<?xml version="1.0" encoding="UTF-8"?><rss xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:atom="http://www.w3.org/2005/Atom" version="2.0"><channel><title><![CDATA[The Data School RSS Feed]]></title><description><![CDATA[The Data School RSS Feed]]></description><link>https://www.thedataschool.co.uk</link><generator>GatsbyJS</generator><lastBuildDate>Fri, 24 Jul 2026 20:54:08 GMT</lastBuildDate><item><title><![CDATA[How to Write a Great README.md (Plus a Markdown Cheat Sheet)]]></title><description><![CDATA[Any project's README.md is its front door. It’s the very first thing a user, a contributor, or a prospective employer sees when they land on a repository. A great README doesn’t just explain what your code does; it acts as a welcoming guide that invites people to use and contribute to your work.

Here are some tips on how to structure a good README, followed by a handy Markdown cheat sheet to help style it.


What Makes a README "Great"?

You don’t need to write a novel, but a professional READM]]></description><link>https://www.thedataschool.co.uk/joss-lazenby/writing-a-great-readme-md-a-markdown-cheatsheet</link><guid isPermaLink="false">https://www.thedataschool.co.uk/joss-lazenby/writing-a-great-readme-md-a-markdown-cheatsheet</guid><pubDate>Fri, 24 Jul 2026 00:00:00 GMT</pubDate><dc:creator>Joss Lazenby</dc:creator><image>https://images.unsplash.com/photo-1486312338219-ce68d2c6f44d?crop=entropy&amp;cs=tinysrgb&amp;fit=max&amp;fm=jpg&amp;ixid=M3wxMTc3M3wwfDF8c2VhcmNofDN8fG5vdGVib29rfGVufDB8fHx8MTc4NDUzMjQxMXww&amp;ixlib=rb-4.1.0&amp;q=80&amp;w=2000</image></item><item><title><![CDATA[Show Your True Colors: Assigning RGB Values in Tableau]]></title><description><![CDATA[As part of Day 7 of the Summer of SQL, you ended up working with LEGO data and in the end, need to make a dashboard based on a view that you create using the LEGO data in a database. While working and designing the dashboard for that day, I ended up running into a small problem. I had a dimension field in my data containing a color RGB value and wanted to color my marks based on that dimension.

The issue was this: Tableau didn't have a way to assign colors based on those values. There were too ]]></description><link>https://www.thedataschool.co.uk/oscar-kriebel/show-your-true-colors-assigning-rgb-values-in-tableau</link><guid isPermaLink="false">https://www.thedataschool.co.uk/oscar-kriebel/show-your-true-colors-assigning-rgb-values-in-tableau</guid><pubDate>Fri, 24 Jul 2026 00:00:00 GMT</pubDate><dc:creator>Oscar Kriebel</dc:creator><image>https://images.unsplash.com/photo-1618513462042-29ac20aefe11?crop=entropy&amp;cs=tinysrgb&amp;fit=max&amp;fm=jpg&amp;ixid=M3wxMTc3M3wwfDF8c2VhcmNofDJ8fHBhbGV0dGV8ZW58MHx8fHwxNzg0OTAxNTI1fDA&amp;ixlib=rb-4.1.0&amp;q=80&amp;w=2000</image></item><item><title><![CDATA[Are AI-Native BI Tools the Future of Data Analytics?]]></title><description><![CDATA[In my last blog post, I discussed how knowledge of data structures can be helpful in the process of data preparation (e.g. cleaning, reshaping, etc.), but the other aspect of data analytics work-–that tends to receive the most coverage—is data analysis. Data analysis is a conglomeration of hard and soft skills that (us) data analytics consultants employ to help people make sense of their data. However, in layman’s terms, data analysis can be broken into 2 main buckets:

 1. Data Visualization

 ]]></description><link>https://www.thedataschool.co.uk/jalil-cooper/are-ai-native-bi-tools-the-future-of-data-analytics-2</link><guid isPermaLink="false">https://www.thedataschool.co.uk/jalil-cooper/are-ai-native-bi-tools-the-future-of-data-analytics-2</guid><pubDate>Thu, 23 Jul 2026 00:00:00 GMT</pubDate><dc:creator>Jalil Cooper</dc:creator><image>https://images.unsplash.com/photo-1716436329475-4c55d05383bb?crop=entropy&amp;cs=tinysrgb&amp;fit=max&amp;fm=jpg&amp;ixid=M3wxMTc3M3wwfDF8c2VhcmNofDE5fHxNYWNoaW5lJTIwTGVhcm5pbmd8ZW58MHx8fHwxNzg0ODQxMzY0fDA&amp;ixlib=rb-4.1.0&amp;q=80&amp;w=2000</image></item><item><title><![CDATA[How to Use Regex in the Alteryx Formula Tool]]></title><description><![CDATA[The dedicated RegEx tool is great when you want to match, parse, replace or tokenize text. Sometimes, though, Regex is only one part of a larger calculation. You might want to use it inside an IF statement, combine it with other cleaning steps, classify records or create a calculated field. In those situations, it often makes more sense to use Regex directly in the Formula tool.

