Databricks vs Snowflake: 9 Critical Differences

Databricks is an Enterprise Software company that was founded by the creators of Apache Spark. It is known for combining the best of Data Lakes and Data Warehouses in a Lakehouse Architecture. Snowflake is a Data Warehousing company that provides seamless access and storage facilities across Clouds. It cements its authority as a service that requires near-zero maintenance to provide secure access to your data.

This blog talks about Snowflake vs Databricks in great detail. It also gives a brief introduction to Snowflake and Databricks before diving into the differences between the two.

What is Snowflake?

Snowflake is a fully managed service that provides customers with near-infinite scalability of concurrent workloads to effortlessly integrate, load, analyze, and securely share their data. Its common applications include Data Lakes, Data Engineering, Data Application Development, Data Science, and secure consumption of shared data. 

Snowflake’s unique architecture natively separates out computing and storage. This architecture enables you to virtually enable your users and data workloads to access a single copy of your data without any detrimental effect on performance. With Snowflake, you can seamlessly run your data solution across multiple regions and Clouds for a consistent experience. Snowflake makes it possible by abstracting the complexity of underlying Cloud infrastructures.

Snowflake also allows you to access shared datasets and data services via the Snowflake Data Marketplace which provides ample opportunities to connect with thousands of Snowflake customers.

Key Features of Snowflake

Here are a few features of Snowflake as, a Software as a Service (SaaS) offering:

• Accelerate Quality of Analytics and Speed: Snowflake allows you to empower your Analytics Pipeline by shifting from nightly batch loads to real-time data streams. You can accelerate the quality of analytics at your workplace by granting secure, concurrent, and governed access to your Data Warehouse across the organization. This allows organizations to optimize the distribution of resources to maximize revenue by saving on costs and manual effort.
• Improved Data-Driven Decision Making: Snowflake allows you to break down Data Silos and provide access to actionable insights across the organization. This is an essential first step to improving partner relationships, optimizing pricing, reducing operational costs, driving sales effectiveness, and much more. 
• Improved User Experiences and Product Offerings: With Snowflake in place, you can better understand user behavior and product usage. You can also leverage the full breadth of data to deliver customer success, vastly improve product offerings, and encourage Data Science innovation.  
• Customized Data Exchange: Snowflake allows you to build your Data Exchange which lets you securely share live, governed data. It also provides an incentive to build better data relationships across your business units and with your partners and customers. It does this by achieving a 360-degree view of your customer, which provides insight into key customer attributes like interests, employment, and many more. 
• Robust Security: You can adopt a secure Data Lake as a single place for all compliance and cybersecurity data. Snowflake Data Lakes guarantee a fast incident response. This allows you to understand the complete picture of an incident by clubbing high-volume log data in a single location, and efficiently analyzing years of log data in seconds. You can now join Semi-structured Logs and Structured Enterprise Data in one Data Lake. Snowflake lets you put your foot in the door without any indexing and easily manipulate and transform data once it is in Snowflake.  

Snowflake allows Data Scientists and Data Analysts to experiment and make new connections without breaking down the core activities. This is a crucial benefit for numerous verticals such as retail where timely information is imperative for success.  

What is Databricks?

Databricks is a Cloud-based data platform powered by Apache Spark. It primarily focuses on Big Data Analytics and Collaboration. With Databricks’ Machine Learning Runtime, managed ML Flow, and Collaborative Notebooks, you can avail a complete Data Science workspace for Business Analysts, Data Scientists, and Data Engineers to collaborate. Databricks houses the Dataframes and Spark SQL libraries, that allow you to interact with structured data.

With Databricks, you can easily gain insights from your existing data while also assisting you in the development of Artificial Intelligence solutions. Databricks also include Machine Learning libraries for training and creating Machine Learning Models, such as Tensorflow, Pytorch, and many more. Various enterprise customers use Databricks to conduct large-scale production operations across a vast multitude of use cases and industries, including Healthcare, Media and Entertainment, Financial Services, Retail, and so much more.   

Key Features of Databricks

Databricks has carved a name for itself as an industry-leading solution for Data Analysts and Data Scientists due to its ability to transform and handle large amounts of data. Here are a few key features of Databricks:

• Delta Lake: Databricks houses an Open-source transactional storage layer meant to be used for the whole data lifecycle. You can use this layer to bring Data Scalability and Reliability to your existing Data Lake.
• Optimized Spark Engine: Databricks allows you to avail the most recent versions of Apache Spark. You can also effortlessly integrate various Open-source libraries with Databricks. Armed with the availability and scalability of multiple Cloud service providers, you can easily set up clusters and build a fully managed Apache Spark environment. Databricks allow you to configure, set up, and fine-tune clusters without having to monitor them to ensure peak performance and reliability. 
• Machine Learning: Databricks offers you one-click access to preconfigure Machine Learning environments with the help of cutting-edge frameworks like Tensorflow, Scikit-Learn, and Pytorch. From a central repository, you can share and track experiments, manage models collaboratively, and reproduce runs. 
• Collaborative Notebooks: Armed with the tools and the language of your choice, you can instantly analyze and access your data, collectively build models, and discover and share new actionable insights. Databricks allows you to code in any language of your choice including Scala, R, SQL, and Python.

