Things to Consider For a Smooth Migration to Snowflake

 

Snowflake is a cloud data warehousing platform that makes it easier for the data team to use and store data. Contrary to traditional storage solutions, there are various data types and business intelligence tools supported by Snowflake. This makes collaboration within the internal and external teams easier throughout the ETL data migration pipeline. Snowflake also supports most of the structured and unstructured data types. While many customers are excited at the prospect of migration, they lack the knowledge of how to start. Regardless of where you are starting from, here are a few considerations to make while moving to the Snowflake.

 

1. Say goodbye to partitions and indexes

Contrary to other data warehouses, Snowflake doesn’t support indexes or partitions. Snowflake rather focuses on the automatic division of large tables into micro-partitions used to calculate statistics pertaining to value ranges carried by each column. These insights are then used to decide which parts of your data set are actually needed to run the query. Although the paradigm shift to micro-partitions is not an issue for most, you need a strong approach if there are indexes and partitions in your current ecosystem, and you are looking forward to migrating to clustering.

 

1.    Document current data schema and lineage. This is especially useful when you need to cross-reference your old data ecosystem with a new one. 

2.  Analyze your current schema and lineage. Next, you need to analyze whether the structure and its corresponding upstream sources will make sense to how you will be utilizing data after it has been migrated to a Snowflake. 

3.  Select appropriate cluster keys. This ensures the best query performance for your team’s access patterns. 

Saying goodbye to indexes and partitions is nothing to worry about as long as your data possess visibility. 

 

2.     Expect (and embrace) syntax issues.

 

For companies that have largely relied on legacy solutions and manual data input, syntax errors can be painful. Simply moving to the cloud doesn’t suffice the issue. It is believed that even if you hire the best people and give them a data dictionary, they still won’t be able to tell you what it all means. You need to understand that syntax errors are a part of the process, and the sooner you do it, the easier it will be for you to identify trends and patterns in the inconsistencies that can lead to the expedition of the resolution. 

 

3.     Monitor your data, always and often.

 

Just like syntax errors, data issues can lead to even the best of snowflake migrations to failure. This leads to false or misleading analysis that can result in unnoticed errors. This often catches the attention of customers in reports or dashboards. Therefore, whenever you upgrade the data warehouse, ensure that the way the team is operating is upgraded. This involves everything- from syntax concurrency to data quality and reliability. 

 

By moving on from partitions and indexes, expecting syntax issues, and prioritizing data quality, you can achieve a seamless Snowflake migration; thus driving more value to your business.