Snowflake ETL Best Practices: 10 Rules for Data Engineers

What are the 10 best practices for ETL with Snowflake?

The 10 Snowflake ETL (extract, transform, load) best practices are:

Separating concerns with data staging
Using Snowflake's COPY command
Optimizing table structures
Using No-Code Data Pipelines
Sharing data with Snowflake collaboration
Monitoring and optimizing performance
Monitoring resources
Using Snowpipe
Leveraging table cloning
Querying data where it resides

1. Separating concerns with data staging

Data staging is recommended because it provides a good separation of concerns between the extraction, transformation, and loading (ETL) processes.
Here is a brief explanation of how it works: The staging area acts as a buffer between the data source and the target tables, allowing for data to be transformed and cleaned before being loaded into the target tables.
This workflow can improve the performance and reliability of the ETL process by reducing the complexity of the load process and making it easier to troubleshoot any issues that may arise.
Additionally, it provides a way to handle any data that may fail validation during the ETL process without having an impact on the data in the target tables.

2. Using Snowflake's COPY command

Using the COPY command has multiple benefits when it comes to loading data into the target tables.
Some of the most important ones are performance, scalability and data consistency, and because of these, in most cases of ingesting large amounts of data, this is the most common method.

3. Optimizing table structures

By optimizing the table structure, organizations can improve query performance and reduce costs by reducing the amount of data that needs to be stored and processed. Some of the ways of optimizing tables are:

1. Using appropriate data types - Snowflake supports a wide range of data types, and it is important to choose the appropriate data type for each column to ensure optimal performance which then would help in reducing storage costs.

2. Using clustering keys - Clustering keys determine the physical order of the data in a table which is of benefit when it comes to improving query performance.

3. Using minimal indexes - Snowflake's query optimizer is very efficient and does not require indexes to improve query performance, but if necessary, minimal indexes can be created to improve performance.

4. Using partitioning - Partitioning a table can improve query performance by allowing Snowflake to skip over irrelevant data when running a query.

4. Using No-Code Data Pipelines

No-code data pipelines can be used in order to automate ETL processes.
They can be a powerful tool for managing data in Snowflake, allowing automated and scheduled data loading, as well as transformations and data governance.
This can help to improve data integrity, streamline data analysis and reporting, and of course, increase overall efficiency.

5. Sharing data with Snowflake collaboration

The data-sharing feature provides an easy way of sharing data between different accounts in Snowflake.
This is pretty useful for cases when there is a need of sharing data with external partners or for creating a data lake.

6. Monitoring and optimizing performance

It's a good practice to monitor and optimize ETL performance using Snowflake's performance monitoring and query tuning features, such as Query Profiles, Explain Plan and Query History.

When it comes to performance optimization, it's suggested these best practices to be followed:

Using the appropriate data types and table structure
Creating the appropriate indexes
Using the appropriate warehouse size
Using the appropriate query hints
Leveraging the power of Materialized Views
Using the appropriate Time Travel settings

By using this feature and following the best practices, users can effectively monitor and optimize the performance of their Snowflake systems.

7. Monitoring resources

Setting monitors when resources are reaching or already reached the limit is a pretty recommended practice in Snowflake.
By monitoring resources in Snowflake, organizations can ensure that the Snowflake environment is running efficiently and effectively, as well as identify and resolve any performance issues, and make decisions about resource allocation.

8. Using Snowpipe

Another good way of loading data aside from using the COPY command is using Snowpipe.
As explained above, Snowpipe is a service that automatically loads data from an external stage into a Snowflake table as soon as it is available.
To set up Snowpipe, users first need to create an external stage that points to the location of the data files.
Once the stage is created, a pipe can be created that references the external stage and the target table. The pipe can be scheduled to run at specific intervals or can be set to run continuously.

9. Leveraging table cloning

It's recommended to use Snowflake's table cloning feature as it allows users to create a copy of an existing database, table, or query result, with the option to include or exclude certain data.
This can be useful for a variety of purposes, among which are testing, backup, analytics, performance and data governance.

