10 Best Practices For Snowflake ETL

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What is Snowflake ETL?

ETL stands for extract, transform and load, in this data is collected from various sources and is unified in a target system preferably a data warehouse. If data from multiple sources is compiled and loaded into snowflake data lake then it is called snowflake ETL.

Why Consider Snowflake?

A. Unique Architecture

Snowflake is famous for its unique architecture that gives it an upper edge as compared to other data warehouses and is considered to be the best platform to start off your business and grow exponentially. The best feature of snowflake is that it separates data storage, data processing, and data consumption by distinguishing them through layers whereas in other traditional warehouses there is only a single layer for storage data re processing, and compute.

B. Efficiency

It saves effort and time by automatically managing all indexing and partitioning of tables not only this it also automatically separates compute on shared data allowing jobs to run in parallel. 

C. Processes Standard SQL

Snowflake allows querying on raw data back from the warehouse using the standard SQL and is ACID compliant.

D. Auto Scaling

The auto suspend feature in snowflake is one of the best that automatically suspends the warehouse if not in use.

  1. Always make use of auto suspend
  2. Effectively manage costs
  3. Make use of Snowflake query profile
  4. Transform data stepwise
  5. Use data cloning
  6. Leverage Snowpipe
  7. Implement data validation
  8. Use Materialized Views
  9. Implement incremental loading
  10. Use external tables

1. Always make use of auto suspend 

When a warehouse is created, in snowflake you can set that warehouse to suspend after a certain amount of time. If the warehouse is inactive for that certain amount of time then snowflake automatically suspends it helping to keep costs at bay. By default the auto suspend option is enabled and it is a good practice to use auto suspend whenever possible.

If the warehouse is suspended and you run a query that is using a warehouse then it automatically resumes the warehouse also. This process is so fast and resumes the warehouse in no time. 

2. Effectively manage costs 

To save enormously on cost one needs to understand the pricing model offered by snowflake. Snowflake separates storage and compute costs. The storage cost is levied based on the average monthly storage consumption and the compute cost is set as per the total Snowflake credits store data consumed.

To effectively manage costs one should follow some of the best practices mentioned below : 

  • Resource monitors should be set up: It will help to keep track of utilization needs.
  • Avoid using Select * statements whenever possible: Suppose a user wants to have a look at the data then instead of viewing the whole data one can just take a glimpse of it. 
  • Setting up alerts: Sometimes for non-snowflake users reader accounts are created as a result users can run a query on the data which unnecessarily shoots up the consumption costs. It is considered to set up alerts for reader accounts to keep track of incurring costs to monitor those accounts.

3. Make use of Snowflake Query Profile

Query profile is a powerful tool that helps in diagnosing a query that provides data scientists its execution details. It gives information about the performance and behavior of a query.

It is considered to use Snowflake's Query Profile tool to analyze issues in queries that are running slow. Query Profile lets you examine how Snowflake ran your query and what steps in the process are causing the query to slow down

4. Transform data stepwise

You should always refrain from using complex SQL queries and always try to write simple queries as it can be difficult to maintain the code. Instead, you can write SQL code in chunks that are a lot easier to understand, and maintain, and they can be proved to be more time efficient. Writing queries in small chunks and then combining them afterward can enhance the warehouse performance and it can even give rise to time-efficient querying.

5. Use Data Cloning 

Cloning is a feature in snowflake that creates a copy of data sharing the database, schema, or table. Cloning forms a derived replica of that object that shares the storage. This cloning feature is very convenient when creating instant backups. No extra cost is involved while using this feature unless and until any changes are made to the original source.

6. Leverage Snowpipe

Snowpipe is a service offered by Snowflake that allows for near-real-time ingestion of raw data files. Leveraging Snowpipe can greatly reduce ETL latency and improve the timeliness of your data.

7. Implement data validation

Implementing data validation checks at each step of data transformation in your ETL process can help ensure the accuracy and completeness of your data.

8. Use Materialized Views

Materialized views are precomputed views stored and can be queried much faster than traditional ones. Using materialized views can greatly improve query performance.

9. Implement incremental loading

Instead of always performing a full load of your data, consider implementing incremental data loading. This means only loading the changes made to your data since the last load. This can greatly reduce the time and cost of your ETL processes.

10. Use external tables

Snowflake supports external tables, which reference data stored outside of Snowflake. Leveraging external tables can help reduce storage costs load data, and simplify data ingestion processes.

Snowflake has a unique 3-layer architecture and is a modern data warehouse to make the most of data engineers its services, these practices can prove to be helpful to you.

Snowflake supports a wide variety of features that are used to apply for implementing the right data analytics use cases and to mine raw data history help businesses make better-informed decisions. Snowflake is one of the most admired data warehouses used and trusted by millions of users today. It provides flexibility, accessibility, and scalability helping businesses to manage their data easily in no time.


