Databricks vs. Snowflake - When to Choose What?
In the world of modern business, data is the lifeblood of success. For Chief Information Officers (CIOs) at the helm of their organization’s data strategy, the choice between two industry giants, Databricks and Snowflake, isn’t merely a technical decision—it’s a strategic one. The choice revolves around a CIO’s capacity to identify the most suitable data platform for advancing their business.
As CIOs grapple with the question of whether to adopt Databricks or Snowflake as the linchpin of their data infrastructure, they begin a journey of thorough assessment. It involves assessing factors such as data volume, complexity, existing technology stack, and overarching organizational goals. This intricate decision-making process also considers cost implications, scalability prospects, and the paramount importance of data security.
In this exploration, we will delve into the nuanced criteria that guide a CIO’s choice, unveil the ideal use cases for both Databricks and Snowflake, and illuminate cases where each platform is more fitting.
Welcome to the strategic world of selecting the right cloud data platform—a decision that can shape the future of data-driven success in your organization.
What is Databricks?
What is Snowflake?
Databricks vs Snowflake: Main Difference
| Databricks | Snowflake | |
|---|---|---|
|
Model |
PaaS |
SaaS |
|
Storage Type |
Data Lakehouse |
Elastic Data Warehouse |
|
Customer support |
Working hours |
24X7 live |
|
Ideal for |
Complex data engineering |
Typical data analysis |
|
Complexity |
Not recommended for beginners |
Recommended for beginners |
|
Data Type |
Useful for all data types |
Semi-structured or structured data |
|
Scaling |
Auto-scaling
|
Auto-scaling up to 128 nodes |
|
Vendor lock-in |
No |
Yes |
|
Offerings |
Big data, data science, data analytics, and machine learning |
Database management & Data warehouse |
|
Data Science and Machine Learning |
Built-in and a single tool for all types of development. |
Accessible solely through third-party integrations. |
|
Query Interface |
SQL, Spark Dataframe, Koalas |
SQL |
|
Query Optimization |
Vectorization and cost-based optimization |
Vectorization and cost-based optimization
|
Databricks vs Snowflake: Features
Databricks Features:
- Real-time Data Collection: Continuously collects and stores data in real-time for immediate analysis.
- Parallel Computing Clusters: Distributes data processing tasks efficiently using parallel computing.
- Spark and Hadoop Integration: Seamlessly integrates with Spark, Hadoop, and other data processing frameworks.
- Cloud-native: Operates in the cloud, providing scalability and resource elasticity.
- Data Compression: Utilizes data compression techniques for efficient storage
- Machine Scaling: Scales machine learning workloads and data processing across multiple computers.
- Scalability: Grows with data complexity and volume, minimizing reengineering efforts.
- Focus on ML and Data Science: Offers robust support for machine learning, streaming, and data-based scientific analysis.
Snowflake Features:
- Multifactor Authentication: Enhances security with multifactor authentication for user accounts.
- Automatic Data Encryption: Automatically encrypts data, ensuring end-to-end security.
- Object-Level Access Control: Provides granular control over user permissions and data access.
- Fail-Safe Recovery: Implements fail-safe recovery mechanisms to protect against data loss.
- Extensive Client Connectors: Offers connectors for Python, Spark, Node.js, and more for easy integration.
- Secure Data Sharing: Facilitates secure data sharing among Snowflake users for collaboration.
- Scalability: Grows with data complexity and volume, minimizing reengineering efforts.
- Optimized Table Structures: Provides user-friendly table structures for efficient data organization.
- Continuous Data Pipelines: Automates data flow and organization, ensuring efficient data processing.
Databricks vs. Snowflake: A Cost Comparison
Databricks charges around $99 per month on average. They also offer a free version and a 14-day free trial for all their packages. Keep in mind that storage costs are separate from this price, so the total cost may vary depending on how much storage you use.
Snowflake is simpler to understand but can be very expensive. It may seem like it starts at around $40 per month, but they split the costs for computing and storage, which can make it more unpredictable. To make the right choice, it’s essential to use the resources provided by both companies to understand your specific needs and how the pricing models fit with your data processing and storage requirements.
Databricks needs good knowledge of setting up infrastructure but can be more economical for large workloads, while Snowflake’s costs can vary more due to the separation of computing and storage charges. So, consider your organization’s needs and budget carefully when making your decision.
Why opt for Databricks Instead of Snowflake?
Databricks has its roots in academia and stands as the exclusive data lakehouse platform in the cloud, a testament to its demonstrated expertise. When assessed from a technical perspective, it shines as the superior choice. Inspired by Databricks Lakehouse’s success, Microsoft launched their Unified Analytic platform called MS Fabric. Prior to this, Power BI often failed with live Direct connection to an SQL Server, causing all kinds of mess with data locking. Now, they can do near real-time visualization with a connection to a DirectLake.
Databricks not only matches Snowflake’s capabilities but also offers an enhanced spectrum of analytical possibilities. Its scalability knows no bounds, provided you’re willing to make the requisite infrastructure investment.
If you’re a seasoned professional seeking a comprehensive solution that not only accommodates data storage and analysis but also facilitates advanced data science exploration, then Databricks is the ideal choice for you.
Why opt for Snowflake Over Databricks?
Snowflake caters to a broad spectrum of users, both technical and non-technical. This cloud-based storage solution empowers you with robust data analytics capabilities without overwhelming you with excessive features.
Snowflake offers a gentler learning curve, making setup and utilization less complex, while still delivering a rich set of features. And also, Snowflake stands out with its superior customer support compared to Databricks.
f you seek a well-organized data warehouse for your storage and analytical needs, Snowflake emerges as a dependable choice.
Apart from these differences Databricks and Snowflake share numerous features:
- Cloud-Based: Both platforms operate in the cloud.
- Analytics: They offer quality analytical capabilities, facilitating data analysis for users.
- Security: Both prioritize high-security measures to protect their clients' data.
- Pricing Model: Both platforms employ a consumption-based pricing model.
- Scalability: Users can easily scale their operations on both Databricks and Snowflake.
Databricks vs Snowflake: The Ultimate Comparison
When evaluating the choice between Databricks and Snowflake, it’s essential to assess your company’s data utilization and processing requirements. Databricks excels in handling streaming data, while Snowflake is better suited for conventional data analysis. It’s worth noting that Databricks is known for its complexity and a somewhat challenging user interface. But, if this doesn’t deter you, and you possess the necessary skills, Databricks offers greater flexibility and an extensive set of features.
Many comparison sources indicate that numerous companies opt to use both platforms in conjunction to harness the advantages of each. Therefore, conducting an internal assessment within your organization to determine specific needs is crucial. And when it comes to the intricate task of extracting complex data from a diverse array of sources, People Tech Group is your trusted ally. We offer an efficient solution that facilitates the seamless movement of data from sources like databases or SaaS applications to your preferred Data Warehouses, such as Snowflake and Databricks. This enables you to effortlessly visualize your data within the Business Intelligence tool of your choice, streamlining your data management process.
