Due to the growing importance of data analytics and data management to enterprises, a comparison of the data platforms Snowflake and Databricks is necessary for today’s market.
Organizations need a mechanism to gather all the data they need to evaluate in one location where it can be ready for data mining as the amount of data to be studied grows gradually.
Without a doubt, the acclaimed cloud-based data systems Snowflake and Databricks are both industry leaders. Which data platform, however, is ideal for your company?
The quantity, speed, and quality that business intelligence applications require are all provided by Snowflake and Databricks.
While there are variances, there are also plenty of parallels. They have a distinct orientation, which is obvious when closely inspected.
The founders of Apache Spark established the enterprise software business Databricks.
It’s renowned for fusing the greatest aspects of data lakes and data warehouses into a lakehouse architecture.
Data warehousing business Snowflake offers cloud-based storage and access services with minimal hassle. It establishes its standing as a solution that offers secure access to your data while requiring almost little upkeep.
This article offers you a detailed comparison of Snowflake Vs. Databricks and explains each product’s benefits so you can decide which is best for your business. Let’s start with their introduction.
What is Snowflake?
Snowflake is a completely managed service that offers customers nearly limitless scalability of concurrent workloads for simple data integration, loading, analysis, and sharing.
Data Lakes, Data Engineering, Data Application Development, Data Science, and safe consumption of shared data are some of its typical uses.
Computing and storage are naturally separated by Snowflake’s distinctive design.
With the help of this architecture, you can practically provide all of your users and data workloads access to a single copy of your data without suffering any negative performance effects.
For a consistent user experience, Snowflake enables you to execute your data solution invisibly across various locations and Clouds.
By removing the complexity of the underlying Cloud infrastructures, Snowflake makes it feasible.
The Snowflake Data Marketplace, which offers many options to interact with thousands of Snowflake customers, also enables you to access shared datasets and data services.
- More effective data-driven decision-making: With Snowflake, you can eliminate data silos and provide everyone in the business access to useful insights. This is a crucial initial step in enhancing partner relationships, optimizing pricing, cutting expenses associated with operations, increasing sales effectiveness, and many other things.
- Improve Analytics Speed and Quality: You can strengthen your analytics pipeline with Snowflake by switching from nightly batch loads to real-time data streams. By allowing everyone in your business safe, concurrent, and controlled access to your data warehouse, you can improve the quality of analytics at work. This reduces expenses and manual labor, enabling firms to distribute resources optimally to maximize income.
- Data exchange with customization: You can create your own data exchange with Snowflake, allowing you to transmit live, regulated data in a safe manner. Additionally, it serves as a motivation to develop stronger data connections with partners, clients, and other business units. It achieves this by obtaining a 360-degree perspective of your consumer, which offers information on important customer characteristics including interests, occupation, and many more.
- Greater Product and User Experiences: You can comprehend user behavior and product use better with Snowflake in place. Additionally, you can make use of the entire data set to satisfy customers, greatly enhance your product line, and promote data science innovation.
- Strong Security: All compliance and cybersecurity data can be centralized in a secure data lake. The rapid incident reaction is guaranteed by snowflake data lakes. Combining massive amounts of log data in one place and quickly evaluating years’ worth of log data, enables you to get the full picture of an occurrence. Semi-structured logs and structured enterprise data can now be combined in a single data lake. Without any indexing, Snowflake enables you to get your foot in the door while making it simple to edit and change data once it has been imported.
What is Databricks?
Databricks is a cloud-based data platform driven by Apache Spark. It focuses on Big Data Analytics and Collaboration majorly.
You can provide a full Data Science workspace for Business Analysts, Data Scientists, and Data Engineers to interact using Databricks’ Machine Learning Runtime, controlled ML Flow, and Collaborative Notebooks.
Dataframes and Spark SQL libraries, which allow you to deal with structured data, are housed at Databricks.
In addition to helping you create Artificial Intelligence solutions, Databricks makes it simple to draw conclusions from your current data.
In addition, Databricks offers a variety of libraries for machine learning, including Tensorflow, Pytorch, and others, for building and training machine learning models.
A wide range of business clients utilizes Databricks to carry out massive production processes across a huge variety of use cases and sectors, including Healthcare, Media & Entertainment, Financial Services, Retail, and so much more.
