Which ETL tool is best for Snowflake?

The unification of data from multiple sources into one place requires both an ETL (Extract, Transform, Load) pipeline and a data storage solution, which is typically cloud-based.

Snowflake is a modern data warehouse, founded in 2014, which has risen to become one of the world’s leading enterprise solutions. It includes automated maintenance and the ability to scale in near real-time, plus flexible pricing based on the options you choose.

If you’re already planning to use Snowflake for your data storage, then you need to consider the best ETL tool for transferring your data into it.

Which tool is best for ETL?

ETL tools are software platforms for building your ETL pipeline, and you should consider the different types. Open-source tools are free and can be altered to fit your particular needs, but come with no guarantee or technical support. On the other hand, enterprise tools are usually expensive but include commercial support. The final option is to build your own custom ETL software, which would provide the ultimate flexibility and be bespoke to your needs. However, this would also require a great deal of internal resources, such as testing, maintenance, and updates.

An example of the commercial option would be Microsoft’s ETL tools, namely Azure Data Factory and SSIS (SQL Server Integration Service). When companies build their own ETL tools, Python is usually the language of choice due to its ability to easily create and manage data structures.

The traditional approach would have been to use local servers, but in the modern world many companies now use cloud ETL tools, which provide greater scalability and availability. When choosing the best ETL tools, here are the top ten features you should look for:

1. Excellent credential management – Different sources often require different credentials to access the data, so these will need to be accessible, secured, and easily manageable.
2. Comprehensive integrations – To process all of the necessary data, the tool should be compatible with every API you need. Data connectors are needed to move data between databases and convert data into different formats.
3. Automation & scheduling – ETL can involve hundreds of integration jobs each day, so you should automate as much as possible to save time. Moreover, these jobs will often be intricately linked, so scheduling control is needed to organize the pipeline.
4. Performance quality – The quality of your tool consists of three main elements: accuracy, stability, and speed. Accuracy is the minimization of errors, stability is how consistent the tool behaves without crashing, and speed is how fast it performs operations. Good parallel processing is crucial because this enables different operations to run simultaneously on multiple nodes. Pushdown optimization is another feature to look for, since it allows data transformation jobs to be moved into a relational database when needed.
5. Both on-premises and cloud-based – Ideally, you would have the option to deploy your ETL tool either on your own server or in the cloud. The tool should also have the ability to virtualize the software and the operating system on which it runs, so as to minimize resources, and help with patching and scaling.
6. Built-in data profiling – This means examining the source datasets to determine structure, quality, and integrity. Additionally, your tool should support custom data quality rules so it can automatically identify missing or invalid records.
7. Data governance – This refers to the adherence to the collection of processes and policies in an organization which ensure the quality and security of the data used. The software should also support custom data quality rules so that the system can automatically identify missing or invalid records.
8. Security – Your datasets should be encrypted with gated access, especially if they contain sensitive information.
9. Monitoring & alerting – ETL pipelines may fail for a number of reasons during operation, so an ETL tool needs to monitor activity and alert you immediately when there’s a failure.
10. Ease of use – An intuitive interface and ease of use are always beneficial.

Does Snowflake use ETL or ELT

Snowflake supports transformation both before and after loading, so you can use either ETL or ELT. The platform works with a wide range of data integration tools, so you should find the right support once you’ve chosen the best ETL tool for Snowflake. You can check the compatibility of Snowflake ETL tools here.

Is Snowflake a data integration tool?

While Snowflake is an excellent cloud-based data warehouse, it can’t perform data integration by itself. For this, you need a separate tool. Ideally, this would be a single platform which takes care of your entire data unification needs.