Snowflake vs Databricks

Why databricks

Databricks will help to grow customers for three primary reasons:

• Superior technology: Till we see leadership varies like Google, Netflix, Uber, and Facebook transformation from open source to hardware products, you can be confident that open-source systems like Databricks are superior in terms of technology. They are far more adaptable.
• Data science and machine learning: As with Data Lake 1.0 vs EDW 1.0, the Databricks framework is unquestionably ideally suited to data science and machine learning workforces than Snowflake.
• Minimal Vendor Lock-In: As with Data Lake 1.0, vendor lock-in is minimal, if at all, with Databricks. In fact, Databricks allows you to leave your data wherever you want. Connect to it with Databricks and procedure it for practically any use case.

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Key difference between snowflake vs databricks:

Data structure:

Snowflake:Unlike EDW 1.0 and similar to a data lake, Snowflake allows you to upload and save both structured and semi-structured files without first organizing the data with an ETL tool before loading it into the EDW. Snowflake will automatically transform the data into its internal structured format once it has been uploaded. Snowflake, unlike a data lake, does require you to add structure to your unstructured data.

Databricks:Databricks, like Data Lake 1.0, can work with all data types in their original format. In fact, Databricks can be used as an ETL tool to structure unstructured data so that other tools, such as Snowflake, can work with it.

Data Ownership:

Snowflake:Snowflake would then tell users that, in comparison to EDW 1.0, the storage and processing layers have been decoupled. That is, you can scale each independently in the cloud based on your needs. This will save you money because, as we've seen, we only process about half of the data we store. Snowflake, like the legacy EDW, does not decouple data ownership. It still controls both the processing layers and data computation.

Databricks:Databricks, on the other hand, completely decouples the data storage and processing layers. Databricks is more concerned with the data processing and application layers. You can end up leaving your information anywhere it is (including on-premises) and in any layout, and Databricks will process it.

Versability:

Snowflake:Snowflake, like EDW 1.0, is particularly fit for SQL-based, Business Intelligence use cases, where it excels. Working on data science and machine learning use cases with Snowflake data will almost certainly necessitate reliance on their partner ecosystem. Snowflake, like Databricks, provides ODBC and JDBC drivers for integrating with third-party systems.These partners would most likely take Snowflake data and process it using a processing engine other than Snowflake, such as Apache Spark, before returning the results to Snowflake.

Databricks:High-performance SQL queries are also supported by Databricks for Business Intelligence use cases. Databricks developed open-source Delta Lake as an additional layer of dependability on top of Data Lake 1.0. Users now can accept SQL queries to high-performance levels normally reserved for SQL queries to an EDW using Databricks Delta Engine on top of Delta Lake.

Features:

Snowflake:Snowflake comes with database, security features, provides good support, security validations and integrations, etc.

Databricks:The features offered are collaboration, interactive exploration, databricks runtime, job scheduled, dashboards, integrated identity management, auditing, notebook workflows, etc.

Pricing:

Snowflake:Snowflake offers four enterprise based pans for the users. They are standard edition, premier edition, enterprise edition and enterprise edition for sensitive data.

Databricks:Databricks comes with three enterprise pricing options for the users. They are databricks for data engineering workloads, databricks for data analytics workloads, and databricks enterprise plans.

Integrations:

Snowflake: Snowflake can be easily integrated with the following business systems and applications such as looker, AWS, tableau, talend and fivetran, etc.

Databricks: Databricks can be integrated with the following business systems and applications such as looker, Amazon redshift, tableau, talend, pentaho, alteryx, redis, cassandra, MongoDB, etc.

Automation :

Snowflake: In order to automatically manage incoming data in close to real-time, Snowflake uses its “Snowpipe” tool. It uses auto-schema to incoming data, detects sensitive information, and divides it accordingly, resolving a major data categorization issue that businesses encounter while managing their security posture. Snowflake automatically encrypts data in transit and at rest to maintain the security of data transmission, regardless if it is sensitive or not. 

Databricks: Databricks offers automation for complicated data engineering and pipeline activities. The Databricks platform automatically processes incoming data so that it is prepared for analysis. Before it is filtered for useful business insights and distributed among the users, data automatically goes through quality control to weed out inaccuracies.

Collaboration :

Snowflake: Snowflake Data Cloud makes it possible for internal and external stakeholders to securely collaborate across departments, clouds, and geographic locations. To make sure that all are on the same page, all authorised users have access to a live data set, and also they have the option for tailored access to restrict a user's view or access. On top of Snowflake's built-in security capabilities, administrators can control who has access, keep track of usage and access, and manage the publishing workflow.

