Tech Talk with Toni

  Your email address is valid! This week, I had the task of gathering a client contact list, but I ran into some issues with certain email addresses. Specifically, I noticed that some email addresses were missing the '@' symbol, while others were lacking the domain name. To ensure the integrity of our client contact list, it's crucial that all email addresses follow the expected format: username@domain.com. This way, when we use this data for its intended purpose – sending emails to customers regarding promotions, updates, or other important communications – we can minimize the number of undeliverable emails. I've already taken steps to address this issue by removing rows that don't adhere to this format using Alteryx.  Today, I'm excited to show you how we can use Great Expectations on Google Colaboratory to identify  erroneous emails. I want to give a special shout-out to our Product Manager, Ikechi Griffith, for challenging me to dive deeper into Great Expect...

  pip install great_expectations As a Data Quality Analyst, I constantly seek tools to enhance my daily workflow, and Great Expectations is a recent discovery. Before getting into what this Python library has to offer, let's address a fundamental question: What is Data Profiling? In order to effectively tackle any problem, one must first understand it.  Data Profiling is the art of uncovering and investigating data quality issues, such as duplication, missing values, and inconsistency.  It is essentially determining the baseline level of quality of a dataset in terms of the six data quality dimensions : Completeness, Uniqueness, Validity, Timeliness, Accuracy and Consistency.  Great Expectations Data Profiling Example Now, picture yourself as a pastry chef whose mission is to ensure that every currants roll emerging from your bakery is nothing short of delightful.  Let's explore how data profiling in Great Expectations guarantees they meet your standards. Step 1...