The three main Regex functions available there are:

 * REGEX_Match()
 * REGEX_Replace()
 * REGEX_CountMatches()


RE]]></description><link>https://www.thedataschool.co.uk/holly-andersen/how-to-use-regex-in-the-alteryx-formula-tool</link><guid isPermaLink="false">https://www.thedataschool.co.uk/holly-andersen/how-to-use-regex-in-the-alteryx-formula-tool</guid><pubDate>Thu, 23 Jul 2026 00:00:00 GMT</pubDate><dc:creator>Holly Andersen</dc:creator><image>https://images.unsplash.com/photo-1559644705-15d30e582900?crop=entropy&amp;cs=tinysrgb&amp;fit=max&amp;fm=jpg&amp;ixid=M3wxMTc3M3wwfDF8c2VhcmNofDEzfHx0aWxlc3xlbnwwfHx8fDE3ODQ3MzIxMjF8MA&amp;ixlib=rb-4.1.0&amp;q=80&amp;w=2000</image></item><item><title><![CDATA[How to build a Live Response Tracker]]></title><description><![CDATA[Creating a Tableau Dashboard connected to a Google Form



If you're looking to track and visualize support tickets, event RSVPs, equipment checkouts, personal habits, or pretty much anything else you can create a Google Form for, I'm here to show you how to create a dashboard that can display updating entries right on Tableau!

Let me preface this by saying I am assuming you will be posting your dashboard on Tableau Public (making it available to any/everyone). Though the steps below are geared]]></description><link>https://www.thedataschool.co.uk/bianca-beingolea/untitled-282</link><guid isPermaLink="false">https://www.thedataschool.co.uk/bianca-beingolea/untitled-282</guid><pubDate>Thu, 23 Jul 2026 00:00:00 GMT</pubDate><dc:creator>Bianca Beingolea-Joseph</dc:creator><image>https://images.unsplash.com/photo-1597386601945-8980df52c3dc?crop=entropy&amp;cs=tinysrgb&amp;fit=max&amp;fm=jpg&amp;ixid=M3wxMTc3M3wwfDF8c2VhcmNofDE4fHxkYXNoYm9hcmR8ZW58MHx8fHwxNzg0NzU3MTE4fDA&amp;ixlib=rb-4.1.0&amp;q=80&amp;w=2000</image></item><item><title><![CDATA[Power BI Tip: Measures and Slicers]]></title><description><![CDATA[One thing that caught my attention while working in Power BI last week was how a measure behaves when a slicer is applied to the same field it references. By default, a slicer will filter most visuals on your report page. But when you wrap your measure in a CALCULATE function using ALL, it removes that filter context, meaning the measure holds its value regardless of what the user selects in the slicer.

A simple example looks like this:

All Category Sales = CALCULATE([Total Sales] , ALL(Catego]]></description><link>https://www.thedataschool.co.uk/gerard-najarro/power-bi-tip-measures-and-slicers</link><guid isPermaLink="false">https://www.thedataschool.co.uk/gerard-najarro/power-bi-tip-measures-and-slicers</guid><pubDate>Thu, 23 Jul 2026 00:00:00 GMT</pubDate><dc:creator>Gerard Najarro</dc:creator><image>https://images.unsplash.com/photo-1502252430442-aac78f397426?crop=entropy&amp;cs=tinysrgb&amp;fit=max&amp;fm=jpg&amp;ixid=M3wxMTc3M3wwfDF8c2VhcmNofDE1fHx0cmVlcyUyMHRvJTIwZm9yZXN0fGVufDB8fHx8MTc4NDc3MjE3OHww&amp;ixlib=rb-4.1.0&amp;q=80&amp;w=2000</image></item><item><title><![CDATA[Preppin' Data 2024 Week 27 Part 1: Joins in Tableau Prep Builder]]></title><description><![CDATA[An introduction to joins in Tableau Prep Builder with a Preppin' Data Challenge example.]]></description><link>https://www.thedataschool.co.uk/amelia-young/preppin-data-2024-week-27-part-1-joins-in-tableau-prep-builder</link><guid isPermaLink="false">https://www.thedataschool.co.uk/amelia-young/preppin-data-2024-week-27-part-1-joins-in-tableau-prep-builder</guid><pubDate>Wed, 22 Jul 2026 00:00:00 GMT</pubDate><dc:creator>Amelia Young</dc:creator><image>https://images.unsplash.com/photo-1565164705190-d5e3b7fb3446?crop=entropy&amp;cs=tinysrgb&amp;fit=max&amp;fm=jpg&amp;ixid=M3wxMTc3M3wwfDF8c2VhcmNofDE2fHxqb2luaW5nfGVufDB8fHx8MTc4NDc1ODQxMHww&amp;ixlib=rb-4.1.0&amp;q=80&amp;w=2000</image></item><item><title><![CDATA[Tableau Server or Tableau Cloud? Key Differences Explained]]></title><description><![CDATA[Tableau Server and Tableau Cloud both provide the same powerful data visualisation and analytics capabilities, but they are different in how they are deployed, managed, secured and maintained.

