Snowflake vs Databricks guide will shed light on the similarities and differences between the top two data warehouse platforms so you can choose the best platform for your business needs.
Gone are the days when organizations used traditional data warehouses to store data from disparate sources. With the evolution of technology, companies are looking for scalable and flexible cloud platforms because of increased data volume and velocity.
Though few decent data warehouse platforms are in the market, the two that fiercely compete include Snowflake and Databricks. In this comparison guide, we will introduce the basics of both platforms and then discuss the critical differences between Databricks and Snowflake.
What is Snowflake?
Snowflake is a cloud-based data warehouse offering a pay-per-use service. It offers robust solutions for computing, analysis, and data retention. In addition, the self-managed service provides a wide range of out-of-the-box services, like data sharing, data cloning, third-party tools, etc., to meet the diverse needs of growing enterprises.
Advantages of Snowflake
- Efficient and adaptable Snowflake architecture.
- Suitable for cross-cloud workloads and multi-cloud platforms.
- Enhanced performance and near-infinite scalability.
- No IT infrastructure or management is required.
- Built-in speed optimization, safe data exchange, and data security.
Use Cases of Snowflake
Snowflake is well-suited for Business Intelligence projects that include using SQL for data analysis, creating visual dashboards, and reporting on data. Additionally, it’s suitable for data transformation.
What is Databricks?
Databricks is a cloud-based data analytics platform helping organizations analyze data at scale regardless of location. It has the ability to process large amounts of data and extract business intelligence using machine learning algorithms. It also supports various cloud service providers, including AWS, Azure, and GCP.
Advantages of Databricks
- Supports popular programming languages like Python, R, and SQL.
- Easy to link with SQL server, JSON files, and CSV files.
- Suitable for smaller projects and large-scale operations.
Use Cases of Databricks
Databricks can be used by businesses that handle large data workloads as it provides a one-stop solution for handling data, AI, and analytics. Further, you can use Databricks to manage data science workloads and ML tasks like predictive analytics.
Snowflake Vs Databricks Comparison Table
Here is a quick Snowflake vs Databricks platform comparison table where we reveal the main differences between the two data warehouses.
|Supporting cloud platforms||AWS, Azure, and GCP||AWS, Azure, and GCP|
|Scalability||Auto-scaling||Auto-scaling up to 128 nodes|
|User-friendliness||Learning curve||Easy to adapt|
|Migration to platform||Complex because it is a data lake||Easy as it is designed based on a data warehouse|
|Data structure||All data types (audio, video, raw, text, logs, etc.)||Structured and semi-structured data|
|Pricing||Pay by usage||Pay by usage|
|Provisioning of different node types||Yes||No|
|Query interface||SQL, Dataframe, Spark, Koalas||SQL|
|Services||Big data, data analytics, data science, and machine learning||Data warehouse and data management|
Similarities Between Databricks and Snowflake
One common thing about Snowflake data warehouses and Databricks lakehouse platform is that they combine unique features of data warehouses and data lakes. You can choose any of them to get the best of both worlds in data storage and computing.
They decouple their computing and storage options, making them independent and scalable. Additionally, these platforms allow you to create dashboards for analytics and reporting.
Differences Between Snowflake and Databricks
Snowflake is revolutionizing the data warehouse market with its SaaS offering, quick scalability, and near-zero maintenance capability. On the contrary, Databricks is known for combining data lakes and data warehouses in a single platform. Let us compare Databricks vs Snowflake based on different parameters like market share, performance, scalability, security, pricing, etc.
According to 6Sense market share reports for Snowflake and Databricks, Snowflake holds an 18.70% share in the data warehousing market. However, Databricks has less market share compared to Snowflake. It holds nearly a 14.47% share of the big data analytics market.
Snowflake performs efficiently for SQL and ETL operations. On the other hand, Databricks is ideal for use cases that involve data science, analytics, and machine learning.
Snowflake gives four enterprise-level perspectives like Basic, Premium, Enterprise, and Professional. On the contrary, Databricks offers three enterprise plans and is less expensive.
Databricks offers robust security by creating Virtual Private Cloud. It also allows the creation of encryption keys or using Personal Access Token for additional security.
Snowflake offers similar security like IP lists, strong encryption keys, multi-factor authentication, etc.
Databricks can scale automatically depending on the workload. For instance, it will add more workers to clusters when the load is high while reducing workers on underutilized clusters.
Snowflake also scales up and down to help you perform tasks like loading, analyzing, and integrating data. In addition, it offers additional compute clusters to maintain the balance when one cluster is overwhelmed.
Databricks uses a two-layered architecture with a bottom layer known as Data Plane. The core aim of Databricks is to store and process data. Databricks File System Layer sits on the top of cloud storage — either Azure Blob Storage or AWS S3.
Alternatively, Snowflake has a three-layered architecture with Data Storage Layer at its base. As the name defines, the third layer is responsible for storing data. The other layer is Query Processing, made up of virtual warehouses. The cloud Services Layer is at the top, where you handle functions like infrastructure management, authentication, and access control.
Besides Snowflake and Databricks, Azure Data Factory is a cloud-based PaaS platform suitable for data science projects. You can read our detailed Azure Data Factory vs Databricks guide to understand how the two differ.
Which Platform is Best: Databricks vs Snowflake
Both Databricks and Snowflake come with different feature sets and strengths. You must pick a platform that fits your data workload, strategy, volumes, and needs. For instance, trust Snowflake if you want a platform with a fixed pricing model for managed storage and computing.
On the other hand, if you want an open-source option that offers flexibility to integrate/use any third party or service, consider choosing Databricks. Companies wanting to leverage the benefits of both worlds can use them together. You can use Snowflake for data warehouse, while Databricks can be used for ETL operations.
If you are perplexed about which data warehouse platform you should choose, contact Inferenz experts. Our data and cloud experts will understand your business requirements and help you migrate data to the right platform. We hope this Snowflake vs Databricks comparison guide has cleared your doubts regarding the two tough contenders in the cloud data industry.