Originally posted on Metabase.com blog In a data-driven world, businesses need accurate and clean data to make informed decisions. Clean data is essential for reliable insight, efficient operations, and ultimately, success. Dirty data remains a significant challenge for many as it can lead to inaccurate analyses and misguided decision-making.
Everyone is talking about GPT-4, and for good reason. The generative capabilities are amazing, generating impressive and coherent text. On the other hand, I've been playing with the tool as a way to do traditional NLP tasks really quickly and with no training or examples (aka "zero shot learning"). This
In late 2021, seed-stage supply chain SaaS startup Backbone contacted Kaleidoscope Data about up-leveling their analytics offerings, including offering a paid "Data Warehouse" service to their customers and starting a data practice. Kaleidoscope Data worked with the CTO and engineering team to build out a scalable analytics infrastructure, grow their
In case you live under a rock, or rather, are smart enough to disconnect yourself from social media: Elon Musk has given in today and agrees to buy Twitter ($TWTR) at his original agreement's purchase price of $54.20/share. After months of Twitter insults to the executive team, accusations
We walk through our DBT project structure and how it helps us transform data from many point-of-sale (POS) systems into a retail analytics data warehouse. * Background * File Structure * Base Tables * Staging Tables * Core Tables * Inventory * Conclusion Background For the last several years, we have been building and maintaining a retail
ETL/ELT frameworks can make working on big data pipelines a lot easier, as compared to writing extraction and transformation scripts from scratch. On the other hand, for very lightweight and standalone data extraction tasks, it's hard to beat a quick script run serverless on your cloud provider of choice.
Most data folks understand a "Data Warehouse" as an internal tool for reporting and analytics across different teams in your organization. However, it can also be used externally - by customers who want more granular access to their data (often your largest customers). Charging customers for this data access can
Background Multi-tenancy in BigQuery can be accomplished in several ways. This article by the folks at Google has many helpful tips for data isolation and security. The main idea is isolating tenant data in BigQuery datasets - all under a single client data project - rather than creating a new