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#Redshift sortkey and distkey how to#
#Redshift sortkey and distkey professional#

#Redshift sortkey and distkey how to#

How to migrate from AWS Redshift to Snowflake? Last but not least Snowflake is a rising platform adding constantly new features and improving the whole data cloud platform. Although, paring semi structured data in Redshift is allowed but doesn't work well as this work with Snowflake. Snowflake stores these types internally in an efficient compressed columnar binary representation of the documents for better performance and efficiency. Semi structured data - Snowflake provides VARIANT data types that you can found valuable for (JSON, Avro, ORC, Parquet, or XML) and performed well.Result caching - Snowflake uses this feature to retrieve only data that’s different since the last time you executed your query.It makes life easy for developer if they are working on certain use case which required same copy of data sets for different use cases. using just few lines of command without copying the data. Cloning - We can easily create copies of database, schema etc.In older days, it was so difficult to achieve and we have to perform lot many activities if we want to rollback DB for any reasons, it is also required significant efforts from the DBA team. Time travel - This is one of the exciting features being introduced by Snowflake, it give us flexibility to instantly roll back the entire warehouse at any point during a chosen retention window.

Overall, there’s more management involved with Redshift than Snowflake. With Redshift, you have to manage specific servers even though the service is virtual. Since compute and storage are separate in Snowflake, you don’t have to resort to copying the data to scale up or down. Redshift Resize operations can also become quite expensive at times, resulting in significant downtime. This poses a few challenges in Redshift, similar to the challenges faced while scaling up or down in Redshift.

Management of cluster (Resizing) -In terms of analyzing and vacuuming the tables regularly, Snowflake offers a unique reliable solution.

This is super helpful and something we could not do in Redshift. Once we provided the permissions for teams to use the warehouse, it will be easy to identify the cost associated with each application and business unit. We can built a dedicated warehouse for our major applications/streams and can named the warehouse so that it can also help us to recognize who within the organization is using it.

Architecture - One of the biggest advantages of Snowflake is it's architecture, it provides the separation between storage and compute which gives you flexibility to choose storage and compute optimally.

Now, let's understand "why" and "how" ? Why to migrate from AWS Redshift to Snowflake? There is no doubt that "migration" is being one of the toughest task hence this need to be planned optimally considering all the aspects, most importantly, we have to perform due diligence extensively to avoid any unknowns at later stage. which needs to be considered before taking the final decision. There are numerous factors like tools capability, cost, performance etc.

#Redshift sortkey and distkey professional#

Background - We as a a data professional come across this scenario " migration from AWS redshift to new data cloud data warehouse - Snowflake" so often these days.