- Delta Lake: Databricks has a transactional storage layer that is open-source and designed to be utilized across the whole data lifecycle. This layer can be used to provide data scalability and reliability to your current data lake.
- Interactive Notebooks: You can rapidly access your data, analyze it, construct models with others, and share fresh, useful insights when you have the right tools and language. Scala, R, SQL, and Python are just a few of the languages that are supported by Databricks.
- Machine learning: With the aid of cutting-edge frameworks like Tensorflow, Scikit-Learn, and Pytorch, Databricks gives you one-click access to preconfigured Machine Learning environments. You can share and monitor experiments, manage models together, and replicate runs all from one central repository.
- Enhanced Spark Engine: You can get the most latest versions of Apache Spark using Databricks. Various Open-source libraries can also be seamlessly integrated with Databricks. You can quickly set up clusters and create a fully managed Apache Spark environment if you have access to the availability and scalability of several Cloud service providers. Clusters can be configured, set up, and fine-tuned with Databricks without the need for ongoing monitoring to maintain optimal performance and dependability.
Core Differences between Snowflake & Databricks
Snowflake is an ANSI SQL-based serverless system with totally distinct storage and computes processing layers.
Each virtual warehouse (i.e., compute cluster) in Snowflake stores a subset of the whole data set locally while using massively parallel processing (MPP) to perform queries.
For internal data organization and optimization into a compressed columnar format that can be stored in the cloud, Snowflake employs micro partitions.
The fact that Snowflake maintains all aspects of data management, including file size, compression, structure, metadata, statistics, and other data items that are not immediately visible to users and can only be accessed through SQL queries, enables all of this to be done automatically.
Virtual warehouses, which are computed clusters made up of many MPP nodes, are used to do all processing within Snowflake.
Snowflake and Databricks are both SaaS solutions, however, Databricks’ architecture is very different because it is built on Spark.
A multi-language engine called Spark can be installed in the cloud and is based on single nodes or clusters. Databricks presently utilizes AWS, GCP, and Azure, much as Snowflake.
A control plane and a data plane make up its structure. All processed data is contained in the data plane, whilst all backend services managed by Databricks Serverless computing are found in the control plane.
Serverless computing enables administrators to create serverless SQL endpoints that are fully managed by Databricks and offer instant computing.
While computational resources for the majority of other Databricks calculations are shared inside the cloud account or traditional data plane, these resources are shared in a Serverless data plane.
The architecture of Databricks is made up of several important parts:
- Databricks Delta Lake
- Databricks Delta Engine
Both semi-structured and structured files can be saved and uploaded using Snowflake without the need for an ETL tool to first arrange the data before importing it into the EDW.
Snowflake instantly converts the data to its own internal, organized format when the data is submitted. In contrast to a Data Lake, Snowflake does not need you to provide structure to your unstructured data before you can load and interact with it.
The data types can all be used with Databricks in their original format. To give your unstructured data structure so that it can be used by other tools like Snowflake, you can even utilize Databricks as an ETL tool.
In the debate between Databricks and Snowflake, Databricks prevails over Snowflake in terms of Data Structure.
Processing and storage layers are separated in Snowflake, allowing them to grow independently on the cloud. This indicates that they can all scale independently in the Cloud based on your requirements.
Your finances will benefit from this. Additionally, both layers’ ownership is kept. Snowflake secures access to data and machine resources using the role-based access control (RBAC) technique.
The data processing and storage layers of Databricks are completely decoupled, in contrast to the decoupled layers in Snowflake.
Users can put their data wherever in any format, and Databricks will handle it effectively because its primary goal is data application.
Databricks is the clear winner in the debate between Databricks and Snowflake since you can simply use it to process the data.
Time Travel and Fail-safe are two special characteristics of Snowflake. The Time Travel function of Snowflake keeps data in a state before an update.
While Enterprise clients can choose a time range of up to 90 days, Time Travel is often restricted to one day. Databases, schemas, and tables can all use this capability.
When the Time Travel retention term expires, a 7-day fail-safe period begins, which is designed to safeguard and restore previous data.
