Redshift questions from AWS Customer
A customer is using s3, Lambda, and Redshift. They have Redshift questions regarding what to do with the data once it is loaded to Redshift (in a star/snowflake schema).
Customer is trying to determine some approaches to reporting from the warehouse.
Here are some questions:
- Is there an equivalent to cubes in redshift?
- Is there a better approach in moving data into redshift (compared to our approach)? This is all they gave me thus far on this question.
- Performance of Redshift queries, seems slow when returning data, but fast for loading data and returning aggregated data. Is there a way to address the performance we are seeing?
Thank you.
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Amazon Redshift does not natively provide cube/slice/dice capabilities, this is normally provided by software products that sit "on top" of a database. For example Microsrategy allows you to build ROLAP cubes on top on data marts sourced from Redshift tables.
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Can you specify what is "our Approach"? The best way to move data into an OLAP RDBMS is in "bulk" to maximize the parallel ingestion. Redshift providers the COPY command to perform bulk load ingestion. Data has to be moved into an S3 bucket previous to the execution of a COPY command. ETL tools may hide the process but in the back this process still happens.
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Query performance in an MPP solution like Redshift depends mainly on how the tables containing the data are "distributed" across the cluster and how the data in them is sorted. Distributing and Sorting is a simple mechanism, but we have created some "automated features" that may allow the developers not to be so concerned about it.
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