This blog explains Databricks vs Snowflake and how they fit into the broader category of data engineering. It also gives a quick overview of Snowflake and Databricks before delving into their differences.
Databricks is an Enterprise Software firm formed by Apache Spark’s founders. In a Lakehouse Architecture, it is known for integrating the finest of Data Lakes and Data Warehouses. Snowflake is a data warehousing company that offers cloud-based access and storage services. It establishes its reputation as a service that requires almost no upkeep to enable secure access to your data.
What is Databricks?
Databricks is an Apache Spark-powered cloud-based data platform. The focus is mostly on Big Data Analytics and Collaboration. You may get a comprehensive Data Science workspace for Business Analysts, Data Scientists, and Data Engineers to interact using Databricks’ Machine Learning Runtime, controlled ML Flow, and Collaborative Notebooks. The Dataframes and Spark SQL libraries, which allow you to deal with structured data, are housed in Databricks.
You can simply acquire insights from your existing data using Databricks, and it can also help you construct Artificial Intelligence solutions. Tensorflow, Pytorch, and other Machine Learning libraries are included in Databricks for training and constructing Machine Learning Models. Databricks is used by a variety of enterprise customers to run large-scale production operations across a wide range of use cases and industries, including healthcare, media and entertainment, finance, retail, and much more.
Source: databricks
Key Features of Databricks
Because of its capacity to transform and handle enormous amounts of data, Databricks has established itself as an industry-leading solution for Data Analysts and Data Scientists. Here are a handful of Databricks’ important features:
1) Optimized Spark Engine: Databricks give you access to the most recent Apache Spark versions. Databricks may also be easily integrated with a variety of open-source libraries. You can quickly set up clusters and establish a fully managed Apache Spark environment using the availability and scalability of several Cloud service providers. Databricks enable you to configure, set up, and fine-tune clusters without the need to monitor them for maximum performance and stability.
2) Delta Lake: Databricks contains an open-source transactional storage layer that may be utilized throughout the data lifecycle. This layer can be used to add Data Scalability and Reliability to an existing Data Lake.
3) Collaborative Notebooks: You can instantly analyze and access your data, collaboratively construct models, and uncover and share new actionable insights with the tools and language of your choosing. You may use Databricks to code in whatever language you like, including Scala, R, SQL, and Python.
4) Machine Learning: With the support of cutting-edge frameworks like Tensorflow, Scikit-Learn, and Pytorch, Databricks provides one-click access to preconfigured Machine Learning environments. You can share and track experiments, collaborate on model management, and replicate runs all from one location.
What is Snowflake?
Snowflake is a fully managed service that allows customers to load, connect, analyze, and securely share their data with near-infinite scalability of concurrent processes. Data Lakes, Data Engineering, Data Application Development, Data Science, and secure data consumption are some of its most prevalent uses.
Snowflake’s architecture is notable for naturally separating computation and storage. This design allows you to give your users and data workloads virtual access to a single copy of your data without sacrificing speed. Snowflake allows you to run your data solution across multiple locations and Clouds for a consistent experience. Snowflake makes this possible by abstracting the complexity of the underlying Cloud infrastructure.
Source: snowflake
Key Features of Snowflake
Here are some of the benefits of using Snowflake as a Software as a Service (SaaS) solution:
1) Improved Data-Driven Decision Making: Snowflake allows you to break down data silos and provide everyone in your organization access to meaningful insights.
2) Accelerate Quality of Analytics and Speed: By switching from nightly batch loads to real-time data streams, Snowflake allows you to boost your Analytics Pipeline. You can improve the quality of analytics at your company by allowing safe, concurrent, and controlled access to your Data Warehouse across your organization. This enables businesses to optimize resource distribution in order to maximize revenue while reducing costs and manual effort.
3) Customized Data Exchange: Snowflake enables you to create a Data Exchange that allows you to securely communicate live, regulated data. It also encourages you to strengthen data relationships across your business units, as well as with partners and customers. This is accomplished by obtaining a 360-degree perspective of your consumer, which provides information on essential customer characteristics such as interests, occupation, and many more.