Databricks: Databricks makes it easier for data engineers, data analysts and data scientists, to collaborate with the option of delta sharing. Information can be shared among various data platforms like Tableau, Power BI, etc.. Additionally, Databricks offers Unity Catalog, a native integration that enables customers to audit and manage data sharing between the organisations.

Data Lake :

Snowflake: Both Data Cloud and Data Lake options exist from Snowflake. An updated, cloud-based data warehouse called Data Cloud provides inexpensive storage with 2 to 3 times data compression. Additionally, Snowflake supports data lake architecture, which automatically collects, organises, and protects the data. It collects data in any format like structured, semi-structured, and unstructured over the clouds and locations. The elastic processing engine of Snowflake makes it possible to analyse and query data quickly and effectively, making a variety of structured data easily accessible for business insights. A pipeline can be developed quickly using SQL, Python, Scala, or another language of choice utilising Snowflake's Snowpark functionality.

Databricks: Databricks' Delta Lake, an open format storage layer unites the structured, semi-structured, streaming and unstructured data formats. Databricks' Delta Lake, which is powered by Apache Spark, enables scalability and performance. Through its Delta Live Tables, which manage data pipelines and keep them updated with fresh data, Delta Lake makes it simpler for data engineers to establish the data lake house foundation.

Machine Learning (ML) : 

Snowflake: Snowflake can be used to create machine learning and analytics, however it doesn't come with these features out of the box and instead needs integration with the other solutions. But it offers drivers that let you access the data and interface with other platform libraries or modules.

Databricks: A strong machine learning environment is available at Databricks for the creation of various models. It also allows programming in a variety of languages, making it simpler to employ libraries and modules.
Although Snowflake doesn't have any ML libraries, it provides connectors to combine different ML tools. Additionally, it makes its storage layer or export query results accessible, which may be employed for testing and training of models.

SQL :

Snowflake: With the ability to handle any scale workload, Snowflake's SQL query function provides seamless data analytics. Users can select multi-cluster computing resources for almost limitless concurrency, and its provision computer clusters meet demand  required for a workload. Snowflake also supports ANSI SQL and semi-structured data, in order to enable the insights users will get through their preferred BI tool. Popular BI/analytics products can be directly connected to using Snowflake's SQL capability. Insights from SQL queries can also be shared through Snowsight, a built-in visualisation tool in Snowflake.

Databricks: Data analysts may easily extract insights using SQL queries that are directly retrieved from the Databricks data lake using SQL on Databricks. The Photon query engine from Databricks enables faster queries at a cheaper cost and without requiring code modification. Through SQL endpoints on Databricks, users may leverage data insights through their preferred BI tool without experiencing any latency. Finally, in order to promote collaboration, save time, and reduce repetition, analysts can store queries and share dashboards that they build.

Scalability :

Both solutions make use of cloud computing to scale faster and with little additional cost. Snowflake is restricted to 128 nodes, whereas Databricks can be scaled to that extent you could afford in the infrastructure. Additionally, Snowflake has alternatives for fixed-sized warehouses, which allow users to easily change the size of clusters but not individual nodes. Moreover, Snowflakes provides auto-suspend and auto-scaling, allowing clusters to begin or end at busy or inactive periods. Contrarily, Databricks allows you to provision numerous node types at various size levels, although it is more difficult than a single click. In order to scale the Databricks cluster, you must possess the required technical knowledge.

Security :

Databricks provides distinct customer keys, full RBAC for clusters, pools, jobs, and table-level, and data security for customers. In contrast, Snowflake offers distinct customer keys.

Architecture :

Due to the decoupling of storage and compute, data lakes' architecture sets them apart from traditional data warehouses. Databricks offers a separate layer for computing and storage, making it easier to scale and use the various processing engines best suited for each use case.
In contrast, Snowflake features a separate storage and processing layer even though it is a managed service and its architecture is visible to end users. Databricks provides you the ability to select the appropriate node despite the fact that node types of Snowflake are unknown.

Use Cases :

The design and architecture of Snowflake make it particularly well suited for SQL-based business analytics use cases. In contrast, Databricks supports a wider range of use cases, including intrusion detection and recommendation engines, and permits SQL-based business intelligence. Additionally, dashboards for reporting and analytics are supported by both solutions.

Any high-volume system can have its throughput requirements met by Databricks, but its query speed for analytics will be subpar. Whereas, Snowflake can outperform Databricks while having limited capabilities for concurrency and continuous writes.

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