>Databricks<\/b><span style=\"font-weight: 400;\"> shines when dealing with <\/span><b>unstructured<\/b><span style=\"font-weight: 400;\"> or <\/span><b>large-scale data lakes<\/b><span style=\"font-weight: 400;\">. If you need flexibility in storing raw or complex data types for future analytics or machine learning tasks, Databricks is a good option.<\/span><\/p>\n<\/li>\n<\/ul>\n<h3><b>Cost Considerations<\/b><\/h3>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">\n<p><b>Snowflake<\/b><span style=\"font-weight: 400;\"> storage is generally straightforward, with costs dependent on data size and usage duration. Its advantage lies in <\/span><b>separating compute from storage<\/b><span style=\"font-weight: 400;\">, so you only pay for compute when running dbt transformations or querying data with PowerBI.<\/span><\/p>\n<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">\n<p><b>Databricks<\/b><span style=\"font-weight: 400;\"> 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.<\/span><\/p>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">It\u2019s essential to evaluate not just the <\/span><b>storage cost<\/b><span style=\"font-weight: 400;\">, but also the <\/span><b>total cost of ownership<\/b><span style=\"font-weight: 400;\"> that includes compute, especially when using tools like PowerBI for near real-time reporting.<\/span><\/p>\n<p>[\/et_pb_text][et_pb_text _builder_version=&#8221;4.27.2&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221; theme_builder_area=&#8221;post_content&#8221;]<\/p>\n<h3><b>Integration with dbt, Fivetran, and PowerBI<\/b><\/h3>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">\n<p><b>Snowflake<\/b><span style=\"font-weight: 400;\"> integrates seamlessly with dbt and Fivetran, making it a great choice if you\u2019re heavily relying on SQL-based workflows and analytics. It\u2019s also tightly integrated with <\/span><b>PowerBI<\/b><span style=\"font-weight: 400;\"> for data visualization, making real-time insights smoother and faster.<\/span><\/p>\n<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">\n<p><b>Databricks<\/b><span style=\"font-weight: 400;\">, with its <\/span><b>Delta Lake<\/b><span style=\"font-weight: 400;\"> 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\u2019s for structured data reporting.<\/span><\/p>\n<\/li>\n<\/ul>\n<p>[\/et_pb_text][et_pb_text _builder_version=&#8221;4.27.2&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221; theme_builder_area=&#8221;post_content&#8221;]<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Conclusion_Which_Platform_Fits_Your_Pipeline_Best\"><\/span><b>Conclusion: Which Platform Fits Your Pipeline Best?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Choosing between Snowflake and Databricks for data storage in your pipeline depends on your specific data needs:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">\n<p><b>Choose Snowflake<\/b><span style=\"font-weight: 400;\"> 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.<\/span><\/p>\n<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">\n<p><b>Choose Databricks<\/b><span style=\"font-weight: 400;\"> 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\u2019 Delta Lake offers robust features for managing complex data environments, but compute costs might rise for real-time queries and frequent transformations.<\/span><\/p>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">In summary, <\/span><b>Snowflake<\/b><span style=\"font-weight: 400;\"> offers cost-effective storage with strong integration for structured data pipelines, while <\/span><b>Databricks<\/b><span style=\"font-weight: 400;\"> 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 <strong><a href=\"https:\/\/wearekemb.com\/en\/contact-us\/\">happy to hop on a call<\/a><\/strong> and discuss your needs!\u00a0<\/span><\/p>\n<p>[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=&#8221;4.19.4&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221; theme_builder_area=&#8221;post_content&#8221;][et_pb_column type=&#8221;4_4&#8243; _builder_version=&#8221;4.19.4&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221; theme_builder_area=&#8221;post_content&#8221;][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Snowflake or Databricks? Discover which data storage solution best supports your pipeline with dbt, Fivetran, and PowerBI &#8211; performance, cost, and integration compared.<\/p>\n","protected":false},"author":15,"featured_media":14161,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"2880","content-type":"","inline_featured_image":false,"footnotes":""},"categories":[24,26],"tags":[],"class_list":["post-14158","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-business-intelligence-en","category-data-reporting-en"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.7 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Snowflake vs Databricks - kemb GmbH - Your Partners in Business Intelligence, Marketing Digital Transformation<\/title>\n<meta name=\"description\" content=\"Snowflake or Databricks? 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