In the past, businesses had two main options for storing and analyzing their data: data warehouses and data lakes. Data warehouses were designed for structured data, while data lakes were designed for unstructured data. However, as the amount of data that businesses generate has grown exponentially, the need for a solution that can handle both structured and unstructured data has become increasingly important.
This is where data lakehouses come in. Data lakehouses are a new breed of data platform that combines the best features of data warehouses and data lakes. They offer the scalability, performance, and security of a data warehouse, as well as the flexibility and cost-effectiveness of a data lake.
Two of the leading data lakehouse platforms are Snowflake and Databricks. Both platforms offer a wide range of features and capabilities, making them a good choice for a variety of use cases. However, there are also some key differences between the two platforms.
Snowflake is a fully managed data warehouse-as-a-service (DWaaS) platform. This means that Snowflake takes care of all the underlying infrastructure, so you don’t have to worry about managing servers, storage, or networking. They do provide ELT support mainly through its COPY command and dedicated schema and file object definition. In General, think of it as a cluster of data bases which provides basic ELT support. They go by the Extract, Load, Transform (ELT) way of data engineering. However, they provide good support with the existing 3rd party Extract, Transform, Load (ETL) tools such as Fivetran, Talend, etc etc. You can even install DBT with it. Snowflake also offers a wide range of features and capabilities, including:
- Scalability: Snowflake can scale up or down to meet your changing needs.
- Performance: Snowflake offers high performance for both OLTP and OLAP workloads.
- Security: Snowflake is highly secure, with features like encryption at rest and in transit.
- Cost-effectiveness: Snowflake is a cost-effective solution, with pricing that is based on usage.
Databricks is a unified analytics platform that combines the best features of data warehouses and data lakes. The main functionality of Data Bricks is its processing power. It integrates the core functionality of spark and is very good for ETL loads. Their storage is what they call a data lakehouse, which is a data lake but has the functionality of a relational database. Basically, is a data lake but you can run SQL on it, which is quite popular lately using schema on read tactic. Databricks offers a wide range of features and capabilities, including:
- Unified analytics: Databricks allows you to analyze all your data, regardless of its structure, in a single platform.
- Speed: Databricks is designed to be fast, with features like in-memory analytics and columnar storage.
- Flexibility: Databricks is a flexible platform that can be used for a variety of use cases, including data science, machine learning, and analytics.
- Cost-effectiveness: Databricks is a cost-effective solution, with pricing that is based on usage.
Which platform is right for you?
So, which platform is right for you? The answer depends on your specific needs and requirements. If you are looking for a fully managed DWaaS platform with high performance and security, then Snowflake is a good choice. If you are looking for a more flexible platform that can be used for a variety of use cases, then Databricks is a good choice.
If you have an existing ETL tool such as Fivetran, Talend, Tibco, etc., go for Snowflake, you only need to worry about how to load your data in. The database partitioning, scaling, and indexes (basically all the database infrastructure) are being handled for you.
If you don’t have an existing ETL tool and your data requires intensive cleaning and have unpredictable data sources and schema, go for Databricks. Leverage the schema-on-read technique to scale your data.
Here are some additional factors to consider when choosing between Snowflake and Databricks:
- Your budget: Snowflake is generally more expensive than Databricks.
- Your technical expertise: Snowflake is a more complex platform to use than Databricks.
- Your data volume: Snowflake is better suited for larger data volumes than Databricks.
- Your use cases: Snowflake is better suited for OLTP workloads than Databricks. Databricks is better suited for data science and machine learning workloads than Snowflake.
Snowflake and Databricks are both leading data lakehouse platforms that offer a wide range of features and capabilities. The best platform for you will depend on your specific needs and requirements.
As far as Snowflake vs Databricks, the biggest difference is that Snowflake stores the data in a proprietary format inside their own servers and uses their own servers for compute costs, so there isn’t that provisioning stage that takes 5 minutes.
Databricks uses mostly open-source software and utilizes cloud companies’ computing and storage costs.
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