4) Improved User Experiences and Product Offerings: You can better understand user behavior and product usage with Snowflake in place. You can also use the full range of data to ensure customer satisfaction, significantly improve product offerings, and foster Data Science innovation.
5) Robust Security: You can use a safe Data Lake to store all compliance and cybersecurity data in one place. Snowflake Data Lakes ensure quick incident response times. This helps you to get a full picture of an occurrence by combining large amounts of log data into a single spot and quickly reviewing years of log data. Semi-structured logs and structured enterprise data can now be combined in a single Data Lake. Snowflake allows you to get your foot in the door without having to index your data and then manipulate and transform it once it’s there.
Databricks vs Snowflake – Key Differences
The following are the main differences between Databricks and Snowflake:
1) Data structure
Snowflake, unlike EDW 1.0 and comparable to a Data Lake, allows you to save and upload both semi-structured and structured files without first organizing the data with an ETL tool before loading it into the EDW. Snowflake will automatically turn the data into its internal organized format once it has been uploaded. Unlike a Data Lake, Snowflake does not require you to provide structure to your unstructured data before you can load and interact with it.
Databricks, on the other hand, can operate with any data type in its native format. Databricks may also be used as an ETL tool to arrange unstructured data so that other programs, such as Snowflake, can work with it. As a result, when it comes to Data Structure, Databricks trumps Snowflake in the Databricks vs Snowflake debate.
2) Versatility
Snowflake is suitable for SQL-based Business Intelligence scenarios. You’ll almost certainly have to rely on their partner ecosystem to work on Machine Learning and Data Science use cases with Snowflake data. Snowflake, like Databricks, has JDBC and ODBC drivers for connecting to third-party platforms. These partners would very likely extract Snowflake data and process it using a processing engine other than Snowflake, such as Apache Spark, before returning the findings to Snowflake.
For Business Intelligence use cases, Databricks also facilitates the execution of high-performance SQL queries. Open-source Delta Lake was created by Databricks as a layer that increases dependability to Data Lake 1.0. You can now send SQL queries to an EDW with high-performance levels that were previously reserved for SQL queries using Databricks Delta Engine on top of Delta Lake.
3) Performance
Snowflake does not have any hash integrations, however Databricks does. Cost-based optimization and vectorization are implemented in both Databricks and Snowflake. Databricks delivers powerful Continuous and Batch Ingestion with Versioning in terms of performance. Snowflake, on the other hand, focuses on batches.
4) Scalability
Databricks and Snowflake both have a lot of write scalability. In terms of individual query scalability, Databricks auto scales based on load, whereas Snowflake provides 1-click cluster resizing with no node size choice.
5) Security
Databricks provides separate customer keys, comprehensive RBAC for clusters, tasks, pools, and table-level data security. Snowflake, on the other hand, gives each consumer their own key (only VPS is isolated tenant, RBAC, Encryption at rest).
6) Integration Support
As Cloud Infrastructures, Databricks and Snowflake both support Azure, Google Cloud, and AWS.
7) Architecture
Both Databricks and Snowflake offer their consumers elasticity in terms of processing and storage separation. Databricks only allows you to query Delta Lake tables in terms of writable storage, whereas Snowflake only supports external tables.
8) Pricing
Snowflake offers four enterprise-level viewpoints to its customers. Premium, Basic, Enterprise, and Professional are the four editions available. Databricks, on the other hand, has three commercial price categories for its subscribers: Business Intelligence workloads, Data Science workloads, and corporate plans.
Conclusion
After a brief introduction to the core features of Databricks and Snowflake, this blog goes into great detail on Databricks vs Snowflake.
Related/References
- Microsoft Certified Azure Data Engineer Associate | DP 203 | Step By Step Activity Guides (Hands-On Labs)
- Exam DP-203: Data Engineering on Microsoft Azure
- Azure Data Lake For Beginners: All you Need To Know
- Batch Processing Vs Stream Processing: All you Need To Know
- Introduction to Big Data and Big Data Architectures
Next Task For You
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