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.
In today’s fast-paced development world, efficiency is paramount. AI can significantly accelerate the coding process, including the creation of scripts to interact with various data formats. This blog post will explore how to use AI assistance to generate Python code for loading a custom XML file.
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).
An approach for creating a dbt demo project which can be shared and run easily by any developer. From an empty directory, to dbt run in under 5 minutes.
I’ve been using eclipse as an IDE of choice with dbt for a couple of years, here is how I configure it for working with dbt projects.
This is a side project of mine, something to keep me out of mischief at home. My aim for this project is to be an example/template/starting point for using flask for a quick (and dirty) approach to a Master Data Management (MDM) application AKA a Data Warehouse Management Application (DWMA).