10. Querying data where it resides

Snowflake is a data warehouse that supports semi-structured data in various formats: Iceberg, JSON, Avro, and Parquet.
This means that the platform can query data where it resides even if the data does not have a fixed schema and this means that users can query and analyze this type of data using SQL.

What are the main benefits of using an ETL process with Snowflake?

Using an ETL pipeline to get data into Snowflake has a number of benefits:

Scalability
Automatic data compression
Continuous data ingestion
Robust security
Caching
Performance monitoring and optimization

Scalability

Snowflake is a cloud-based data platform, so it easily scales up or down in order to meet the different needs of data processing. Efficient and cost-effective data processing is the main benefit of this feature because resources will only be used when they're needed.

Automatic data compression

In Snowflake data is compressed automatically, which can result in significant storage savings, which then would help in reducing storage costs, as well as in improving the performance of data processing.

Continuous data ingestion

Snowpipe is a continuous data ingestion service that allows streaming data into Snowflake. This is useful when it comes to real-time data processing and for applications of the following types: fraud detection, log analysis and IoT.

Robust security

Security is always a much-needed and appreciated aspect in any system, but especially in ETL ones. In order to ensure the security of the data, Snowflake provides advanced security features, such as multi-factor authentication, data encryption, and role-based access control.

Caching

Caching is a powerful feature in Snowflake that can significantly improve query performance, though data engineers need to be careful when using this feature so that they can ensure the data is accurate and up-to-date.

Performance monitoring and optimization

Snowflake provides built-in performance monitoring and optimization features, such as Query Profiles, Explain Plan and Query History. These are super useful when it comes to monitoring and optimizing ETL performance.

How can data engineers use ETL with Snowflake?

Data engineers can use ETL with Snowflake in a number of ways:

Data warehousing
Data ingestion
Data transformation
Data loading
Data management
Data integration
Data security
Data collaboration
Performance optimization

Data warehousing

Snowflake can be used as a data warehouse to store and process large amounts of data from various sources, such as databases, flat files or APIs. When data engineers need, for example, to clean and prepare the data for reporting and analysis, they can take advantage of Snowflake's built-in transformation functions and SQL capabilities.

Data ingestion

Data engineers can use Snowflake's native data ingestion capabilities to easily extract data from various sources such as flat files, databases, cloud storage platforms (e.g. Amazon S3, Azure Blob Storage), etc.

Data transformation

Snowflake, as a platform that provides a powerful SQL-based data transformation engine built on AWS, makes it super easy for data engineers to perform various data transformations such as filtering, aggregating, and joining data. When it comes to data transformation, data engineers can also use Snowflake's built-in functions to perform complex data transformations.

Data loading

Another use case of using Snowflake is data loading which allows data engineers to load transformed data into different types of schemas, such as traditional schemas, time-series schemas, and clustered schemas.

Data management

Snowflake has a variety of data management features such as data sharing, data archiving, and data cloning. All of these features provide an easy way of managing and distributing data across different teams and projects.

Data integration

Snowflake's support for a variety of data formats such as JSON, Avro and Parquet, and connectors to popular data sources like Salesforce, Marketo and Google Analytics, as well as other ETL tools, make it easy for data engineers to integrate data from multiple sources and perform data transformation tasks.

Data security

Snowflake provides advanced security features, such as multi-factor authentication, data encryption, and role-based access control, which can be used to ensure the security and compliance of the data. These features of Snowflake make it a great platform for users to be able to implement data governance policies and ensure that only authorized users have access to sensitive data.

Data collaboration

Snowflake's data-sharing feature allows sharing of data between different accounts, which can be used to collaborate with partners and clients. Also, Snowflake can be used to store raw data from various sources and then share it with internal stakeholders (data scientists, data analysts, etc.) for further analysis.

