No Clocks

THE Property Data Solution. Our revolutionary tech allows us to get you property and owner data (and lots of it!) faster and cheaper than you've ever been able to before. Slow or buggy applications due to unreliable third party data APIs are a problem of the past.

Unable to find practical examples of idempotent data pipelines? Then, this post is for you. In this post, we go over a technique that you can use to make your data pipelines professional and data reprocessing a breeze.

Data pipelines built (and added on to) without a solid foundation will suffer from poor efficiency, slow development speed, long times to triage production issues, and hard testability. What if your data pipelines are elegant and enable you to deliver features quickly? An easy-to-maintain and extendable data pipeline significantly increase developer morale, stakeholder trust, and the business bottom line! Using the correct design pattern will increase feature delivery speed and developer value (allowing devs to do more in less time), decrease toil during pipeline failures, and build trust with stakeholders. This post goes over the most commonly used data flow design patterns, what they do, when to use them, and, more importantly, when not to use them. By the end of this post, you will have an overview of the typical data flow patterns and be able to choose the right one for your use case.

GDPR and your right to be deleted

Schema-driven development is an important concept to know in 2021. What exactly is schema-driven development? What are the benefits of schema-driven development? We will explore the answers to these questions in this article.

Unpacking Data Science One Step At A Time.

re_data is an open-source data reliability framework for modern data stack. 😊

Data Carpentry is a lesson program of The Carpentries that develops and provides data skills training to researchers.

An intuitive and practical approach to data analysis, data preparation and machine learning, suitable for all ages!

DASE Data Analysis in Software Engineering

Suppose you are starting a new data science project (which could either be a short analysis of one dataset, or a complex multi-year…

Learn more about how and when to perform Exploratory Data Analysis - and how to use EDA data for powerful Business Intelligence.

Data Science is not just for Engineers and Statisticians. Exploratory makes it for Everyone.

This Community Page article describes a data management workflow that can be readily implemented by small research teams and which solves the core challenges of managing regularly updating data. It includes a template repository and tutorial to assist others in setting up their own regularly updating data management systems.

Business is changing as a result of the increasing quantity and variety of data available. Significant new opportunities can be realized by harnessing the knowledge contained in these data - if you know where to look. A data science team can help to bring raw data through the analysis process and derive insights that …

In this article, we'll examine how Blue Oceans are created and how your organization can create Blue Oceans with Data Science too. We'll finish with a roadmap for your organization to build Blue Oceans with Data Science.

In this extensive lecture, I, Steven Bucher, a product manager on the PowerShell team, discuss the integration of AI into the shell environment. Over the pas...

This is a book that covers the standard shiny app workflow.

Other than location, what’s it take to run a successful real estate business? We examine a data model to help real estate agencies stay organized.

Most of us are familiar with the apartment rental process. But what does it take to run a leasing office? In this article, we look at a data model designed to do just that.

AI Database Generator is a sophisticated tool that utilizes artificial intelligence and machine learning algorithms to automate the design and creation of database schemas.

Get a quick rent estimate by address or zip code with Rentometer. Compare rental rates and comps to ensure you're pricing your property right.

Automatic normalisation of a data frame to third normal form, with the intention of easing the process of data cleaning. (Usage to design your actual database for you is not advised.) Originally inspired by the 'AutoNormalize' library for 'Python' by 'Alteryx' (<a href="https://github.com/alteryx/autonormalize" target="_top"https://github.com/alteryx/autonormalize/a>), with various changes and improvements. Automatic discovery of functional or approximate dependencies, normalisation based on those, and plotting of the resulting "database" via 'Graphviz', with options to exclude some attributes at discovery time, or remove discovered dependencies at normalisation time.

Easy entrata API wrapper (WIP)

Move-ins are done. Students, eager to learn and enjoying their lives away from home, are roaming through your community’s halls. Your onsite teams are kicking

Wrapper around the Canadian Mortgage and Housing Corporation (CMHC) web interface. It enables programmatic and reproducible access to a wide variety of housing data from CMHC.

Building skills and community to analyze Canadian demographic and housing data