To discuss Snowflake’s Best Practices in Data Engineering, we must first define Data Engineering.
Data engineering refers to the process of turning unusable data into information that can be used to support business decisions. Data is acquired, the raw data history is ingested, cleaned, restructured, and then enhanced by merging new qualities before being made ready for end-user consumption.
The components of data engineering are similar across all analytics platforms. We collected a range of best practices from our Snowflake-certified team and brought them to you below:
Data collection: this process is gathering the raw data files and putting them on cloud storage services like Amazon S3, Azure Blob, or Google Cloud Platform. Data is first loaded into a Snowflake table for ingestion and landing, after which it can be cleaned and altered.
Raw history: it’s best practice to save the history of raw data to allow machine learning and data re-processing as necessary, unless the data is derived from a raw data lake.
Data integration: focuses on organizing and integrating the data once it has been cleaned up, enhanced with new properties, and restructured. The final results are often stored in Snowflake permanent tables, whereas intermediate results are typically kept in temporary or transient tables during the transformation process.
Data presentation and consumption: although the Data Integration area may store data in a Third Normal Form or in a Data Vault, it’s often a good idea to store data that is ready for consumption in denormalized tables.
Snowflake: Best Practices to Adopt in Data Engineering
Snowflake is a highly popular, powerful tool for cloud data management. Great power also brings some risks. Errors can be quite expensive when dealing with vast amounts of data and data processing.
Organizations require new options for expediting and automating these processes with more adaptable and changing downstream schemas when it comes to investigating, organizing, blending, and cleaning enormous volumes of new, heterogeneous, less-structured data.
1. Make data warehouses capable of auto-suspension
You must configure your virtual warehouses to auto-suspend in order to stop credit use. When you choose this option, data warehouses will automatically shut down after processing queries.
2. Enable auto-resuming
In keeping with the aforementioned strategy, you should establish suitable time-out limits when you set virtual warehouses to automatically suspend and resume. Users won’t be able to query the system if you don’t use the auto-resume option.
3. Optimize your data models in Snowflake
The models will be properly tuned for Snowflake in the final step for quick and scalable execution by data engineers. Here are some methods for optimization:
- Utilizing virtual data warehouses is advised by Snowflake in order to manage various workloads effectively. Users should separate “loading” and “execution” into separate virtual data warehouses.
- Snowflake advises using materialized views to enhance performance for frequent and repetitive queries, and data transformation models meet this requirement wonderfully.
4. Remove any unused tables from the warehouses
The ideal candidates to be removed from the system are unused tables. Make sure no one is querying a table before removing it. Checking all the tables before deleting them is a smart idea.
5. Set workload-appropriate timeouts
Every virtual data warehouse needs to have the proper timeouts enabled for each workload. Suspension timeout for BI and SELECT query warehouses should be limited to 10 minutes to keep data caches warm for frequent user access. Suspension timeout can be kept at 5 minutes for warehouses with DevOps, DataOps, and data science databases.
6. Maintain account statement timeouts
Queries that take a long time to execute due to a frozen cluster or user error can be automatically stopped by using account statement timeout parameters like queued timeout and timeout commands.
7. Check the weekly average of credit consumption
It might be challenging to maintain track of your credit usage history when working on extended projects, particularly when your credit limit is exceeded. Making a query that examines your seven-day average credit utilization and identifies the weeks where the consumption exceeds the average is a helpful technique.
8. Model canonical data
The data engineering team can convert the raw data into canonical data models that represent particular subjects once the data has been entered into the CDW and has undergone the first pass of data transformation. Canonical data models produce reusable, shared components for a variety of scenarios.
9. Query data suggestions
Snowflake helps you assign the Virtual Warehouse based on the verticals/departments so caching will be more efficient when querying the same data. This increases the likelihood that information retrieved to the cache by one user will be used by other users as well.
10. Observe the data type guidelines
Data types are crucial to take into account while replicating data because they vary greatly from one system to another. One incorrect data type might halt the entire data replication process and result in problems using the data downstream.
11. Plan for disaster recovery
Have a backup plan in place to restore your data in the event of a data replication issue. Snowflake has a Time Travel feature which allows your team to roll back to the last known steady state of your data before sending your changes into production. If a user needs to access data that is older than the time-travel window, Snowflake has a failsafe data recovery process in place. The service is available for all Snowflake accounts and can be accessed through the Snowflake web interface or via the Snowflake API.