Databricks Similar to how Snowflake’s Time Travel feature operates, Delta Lake’s does as well. Data kept in Delta Lake is automatically versioned, allowing users to retrieve earlier data versions for future usage.
Databricks runs on Spark, and since Spark is built on object-level storage, Databricks never really store any data.
This is one of its main advantages. This also implies that Databricks might handle use cases for on-premise systems.
All data is automatically encrypted at rest within Snowflake.
All communications between the control plane and data plane occur within the private network of the cloud provider, and all data saved within Databricks is secured.
Both options offer RBAC (role-based access control). Snowflake and Databricks adhere to several laws and certifications, including SOC 2 Type II, ISO 27001, HIPAA, and GDPR.
However, as Databricks operates on top of object-level storage like AWS S3, Azure Blob Storage, Google Cloud Storage, etc., it lacks a storage layer in contrast to Snowflake.
In terms of performance, Snowflake and Databricks are such radically dissimilar solutions that it is quite challenging to compare them.
It is possible to modify each benchmark to present a slightly different tale. A perfect example of this is the recent study conducted by Databricks about the TPC-DS benchmark.
In terms of a head-to-head comparison, Snowflake and Databricks support slightly different use cases, and none is inherently superior to the other.
Snowflake, however, might be a preferable option for interactive queries since it optimizes all storage for data access at the moment of ingestion.
BI and SQL use cases are well-supported by Databricks and Snowflake.
Snowflake provides JDBC and ODBC drivers that are simple to integrate with other software.
Given that customers don’t have to administer the program, it is mostly renowned for its use-cases in BI and for businesses choosing a straightforward analytical platform.
The open-source Delta Lake that Databricks has released adds an additional layer of stability to their Data Lake in the meanwhile. Customers can send SQL queries to Delta Lake with great performance.
Given their variety and superior technology, Databricks is well renowned for their use-cases that minimize vendor lock-in, are better suited for ML workloads, and assist tech giants.
Customers have access to four enterprise-level views with Snowflake. Standard, Enterprise, Business Critical, and Virtual Private Snowflake are the four versions available. The whole price information is available here.
On the other hand, the three commercial price tiers offered by Databricks are basic, premium, and enterprise. You can view the entire price list right here.
Excellent data analysis tools include Snowflake and Databricks.
There are benefits and drawbacks to each. Usage patterns, data volumes, workloads, and data strategy all come into play when deciding which platform is ideal for your business.
Snowflake is better suited for those who are experienced with SQL and for typical data transformation and analysis.
Streaming, ML, AI, and data science workloads are better suited for Databricks because of its Spark engine, which supports the usage of numerous languages.
In order to catch up with other languages, Snowflake has introduced support for Python, Java, and Scala.
Some claim that Snowflake minimizes storage during intake, so it is superior for interactive queries.
Additionally, it is excellent at producing reports and dashboards and managing BI workloads. In terms of a data warehouse, it performs well.
However, some users have noted that it suffers with large data quantities, such as those seen in streaming applications. Snowflake triumphs in a direct competition based on data warehousing skills.
However, Databricks isn’t actually a data warehouse. Its data platform is more comprehensive and has superior ELT, data science, and machine learning capabilities to Snowflake.
Users don’t control the cost of managed object storage where they store their data. The data lake and data processing are the main topics.
However, it is specifically targeted at data scientists and extremely skilled analysts.
In conclusion, Databricks triumphs for a technical audience. Both technically savvy and non-technically savvy users can easily utilize Snowflake.
Almost all of the data management features that Snowflake offers are available through Databricks and a lot more. But it is more difficult to operate, involves a high learning curve, and needs more upkeep.
However, it can handle a far larger range of data workloads and languages. And those who are familiar with Apache Spark will lean toward Databricks.
Snowflake is better suited for customers who want to quickly install a good data warehouse and analytics platform without getting bogged down in setups, data science details, or manual setup.
This is also not to claim that Snowflake is a simple tool or for new users. Not at all.
It isn’t as high-end as Databricks; that platform is more suited for complicated data engineering, ETL, data science, and streaming applications.
Snowflake is a data warehouse for analytics that stores production data. Additionally, it is beneficial for individuals who wish to start small and ramp up gradually as well as for novices.