The main difference between Databricks and Snowflake is that Databricks is better suited for data science and massive workloads. In contrast, Snowflake is better for SQL-like business intelligence and smaller workloads.
|Cloud Platform Support||Cloud providers like Azure, Google, AWS||Cloud providers like Azure, Google, AWS|
|Who’s it’s for||Data scientists, data engineers, and data analysts||Data analysts|
|Scalability||Auto-scaling||Auto-scalability up to 128 nodes|
|Architecture||Built on Apache Spark – a cluster-based computing framework for big data processing||Consists of query processing, database storage, and cloud services|
|User-friendliness||Has a steep learning curve||User-friendly|
|Use cases||Data science, big data, data analytics, and machine learning||Data analytics and business intelligence|
|Data structure||All data types, including structured, semi-structured, and unstructured data. It can handle data like video, audio, text, etc.||Snowflake stores data in a structured and semi-structured format. However, the recently launched Snowpark API helps with the processing of unstructured data|
|Pricing model||Pay by usage||Pay by usage|
|Query||SQL, Koalas, Spark Dataframe||Custom SQL query engine that runs natively on the cloud|
|Transactions||Supports ACID (Atomicity, Consistency, Isolation, and Durability) transactions||Supports ACID transactions|
|Security||Provides separate customer keys and RBAC (role-based access control) for workspace objects, pools, clusters, and table levels||Uses always-on encryption. Provides separate customer keys and RBAC|
|Pricing||Pay-as-you-go pricing.||Pay-as-you-go pricing.|
Databricks is a cloud-based data lakehouse powered by Apache Spark. It's great at big data processing, analysis, machine learning, and AI applications. The platform was designed for data engineers and data scientists and supports many development languages.
Unified analytics platform. Databricks has data science, data engineering, and AI capabilities in one platform. The combination of all this in one application helps teams in different departments collaborate together.
Apache Spark. Powered by Apache Spark, Databricks excels at high-performance machine learning and big data processing.
Interactive Workspace. Databricks supports various languages like Python, Scala, R, and SQL. It also comes with a built-in Jupyter Notebook integration. These notebooks help dev teams share code as well as run data pipelines and machine learning.
Delta Lake. Databricks Delta Lake is an open-source storage layer that provides ACID (Atomicity, Consistency, Isolation, and Durability) and other reliability features to your data lake. It helps improve data quality and consistency.
MLflow. MLflow is an open-source platform used for configuring machine learning environments. It has three core components -- model management, model development, and experiment tracking.
Integrates with numerous data sources. Not only is Databricks connected to the full Azure stack, it also links to other resources like CSV files, SQL servers, and JSON files.
Data reliability. Data in data lakes can be of poor quality. This is because there's no control over the data ingested. However, Databricks storage layer counteracts this by making sure quality data goes into the system.
Data versioning. Databricks takes data snapshots that gives developers the option to revert to earlier versions of data.
Works for smaller projects. While Databricks is ideal for large-scale operations, you can also use it for smaller projects. It's a one-stop solution for almost any analytical task.
Amazing customer service. On the plus side, Databricks has great tech support. So despite their smaller community, the overall community size may not matter as much.
Steep learning curve and setup complexity. Despite detailed documentation, learning to operate Databricks can be difficult; it has too many tools, features, and integrations.
Navigating its setup is challenging. Tech people call it "time-consuming" and "confusing," saying it takes several hours or days to do so.
Lacks ease of use. For example, it doesn't offer drag-and-drop and visualization features -- things that improve a non-programmer's experience.
Scala as a main language. While Databricks supports languages like SQL, Python, and R, it's initially based on Spark.
Hard to find data scientists who know Scala. Spark is written in Scala that runs on Java Virtual Machine (JVM), and commands issued in non-JVM languages need additional transformations to be executed on top of a JVM process. Writing code in Scala beats those written in Python and R. Unfortunately it can be difficult to find data scientists who know the latter as it's harder to learn.
Small community. Databricks has a relatively small community compared to other popular tools. If you check StackOverflow, you'll find only 500 questions on Databricks. And the community has only 350+ members on Reddit. So it's harder to find answers to navigate the platform.
Snowflake is a cloud-based data warehouse solution and SaaS solution. It's used for data storage, management, and real-time analytics of structured and semi-structured data. It also supports massive parallel processing (MPP) for faster data querying and analysis.
Business intelligence and analysis. Snowflake can help you get insights from data through its advanced analytics and interactive reporting. It's compatible with business intelligence tools and data platforms like Looker, QuickSight, Power BI, and Tableau.
Easy-to-use cloud data warehousing. It excels at providing a scalable and easy-to-use data warehouse platform.
Supports structured and semi-structured data. Snowflake supports both structured and semi-structured data such as XML, JSON, Avro, and Parquet.
Data integration and sharing. It has native data-sharing capabilities that can facilitate data collaboration between organizations.
