Who are the Top 100 Landowners in the US? | Land Report 100
Land Report 100
Explore the 2025 Top 100 U.S. Landowners. View Top 100
Presented by:
Land Report 100
Explore the 2025 Top 100
U.S. Landowners. View Top 100
Presented by:
Land Report 100
Who is America's
Largest Landowner?
This question is the quest
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.
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...
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
Semi-structured files typically contain data that has been nested, and we often want to store that data in a structured format more friendly to analytics and reporting. Many times, as we flatten out deeply nested data, we end up with a multi-join or cartesian join where all upper-level elements of the file are joined with all nested elements of the file.
real-world examples are often very large when flattened. In these cases, we need to evaluate the data contained in the JSON response and determine the best model to represent the data in different tables.
In order to split the dimensions into separate tables, the first Extract Nested Data component will pass the full element as a variant downstream in order to start to split out the different datasets into separate streams.
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.
autodb: Automatic Database Normalisation for Data Frames
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.
The Importance of Market Surveys in Student Housing - Radix Software
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
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