  • Always use auto suspend when the warehouse is not being used, this reduces consumption and utilization.
  • Set up alerts for new reader accounts to bring costs down
  • Avoid using select * statements to view the data instead use a query that is fast and that will not require scanning all your data.
  • Always keep an eye on the resource monitor to calculate costs and check whether the threshold limit is reached.
  • Use the Snowflake query profile to keep a track of slow processing queries.
  • Transform data in steps instead of writing a single complex code snippet.
  • Use data cloning to copy the tables, schemas, or database.

    Frequently Asked Questions (FAQ) - Snowflake ETL Best Practices
    1. What is Snowflake ETL?
      Snowflake ETL refers to the process of extracting, transforming, and loading data from various sources into Snowflake, a cloud data warehouse. This unified data aggregation allows for efficient analytics and reporting.
    2. Why consider Snowflake for ETL processes?
      Snowflake offers a unique architecture that separates storage, processing, and consumption layers, providing scalability, efficiency, and cost-effectiveness compared to traditional data warehouses.
    3. How can I effectively manage costs in Snowflake?
      Managing costs involves setting up resource monitors, avoiding unnecessary data scans with selective queries, setting alerts for unexpected usage, and leveraging features like auto-suspend for idle warehouses.
    4. What is Snowflake Query Profile and how can it help optimize queries?
      Snowflake Query Profile is a diagnostic tool that provides detailed insights into query execution. It helps identify performance bottlenecks and optimize slow-running queries.
    5. What are best practices for transforming data in Snowflake?
      Transform data stepwise by breaking down complex SQL queries into simpler, manageable steps. This approach improves code maintainability and enhances warehouse performance.
    6. How does data cloning benefit Snowflake users?
      Data cloning in Snowflake allows for instant backups and replication of databases, schemas, or tables without additional storage costs, providing data redundancy and disaster recovery capabilities.
    7. What is Snowpipe and how can it enhance ETL processes?
      Snowpipe is a real-time data ingestion service in Snowflake that reduces ETL latency by automating the ingestion of data from external sources into Snowflake tables.
    8. Why is data validation important in Snowflake ETL?
      Implementing data validation checks ensures data accuracy and completeness at each stage of the ETL process, maintaining data integrity and reliability.
    9. How do Materialized Views improve query performance in Snowflake?
      Materialized Views precompute and store query results, allowing for faster data retrieval and reducing the computational overhead on recurring queries.
    10. What is incremental loading and how does it optimize ETL workflows?
      Incremental loading involves loading only the data changes since the last load, minimizing processing time and resource consumption during ETL operations.
    11. How can external tables be leveraged in Snowflake for data integration?
      Snowflake supports external tables that reference data stored outside the platform, enabling seamless data ingestion and reducing storage costs.
    12. What are the key benefits of Snowflake's 3-layer architecture?
      Snowflake's architecture separates storage, compute, and query layers, allowing for independent scaling, efficient resource utilization, and improved performance.
    13. How does Snowflake ensure security and data governance?
      Snowflake offers role-based access control, data encryption, and continuous data protection to safeguard sensitive data and ensure compliance with data governance policies.
    14. What strategies can be used to optimize query performance in Snowflake?
      Optimizing query performance involves utilizing appropriate data partitioning, indexing, and leveraging caching mechanisms to reduce query response times.
    15. What file formats does Snowflake support for data integration?
      Snowflake supports various file formats like CSV, JSON, Parquet, Avro, etc., enabling seamless integration with diverse data sources and simplifying data processing.
    16. How does Snowflake handle massive data files and ensure efficient data processing?
      Snowflake's architecture allows for automatic data compression, efficient query distribution, and parallel processing of large datasets, ensuring optimal performance.
    17. What role does the Snowflake data engineering team play in ETL processes?
      The data engineering team designs and implements data pipelines, manages data transformations, and ensures the efficiency and reliability of ETL workflows in Snowflake.
    18. What is the importance of continuous data streaming in Snowflake ETL?
      Continuous data streaming enables real-time data ingestion and processing, ensuring timely insights and up-to-date analytics for business intelligence and decision-making.
    19. How does Snowflake support automatic schema evolution and data model flexibility?
      Snowflake automatically manages schema changes and supports multiple data models, allowing for seamless evolution of data structures without disruption to existing applications.
    20. What are the best practices for ensuring data integrity and reliability in Snowflake ETL?
      To ensure data integrity, Snowflake users should implement robust validation processes, leverage transactional features, and establish data quality monitoring mechanisms to detect anomalies and discrepancies.

Written by
Rupal Sharma


10 Best Practices For Snowflake ETL