Performance optimization

Snowflake provides built-in performance optimization features such as data partitioning and clustering, as well as automatic query optimization features which are used to improve query performance.

Understanding Snowflake's Pricing Model

While specific prices depend on your cloud provider, region and Snowflake edition, the main components of Snowflake's pricing model are:

Compute
Data storage
Data transfer

Let's check a bit more in-depth about how each of these main components affects the pricing model.

Compute

The cost of this component depends on the specific compute resource but it's mostly based on usage time.

Data storage

The data storage is a separate component of Snowflake's pricing model and that means that users pay for it separately from the compute resources. For data storage Snowflake has a flat rate per terabyte based on the average bytes stored during the month.

When it comes to data storage, Snowflake provides multiple features, for example, the Continuous Data Protection (CDP) feature which includes Fail-Safe and Time Travel, is given to all Snowflake accounts for no additional cost.

Data transfer

Snowflake charges for data export, but not for data import. Moreover, users are charged only when they move data from one region to another or if they decide to move the data between different cloud platforms.

Snowflake ETL Pros & Cons

Snowflake is a powerful and flexible data warehousing option that can be a great choice as an ETL platform, but users should be aware that it may not be the best choice for them depending on their use case. In order to make a good decision they should always consider the specific needs of their organization and the data that they will be working with.

Pros

Scalability: As we mentioned above, Snowflake, as a cloud data warehouse, can easily scale up or down to meet changing data processing needs.
One of the main features of Snowflake's scalability is its use of a shared data architecture.
This type of architecture is organized in a way where data is stored in a centralized repository and each user is given a virtual warehouse that is used to access the data. This way it is easy for multiple users to access the data at the same time without interfering with each other's queries.
Semi-structured data: The support of semi-structured data in JSON, Avro, and Parquet formats, allows us to easily integrate our data with weblogs, IoT devices, and mobile apps.
Automatic data compression: Snowflake automatically compresses data, which can result in significant storage savings.
High level of security: One of the most valuable features and pros of using Snowflake is its high level of security which is pretty important when it comes to ETL operations.
Time travel and data sharing: The time travel and data sharing features of Snowflake provide easy auditing and sharing of data with external partners.

Cons

Cost: The main disadvantage of using Snowflake might be the cost as it can be more expensive than other ETL solutions, especially when it is used for large data sets.
Relatively young platform: Although Snowflake is one of the leading ETL platforms at the moment, still it is not the most mature one and that might make it harder to find help and resources when working with it.
Limited integration options: Since Snowflake does not have the same level of integration with other tools, like some of the more mature ETL platforms do, it can be harder to integrate with other systems.

ETL for Snowflake: Key Takeaways

Scalability - the biggest strength of Snowflake

The main takeaway is that scalability makes Snowflake a super flexible platform, moreover, we can comfortably say that it is one of its biggest strengths. It is a feature that makes Snowflake a perfect fit both for organizations with the need to handle large amounts of data and those with a need for smaller amounts.

Considerations to be taken before making a decision to use Snowflake

Another takeaway would be that a very important step for any organization, before making a decision to use Snowflake, is that they should consider their specific needs.

A key factor would be the data they will be working with, taking into account mainly its size and complexity.
The other important consideration is their existing infrastructure and IT environment because the cloud-based Snowflake architecture means it is easily integrated with other cloud services like SaaS services or databases, but it might not be the best option for organizations that have a significant investment in on-premise infrastructure.
Finally, organizations need to check their budget because Snowflake can be more expensive than other data warehousing solutions, especially in cases of handling large amounts of data. On the other hand, there is the pay-as-you-go pricing model of Snowflake which may pay off more for organizations with variable data needs or for those looking for a more flexible pricing option.

Security is top of mind with Snowflake

Security is a critical concern for any ETL operation or tool, and Snowflake took good care of it, offering a high level of security by providing a variety of security features that help protect data from unauthorized access, both during the ETL processes and at rest. This makes it an ideal solution for ETL operations, especially for organizations that deal with sensitive data.