Contributor: Rick Arnett Data Mesh is a new paradigm for managing enterprise data. Created in 2019 by Zhamak Dehghani, it directly addresses some of the biggest challenges facing data architects and data engineers head-on. It also borrows ideas from big IT Agile, microservices, and domain driven development. Before we talk about data mesh, let’s take
Contributor: Matt McGorrey Snowflake is more than just cloud data storage Following Snowflake Summit 2022, it’s clear that the company wants to be the number one “data cloud,” and that means more than just storing data. Snowflake wants their platform to support the next generation of data science, analytics, and data-first applications. They announced Native
Snowflake recently hosted Snowpark Day: a showcase of Snowflake’s developer-friendly framework. Snowpark gives developers the tools to flip, spin, and grind data using the gear they already know. The gear in this case is Python, Java, or Scala – the languages Snowpark supports; and the tricks are the ability to execute queries, load data, and
When most people hear the word “snowflake,” they think of cold days and long nights. But some get excited, and anyone in the data business knows exactly why. Snowflake is an analytic Data Cloud that is much faster, easier to use, and more versatile than its peers. It’s a SaaS platform that makes use of
Businesses today, both large and small, are making significant investments in replacing manual, paper-based processes and legacy IT to modern, real-time, data-driven systems that are easily integrated. The initial driver for this shift, commonly referred to as “digital transformation,” was the replacement of paper-based processes with digital ones. Now, many firms have moved beyond “lifting
In a previous blog post, I talked about the importance of data agility in an organization. As mentioned in that post, data agility is defined by TDWI as: “how fast can you extract value from your mountains of data and how quickly can you translate that information into action?” For business, the translation is about
By: Katie Toyoshima, Aurora Rosin, & Nate Van Roosendaal In the first installment of this blog series, we took a look at Magenta Inc.’s issues with customer churn. In Part 2, Magenta’s Senior Data Architect, Jake, integrated the company’s data into Snowflake, alongside existing data from Salesforce. The data from the centralized data hub was
Contributing Authors: Peter Ottsen and Anthony Nardiello Proactively Addressing Churn with Data Science In part 1 of our series, you were introduced to Magenta Inc., a fictional company facing issues with customer churn. Elaine Gadget, Magenta’s Senior Sales Representative, has already worked with her team to identify an important problem—the sales team has no visibility
Contributing Authors: Tyler Pollard, Emma LeMasters This is the second post in a short blog series focused on preventing customer churn using a centralized data hub. Here’s an infographic version of the series! Getting the Right Data with the Right Tools As mentioned in Part 1 of this series, our fictional company, Magenta Inc., is
This is the first post in a short blog series focused on preventing customer churn using a centralized data hub. Here’s an infographic version of the series! The Problem: Recent Churn… and a Lack of Insight Here’s a problem our clients deal with all too often: miscommunication. As an example, let’s take a hypothetical