My experience with Snowflake spans several different organizational contexts, it’s power in scaling to meet business data requirements is closely followed by how the costs can similarly scale. This post explores practical strategies to help you reduce your Snowflake costs without sacrificing performance. Starting with general guidance, then dipping into technical strategies including dbt specific packages and approaches.
Yes, the title is clickbait, but the savings are real! The approach can be applied to save money on any cloud data infrastructure. The saving in question applies to regular refreshes, not other data processes (more on that in the future).
This covers cloud computing and video editing which is a hobby of mine. As AWS offers Nvidia hardware, I want to leverage it to run DaVinci Resolve, my video editor of choice.
As I previously did a blog post on Querying SQL Server with something LIKE a regular expression (Using simple regular expressions in a LIKE statement), I thought I would use that as a segue into Apache Hive and HiveQL. Because Hive and therefore HiveQL is built using Java, it has the full power of Java regular…
After previously spending a lot of time looking at dates in SQL Server and oracle, I wanted to do the same justice to Hive.
Hive doesn’t have the functions LEFT() and RIGHT() as used in other RDBMS/SQL platforms. Here I demonstrate using hive functions for parsing text values which can emulate LEFT and RIGHT.
I cover getting started with the HDP Virtual Machine and starting hive, in preparation for future posts on Hive and Big Data.