Table of Contents
Storing and managing large volumes of data can be challenging without a modern cloud data platform. Let me introduce you to 2 of the most popular cloud data platforms: Snowflake and Databricks.
Snowflake is a cloud data warehouse designed to store and manage structured and Semi-structured data using SQL queries for Business Intelligence (BI), SQL Analytics & Data warehousing.
Databricks, on the other hand, follows a data lakehouse approach built on Apache Spark, designed to store & manage raw, unprocessed, processed, and curated data, ideal for ML, AI, Streaming, and data engineering.
There is no right or wrong between the two platforms. Snowflake will be the ideal choice if your business has a lot of structured data and your BI team uses SQL queries and wants simple data warehousing with less management. Databricks will be best for businesses that work with Python, Spark, and ML engineers and want to store large volumes of unstructured data.
Snowflake is a fully managed multi-cloud platform that handles the entire data process in one ecosystem, bringing data from different sources, processing, analyzing, using it to create ML models, and building applications that use data and AI. It requires zero management and allows organizations to work faster and be more innovative with their data.
Databricks is a unified data and AI platform that enables organizations to store, process, manage, and analyze large-scale data, perform reporting, and train AI/ML models. It is a managed version of Apache Spark. Organizations do not need to worry about managing servers, cluster crashes, or scaling issues, as Databricks takes care of all. It helps with data cleaning, transformation, building ETL pipelines, and creating ML models. It also supports data migration services across public cloud providers like AWS, Microsoft Azure, and Google Cloud.
| Snowflake | Databricks |
| It is a cloud data platform designed for structured and semi-structured data | It is a Lakehouse platform |
| It works well with SQL queries, BI dashboards, and analytics | It combines big data processing, AI, and analytics into one ecosystem |
| Best for data warehousing, SQL analytics, and business intelligence (BI). | Best for data engineers, machine learning teams, complex transformation and ETL workloads |
| It runs faster for small to medium-sized analytics | To build massive ETL and AI workloads, Databricks is much ahead |
| Best for running SQL queries | Best with SQL, Python, Scala, R languages |
| Snowflake can be cheaper for teams who only need SQL-based analytics | For AI, ETL, and ML workloads, Databricks can be cheaper if optimized efficiently |
| Snowflake is easy to use with a clean UI and minimal setup. | Databricks lacks simple, out-of-the-box functionality and requires manual coding |
Snowflake’s multi-cluster shared data architecture consists of 3 separate layers,
Snowflake stores Structured, Semi-structured, and Unstructured data with support for various types of tables: Snowflake tables, Apache Iceberg tables, and Hybrid tables.
Snowflake table
Unlike traditional databases, Snowflake tables automatically compress, organize, and optimize the data for fast query performance, best for data warehousing.
Apache Iceberg
Apache Iceberg is a high-performance modern table that manages and organizes already stored files in Snowflake and adds features like transactions, schema evolution, and time travel.
Hybrid Tables
Hybrid tables are best for fast transactional workloads. It uses index-based reads and writes to provide fast response times, minimal delay, quick response, and fast data processing.
The compute layer uses virtual warehouses, which are a cluster of compute resources that run SQL queries and code in languages like Python, Java, and Scala. Each virtual warehouse works independently, meaning any issues occurs in one warehouse will not affect the other warehouses’ performance.
The cloud service layer is the part of Snowflake that manages Security, metadata management, User authentication, query parsing, and optimization.
Databricks architecture
Databricks architecture has two main layers: Control Plane and Compute Plane.
The Control Plane is a Management layer of Databricks that manages workspace applications, notebooks, configurations, and clusters.
Compute Plane is a data processing layer where compute resources run data processing jobs, Data transformations, SQL queries, and Machine learning workloads.
While both Snowflake and Databricks work best for large-scale analytics, they differ in how they optimize performance.
| Snowflake | Databricks |
| Use multi-cluster warehouses to handle complex queries | Uses distributed computing to process large datasets in parallel |
| Use features like query acceleration, search optimization, and materialized views. | Use features such as data skipping, file compaction, and optimized storage layouts to achieve faster reads and writes. |
| Monitor queries, identify bottlenecks, and continuously improve performance. | Monitor clusters, queries, execution metrics, and identify bottlenecks to optimize workloads. |
| Best for Business intelligence (BI), reporting, dashboards, and SQL analytics | SQL queries, ETL pipelines, DataFrame operations, and Delta Lake workloads. |
| Snowflake | Databricks |
| Snowflake charges around $1.50–$4.00 per credit depending on edition and commitment | Databricks charges per DBU (Databricks Unit), ranging from $0.22 for jobs light compute to $0.70 for serverless SQL |
| Storage runs around $23/TB/month. | For storage, you have to pay separately through the cloud provider (AWS, Azure, or Google Cloud) |
| Infrastructure is managed and included in Snowflake pricing | For cloud VMs, storage, and networking, additional costs are needed to pay |
| Costs are high due to automatic optimization, minimal tuning, and management | Databricks can be cheaper with proper optimization but requires knowledge of Spark and tuning. |
AI and machine learning features
Snowflake
Databricks
DevsTree, best data engineering company for Snowflake and Databricks, providing scalable data, analytics, and AI solutions.
Security and governance
Snowflake
Databricks
Integrations
Snowflake integrates well with Cloud Infrastructure, BI and Analytics, and ETL & Data Engineering Tools.
Databricks supports integrations with Cloud Infrastructure, BI and Visualization Tools, and Data Engineering and Developer Tools.
Snowflake
| Pros | Cons |
| Snowflake supports multicloud and can run on all major clouds: AWS, Azure, and GCP. | Snowflake has limited native ML capabilities |
| Easy to use. Even first-time users can easily learn Snowflake | Without proper optimization, the costs can grow quickly for continuous compute |
| Support for Java, Python, and other languages via Snowpark | Complex legacy data migration and planning challenges |
| Strong BI ecosystem and highly secure | Has a smaller user community than its competitors |
Databricks
| Pros | Cons |
| Complete support for machine learning and faster AI | Without Spark and lakehouse knowledge, it can be complex and time-consuming. |
| Built-in machine learning support and popular libraries for faster model development. | It is run on Scala language which can be hard to learn for many |
| Keeps data secure within your cloud. | Has a small user community compared to popular free tools |
| Detailed guides for AWS, Azure, and GCP. | Can be expensive for smaller projects |
Both Snowflake and Databricks are powerful cloud data platforms, but they serve different business needs. Snowflake is the better choice for organizations focused on SQL analytics, business intelligence, and data warehousing with minimal management. Databricks is ideal for companies building AI, machine learning, ETL pipelines, and large-scale data engineering solutions. The right platform depends on your data types, team expertise, workload, and long-term business goals. By evaluating these factors carefully, you can choose the solution that delivers the best performance, scalability, and value for your organization.
FAQ's
Frequently Asked Questions
If your business requirement includes teams working with BI Dashboards, reporting, and SQL analytics, with little to no management, then Snowflake is the way to go. The separation of storage and compute, micro-partitioning and clustering, and virtual warehouses make it ideal for analytics workloads.
If your business requirements rely on advanced Machine Learning and AI model management, the ability to process unstructured data, support ACID transactions, and unified data governance, then Databricks is the ideal choice.
Databricks is best for handling complex data engineering, advanced analytics, and supporting end-to-end ML deployment. However, if you need a platform that works with traditional BI, is simple to maintain, and supports micro-partition clustering to organize data efficiently, then Snowflake is the way to go.
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