Why we chose Databricks over Snowflake?
Total Cost of Ownership / Cost
Snowflake is the easier plug-and-play cloud data warehouse while Databricks enables custom big data processing. For a unified analytics platform with end-to-end ML capabilities, Databricks is the better choice.
Databricks makes it easy for new users to get started on the platform. It removes many of the burdens and concerns of working with cloud infrastructure, without limiting the customizations and control experienced data, operations, and security teams require.
It allows its users to build, deploy, share, and maintain to-class data, analytics. It also provides best in class AI solutions. This platform integrates with cloud storage and security. After that Databricks manages and deploys your cloud infrastructure without your manual interference.
- AWS Redshift. ...
- BigQuery. ...
- Azure Synapse. ...
- Databricks Lakehouse. ...
- Dremio. ...
- ClickHouse. ...
Snowflake has a market share of 18.33%. Databricks, on the other hand, has a market share of 8.67%. Snowflake is the most efficient for SQL and ETL operations. Databricks is ideally suited for use cases involving Data Science/Machine Learning and Analytics.
For data analysts and business intelligence professionals, Databricks also offers Databricks SQL. This is an interface and engine that looks and feels like a database or data warehouse interactive development environment.
Enterprises are accumulating massive quantities of data, but the big data analysis process in itself brings many barriers, ranging from infrastructure management needs to provisioning bottlenecks to high costs of acquisition and management. Databricks is designed to remove all these hurdles.
Databricks offers a comprehensive solution for data-driven organisations and offers superior performance in: ETL workloads; • processing different data types; • cataloguing and lineage; • AI/ML ecosystem integration; and • real-time data processing.
Data can be Extracted, Transformed, and Loaded (ETL) from one source to another using an ETL tool. Azure Databricks ETL provides capabilities to transform data using different operations like join, parse, pivot rank, and filter into Azure Synapse.
What is Databricks secret?
A secret is a key-value pair that stores secret material, with a key name unique within a secret scope. Each scope is limited to 1000 secrets. The maximum allowed secret value size is 128 KB. See also the Secrets API.
ETL (Extract, Transform, and Load) is a Data Engineering process that involves extracting data from various sources, transforming it into a specific format, and loading it to a centralized location (majorly a Data Warehouse). One of the best ETL Pipelines is provided by Databricks ETL.
In simple terms, Databricks cost is based on how much data you process, and the type of workload you're executing and which product you're using. Each type of compute has a different price per processing unit—known as Databricks unit, or DBU.
What are the cons of using Snowflake? Some potential drawbacks of using Snowflake include: Limited support for unstructured data: Designed primarily for structured and semi-structured data. Dependency on cloud providers: Reliant on underlying cloud platforms for infrastructure and availability.
Why choose Snowflake over its competitors? One of Snowflake's main advantages is that it is multi-cloud. It's available on all major cloud platforms: Azure, AWS, and GCP. Companies that operate in multi-cloud environments can query their Snowflake data directly from any of these platforms.
Snowflake pricing is based on compute, storage, and cloud services usage. Warehouses are available in sizes x-small to 6X-large, with each tier doubling in cost and compute power.
What is Azure Databricks? A Platform as a Service (PaaS) that provides a unified data analysis system to organizations. Cloud-based big data solution used for processing and transforming massive quantities of data.
The Databricks Runtime implements the open Apache Spark with a highly optimized execution engine, which provides significant performance gains compared to standard open source Apache Spark found on cloud platforms.
Databricks provides a Snowflake connector in the Databricks Runtime to support reading and writing data from Snowflake. You may prefer Lakehouse Federation for managing queries on Snowflake data. See Run queries using Lakehouse Federation.
Databricks is an AWS Partner.
Which language is best for Databricks?
Spark-SQL and Java/Scala (built on the JVM) will consistently outperform Python and R in the Spark environment in terms of speed and performance.
NET applications; Databricks uses the optimized version of Apache Spark, allowing its users to use GPU-enabled clusters for their ML workloads, offering much better performance than Azure. Hence, workloads requiring fast training and inference on performing data will benefit from using Databricks.
Databricks SQL provides general compute resources that are executed against the tables in the lakehouse. Databricks SQL is powered by SQL warehouses, offering scalable SQL compute resources decoupled from storage.
Unlike the Databricks Free Trial, Community Edition doesn't require that you have your own cloud account or supply cloud compute or storage resources. However, several features available in the Databricks Platform Free Trial, such as the REST API, are not available in Databricks Community Edition.
In summary, Databricks wins for a technical audience, and Amazon wins for a less technically gifted user base. Databricks provides pretty much of the data management functionality offered by AWS Redshift. But, it isn't as easy to use, has a steep learning curve, and requires plenty of maintenance.