What is the EDW 1.0 Story?

The EDW (Enterprise Data Warehouse) was first introduced in the 1980s as data became more available. Businesses began to depend more heavily on data to make business-critical decisions. They needed to consolidate and organize their data in a central place. Vendors like Teradata, Oracle, and IBM came up with a handy solution to meet this need. They sold customers On-premise EDW Systems (Software+Hardware) that were capable of both processing and storing your data.  

3 Key EDW Characteristics 1.0

Here are a few key characteristics of EDW 1.0:

• Centralized Processing and Storage: Data was processed and stored in gigantic tables. Some systems made various copies of these large tables to speed up processing in a “parallel” architecture.
• Expensive: These systems were built for business-critical applications that cannot go down. They also required high-performance levels for processing SQL queries. To achieve all the aforementioned goals, they built them with premium, expensive hardware.
• Structured Data: The traditional EDW only stored data that was “structured” into tables or organized with a “schema” of columns and rows, similar to what can be seen in Excel. This structure helps you quickly analyze and access your data with SQL queries. Therefore, you needed an ETL (Extract, Transform, Load) solution to extract your data, organize it, and load it into your EDW.

EDW 1.0 was an effective solution for a while until the world changed. As businesses began to acquire data at a greater volume, speed, and variety, these enterprises failed to organize their data quickly enough to make it useful in the EDW (Enterprise Data Warehouse). Thus, the need for a new Data Processing and Storage solution that was more flexible became more prominent.

What is the Data Lake 1.0 Story?

Data Lake solutions came to the fore in 2006 within the leading technology companies, Yahoo and Google, out of sheer necessity as they were acquiring data faster than everyone else. Next, they Open-sourced these early systems and provided them to the world at no cost to use and improve as they saw fit to meet their specific needs. This is how the industry-leading Data Lake ecosystem, Apache Hadoop came into being.

However, Hadoop was not suitable for most enterprises, right off the bat. Most enterprises also needed a vendor to reliably support these systems. As a result, vendors like Cloudera, Pivotal, Hortonworks, and others emerged to build fully supported Data Lake offerings for the enterprise solutions built around the Apache Hadoop Open-source core.

3 Key Data Lake Characteristics 1.0

Here are the top 3 characteristics of Data Lake 1.0:

• Decentralized Processing and Storage: As opposed to storing data in large tables, data was split up into various smaller “distributed” files or tables that you could store in the Open-source Hadoop Distributed File System (HDFS). These distributed files were stored on various affordable connected computers. Only the computer, or the “node” that stored the specific data you wanted to work with processed the data. This method was more efficient as compared to working with huge centralized files or tables. Here, the MapReduce programming model was used to process your data on the computer disk where it was stored. 
• Cost-Effective: Opposed to working with expensive hardware that would fail less often, HDFS was designed keeping in mind the assumption that hardware would fail, and to automatically handle those failures. This meant that you could now store data on low-cost computers that worked together to provide the same reliability as the EDW. With traditional EDW solutions, businesses had to purchase way more expensive capacity up-front instead of what they needed at the moment to plan for the future. This is in sharp contrast to Data Lake solutions that enabled businesses to simply purchase additional expensive hardware as and when needed. 
• Unstructured Data: Data Lakes were designed to store data in its original, or “unstructured” format. You didn’t require an ETL system to structure your data before saving and loading it in the Data Lake.

Databricks Lakehouse vs Snowflake Cloud Data Platform

Here are the key differences between Databricks vs Snowflake:

• Data Ownership
• Data Structure
• Use Case Versatility
• Performance
• Scalability
• Security
• Integration Support
• Architecture
• Pricing

Data Ownership

Compared to EDW 1.0, Snowflake has decoupled the processing and storage layers. This means that they can scale each independently in the Cloud according to your needs. This will help you save money. As you’ve seen, you’re processing less than half of the data that you store. Similar to the Legacy EDW, Snowflake does not decouple Data Ownership. It still retains ownership of both the Data Processing and Data Storage layers.

On the other hand, with Databricks, Data Processing and Data Storage layers are fully decoupled. Databricks focuses primarily on the Data Application and Data Processing layers. You can leave your data wherever it is (even On-premise), in any format. You can easily use Databricks to process it which puts Databricks on top in the discussion of Databricks vs Snowflake. 

