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Nationwide property data and mapping tools for everyone. Surf 159 million land parcels on our map or license them for yours.

From finances to closing, learn how to buy land in 9 steps so you know what to expect as you start your search.

Look up property lines and ownership info nationwide.

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.

Transform text into images and explore with endless imagination.

Harlequin is a drop-in replacement for the DuckDB CLI, SQLite CLI, psql, etc. that brings SQL IDE features to your terminal.

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...

Use modern Icon libraries to construct customisable leaflet marker icons.

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

GIS, demographics, and data science consulting

Parsing JSON with the  Extract Nested Data component within Matillion Data Productivity Cloud connected to Snowflake simplifies the parsing for many semi-structured data patterns.   The JSON format has become a more popular format for semi-structured data, primarily because it is more consistent containing all key:value pairs. JSON handles repeating elements by containing them in an array as a value of a key:value pair.   For this article, I am using the same example data set that was used in part one on XML only this sample data is represented as JSON. I also walk you through how to convert the XML to JSON to simplify parsing XML.  Extract Nested Data  We start by using the Extract Nested Data component, which simplifies parsing semi-structured data. In this example, we’re using several of them to traverse the nested elements.   First, the JSON file is loaded into a table called donut_json, which contains a single column defined as a variant “data_value.”  Next, configure the Columns property of the Extract Nested component. I used “Autofill”’ and let the component identify the structure of the JSON. I have deselected all the columns and chosen to pass through the Item attributes and element values. In the example, I also passed through the Filling element, keeping it a variant for further processing downstream.  Since the topping elements are repeating at the first level, the component has flattened toppings into separate rows automatically, so I was able to select the element value level for toppings.   Another property to call out is the Outer join property on the Configuration tab. Since all of the elements do not exist for every item, I needed to set Outer Join = “Yes.”  This will retain all the rows for all items, even though only two items have Fillings.   Flatten Variant  The Flatten Variant component is used to flatten arrays. Although the Extract Nested Data component can sometimes be used, the Flatten Variant lets you explicitly break a column into more rows than the original extract nested data if you are seeking further granularity from the extract nested component.  The batter element in this example has two formats, so I have to treat the Batter array differently by using a Flatten Variant component to parse the array of batters into separate rows. The initial Extract Nested Data component created a new row for each item and each topping. From there, we want a new row for each item, topping and batter. I tested the batter element to determine if it’s an array, by using the IS_ARRAY() function in a Calculator component.   IS_ARRAY("items_item-element_batters_batter") After that,  Flatten the array into separate rows per batter element before extracting the attributes.   Set the Column Flatten property to read the batter array column In the column mappings, use the flatten alias to map to an output variant column   Finally, we bring all the rows back together, remove unwanted columns, and write to a new table. The Unite component unions all the rows back together The Rename component allows us to remove any unwanted fields, like the arrays, and rename and reorder the fields  The Rewrite component writes to a new table   The resulting final pipeline is much simpler than the previous XML one.   Convert XML to JSON Our example pipeline started with a file that was already in a JSON format.  However, if you have an XML file that needs to be converted and you would like to convert the XML to JSON inside a pipeline, you’ll use the code below.  Create an Orchestration Pipeline First, I created a separate Orchestration pipeline that contains a SQL Script component to create a Snowflake UDF using the code below. This code calls a Snowflake Snowpark package called “xmltodict.”  Our example XML_to_JSON Python code follows. Parse With the Calculator Component Next in my Transformation pipeline, I called the procedure in a Calculator component. The parse_json function formats the JSON so it’s readable.  Normalizing Semi-Structured Data  Semi-structured files typically contain data