Security and compliance. Snowflake has strong security and compliance with features such as encryption and role-based access control (RBAC). The platform also supports various compliance standards.
Data protection and security. Snowflake keeps your data highly secure. You can also set regions for storage to comply with regulatory guidelines like HIPAA, SOC1, SOC2, and PCI DSS.
Built-in features that encrypt data at rest and in transit, plus the ability to regulate access levels and control IP allows and blocklists.
Performance and scalability. Snowflake runs an almost unlimited number of concurrent workloads against a single copy of data -- because storage and compute are separate. This allows multiple users to execute multiple queries simultaneously.
Processing Power One benchmark shows that Snowflake can process 6-60 million rows of data in anywhere from 2 seconds to 10 seconds -- a feat that's fairly impressive.
Vertical and horizontal capabilities The platform can also be scaled both vertically and horizontally. Vertical scaling (by upgrading CPUs) can add more computer power to the already existing warehouses, whereas horizontal scaling is done by adding more cluster nodes.
Easy learning curve. Snowflake is fully SQL-based, making it easy for beginners without coding experience to learn. And if you have experience with data analytics or BI tools that work with SQL, you can find your way around Snowflake easily.
Costs can add up. Snowflake's pay-as-you-go pricing can get expensive as it's heavily dependent on your usage pattern.
Struggles with large data volumes. Unlike Databricks, Snowflake can struggle with large data volumes.
Not a tightly coupled cloud ecosystem. Most public cloud offerings have their own cloud data warehouse tool, like Google BigQuery, Microsoft Azure SQL DW, and Amazon Redshift.
Small community. Compared to Google BigQuery and Amazon Redshift, Snowflake has only 6000+ members on its subreddit, which is smaller than the other two.
It also has fewer questions on StackOverflow comparatively.
That being said, the community is very active, growing and is easier to use compared to the other solutions. You're less likely to run into issues.
Databricks has a two-layered architecture.
The bottom layer is the Data Plane, where all the data is stored. The top layer is the Control Plane which includes the different services provided by Databricks. Notebook commands and other workspace configurations are stored here.
Additionally, Databrick has a data warehouse layer called Delta Lake. It has three tables that retain data of different quality -- one for raw data, one for slightly clean data, and one for clean, consumable data.
In contrast, Snowflake comes with 3 different layers.
The bottom layer is the storage layer where data is stored in a columnar format.
The middle layer is the compute layer or query processing layer that uses "Virtual Warehouses" for running queries. These are independent compute clusters that consist of multiple nodes.
The top-most layer is the cloud services layer which manages the other parts of Snowflake. Login requests and queries submitted to Snowflake will be first sent to this layer and then forwarded to the compute layer for processing.
The biggest difference between Databricks and Snowflake is in encryption. Snowflake has always-on encryption mode, whereas Databricks encrypts at rest.
Both services provide role-based access (RBAC) which is based on one's role in any organization and the level of authorized access.
Both Databricks and Snowflake can scale data in their own ways. Databricks uses Spark to manage large amounts of data, whereas Snowflake's design facilitates independent scaling of storage and compute resources.
Built on Apache Spark, Databricks is optimized for high-performance data processing (especially large datasets), machine learning, and analysis.
On the other hand, Snowflake is great for ETL (extract, transform, and load) and SQL purposes. You can use it for fast queries and data analysis as it optimizes all storage during ingestion.
Both Databricks and Snowflake have many integration options with the most popular data sources and platforms.
Databricks integrates well with big data processing tools such as Hadoop. It's compatible with data acquisition vendors like Fivetran, Rivery, and Data Factory. It works seamlessly with Amazon S3, Google Cloud Storage, and Azure Blob Storage. In addition, it also supports data visualization tools like Power Bi and Tableau.
Snowflake also has connectors and integration with data ingestion and ETL tools like Fivetran, Talend, and Matillion. Plus, it supports integration with business intelligence platforms like Tableau, Looker, and Power Bi.
Databricks and Snowflake have pay-as-you-go pricing, which means you only pay for the resources you consume.
Snowflake's pricing is based on warehouse usage.
These warehouses come in pre-configured sizes -- X-Small, Large, X--Large, etc. Prices vary greatly depending on the size -- the more the size, the greater the pricing. The service also charges based on the total load.
Databricks can be less expensive than Snowflake in terms of data storage as it lets customers have individual storage environments customizable to their unique needs. For computing, the platform designs its prices according to DBUs or Databricks processing units.
Databricks has three business price tiers, Standard, Premium, and Enterprise.
Databricks is better than Snowflake for some business use cases like data science; however, Snowflake is better for applications like business intelligence.
Whichever solution you use, Portable is a great tool to extract, transform, and load data from more than 300+ long-tail applications.
So if you're looking for the best Databricks or Snowflake ETL tool, check out Portable now.