Data Structure

As opposed to EDW 1.0 and similar to a Data Lake, Snowflake allows you to save and upload both Semi-structured and Structured files without using an ETL tool to first organize the data before loading it into the EDW. Once uploaded, Snowflake will automatically transform the data into its internal structured format. Snowflake, however, does not need you to add structure to your Unstructured data before you can load and work with it, unlike a Data Lake.

On the other hand, Databricks can work with all the data types in their original format. You can even use Databricks as an ETL tool to add structure to your Unstructured data so that other tools like Snowflake can work with it. Therefore, in terms of Data Structure, Databricks trumps Snowflake in the discussion for Databricks vs Snowflake.

Use Case Versatility

Snowflake is best suited for SQL-based, Business Intelligence use cases. To work on Machine Learning and Data Science use cases with Snowflake data, you will likely have to rely on their partner ecosystem. Like Databricks, Snowflake provides JDBC and ODBC drivers to integrate with third-party platforms. These partners would likely pull Snowflake data and use a processing engine outside of Snowflake, like Apache Spark, before sending results back to Snowflake.

Databricks also allow the execution of high-performance SQL queries for Business Intelligence use cases. Databricks developed Open-source Delta Lake as a layer that adds reliability on top of the Data Lake 1.0. With Databricks Delta Engine on top of Delta Lake, you can now submit SQL queries with high-performance levels that were previously reserved for SQL queries to an EDW.  

Performance

In terms of indexing capabilities, Databricks offers hash integrations whereas Snowflake offers none. Both Databricks and Snowflake implement cost-based optimization and vectorization. In terms of Ingestion performance, Databricks provides strong Continuous and Batch Ingestion with Versioning. Snowflake, on the other hand, is Batch-centric.

Scalability

Both Databricks and Snowflake offer strong write Scalability. In terms of individual query scalability, autoscaling is based on the load in Databricks, whereas Snowflake allows 1-click cluster resize with no choice of node size.

Security

In terms of Data Security, Databricks offers separate customer keys, and complete RBAC for clusters, jobs, pools, and table-level. Snowflake, on the other hand, provides separate customer keys (only VPS is isolated tenant, RBAC, Encryption at rest).

Integration Support

Both Databricks and Snowflake support Azure, Google Cloud, and AWS as Cloud Infrastructures.

Architecture

Both Databricks and Snowflake provide their users with elasticity, in terms of separation of computing and storage. In terms of writable storage, Databricks only allows you to query Delta Lake tables whereas Snowflake only supports external tables.

Pricing

Snowflake provides customers with four enterprise-level perspectives. There are four editions: Premium, Basic, Enterprise, and Professional. Databricks, on the other hand, offers 3 business price tiers to its subscribers: those for Business Intelligence workloads, those for Data Science workloads, and those for corporate plans.

Databricks Lakehouse vs Snowflake: Where Should You Put Your Data?

According to Data Scientists, the best way to predict the future is to first take a look at similar historical events and their outcomes. You can use the same approach here and consider the fate of EDW versus Data Lake 1.0 to train your Mental Models to help you predict what you may see with Databricks vs Snowflake. This will help you make an educated decision as to where you should put your data.

Databricks

Databricks will continue to acquire new customers for the following 3 primary reasons:

• Minimal Vendor Lock-in: Similar to Data Lake 1.0, Vendor Lock-in is hardly a concern with Databricks, if at all. As a matter of fact, with Databricks you can simply leave your data whenever you want. You can then use Databricks to connect to it and process it for virtually any use case.
• Machine Learning and Data Science: The Databricks platform is better suited to Machine Learning and Data Science workloads as compared to Snowflake. 
• Superior Technology: Until technology giants like Uber, Google, Netflix, and Facebook transition from Open-source to proprietary systems, you can take comfort in the fact that systems based on Open-source, like Databricks will stand superior from a technology perspective. This is because they are far more versatile.

Snowflake

Snowflake would continue to acquire new customers for 3 primary reasons:

• Business Intelligence: Similar to EDW 1.0, Snowflake can be a splendid option for Business Intelligence workloads where it works the best.
• Simplicity: Snowflake is ridiculously simple to use. Similar to EDW 1.0, Snowflake will continue to appeal to the analyst community for this simple reason. In the Cloud, customers no longer have to worry about managing hardware. Plus, with Snowflake, they don’t even have to worry about managing the software either.
• A Superior Alternative to EDW 1.0: This is evident because people no longer want to buy big metal boxes, house them with real estate, and hire people to manage them since this comprises significant overhead. This is why Snowflake trumps the traditional solution.

Conclusion

This blog talks about Databricks vs Snowflake in great detail after giving a brief introduction to the key features of Databricks and Snowflake.

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