I will compare Tableau Server and Tableau Cloud explaining key differences and outlines the advantages and disadvantages of each system.




What is Tableau Server?

Tableau Server is a self-managed analytics platform that enables organisations to publish, share and collaborate on Tableau dashboards and d]]></description><link>https://www.thedataschool.co.uk/kaori-ikarashi/tableau-server-vs-tableau-cloud-2</link><guid isPermaLink="false">https://www.thedataschool.co.uk/kaori-ikarashi/tableau-server-vs-tableau-cloud-2</guid><pubDate>Wed, 22 Jul 2026 00:00:00 GMT</pubDate><dc:creator>Kaori Ikarashi</dc:creator><image>https://images.unsplash.com/photo-1690627931320-16ac56eb2588?crop=entropy&amp;cs=tinysrgb&amp;fit=max&amp;fm=jpg&amp;ixid=M3wxMTc3M3wwfDF8c2VhcmNofDJ8fGNsb3VkJTIwdnMlMjBzZXJ2ZXJ8ZW58MHx8fHwxNzg0NzM3NDk0fDA&amp;ixlib=rb-4.1.0&amp;q=80&amp;w=2000</image></item><item><title><![CDATA[Fuzzy Matching In Alteryx]]></title><description><![CDATA[How does fuzzy matching work in Alteryx and what are all the options available?]]></description><link>https://www.thedataschool.co.uk/sophie-yeung/fuzzy-matching-in-alteryx-2</link><guid isPermaLink="false">https://www.thedataschool.co.uk/sophie-yeung/fuzzy-matching-in-alteryx-2</guid><pubDate>Wed, 22 Jul 2026 00:00:00 GMT</pubDate><dc:creator>Sophie Yeung</dc:creator><image>https://images.unsplash.com/photo-1772958413628-6f0d2fa60cee?crop=entropy&amp;cs=tinysrgb&amp;fit=max&amp;fm=jpg&amp;ixid=M3wxMTc3M3wwfDF8c2VhcmNofDExfHxibHVycnklMjBoYW5kc3xlbnwwfHx8fDE3ODQ3MzE5OTB8MA&amp;ixlib=rb-4.1.0&amp;q=80&amp;w=2000</image></item><item><title><![CDATA[A Long Story Short: SQL's Stored Procedures and Information Schema]]></title><description><![CDATA[Recently, I went through a course at the Information Lab for an introduction into Data Engineering for my future placements. During this time, we learned about different elements when working with a database. This comes in the form of different commands that are a part of different kinds of SQL statements:

 * DDL (Data Definition Language) with CREATE, ALTER, DROP, and TRUNCATE.
 * DML (Data Manipulation Language) with INSERT, UPDATE, MERGE, and DELETE.
 * DQL (Data Query Language) with the cla]]></description><link>https://www.thedataschool.co.uk/oscar-kriebel/a-long-story-short-sqls-stored-procedures-and-information-schema</link><guid isPermaLink="false">https://www.thedataschool.co.uk/oscar-kriebel/a-long-story-short-sqls-stored-procedures-and-information-schema</guid><pubDate>Wed, 22 Jul 2026 00:00:00 GMT</pubDate><dc:creator>Oscar Kriebel</dc:creator><image>https://images.unsplash.com/photo-1509021436665-8f07dbf5bf1d?crop=entropy&amp;cs=tinysrgb&amp;fit=max&amp;fm=jpg&amp;ixid=M3wxMTc3M3wwfDF8c2VhcmNofDR8fG9wZW4lMjBib29rfGVufDB8fHx8MTc4NDY0NDI3NXww&amp;ixlib=rb-4.1.0&amp;q=80&amp;w=2000</image></item><item><title><![CDATA[How to Use the RegEx Tool in Alteryx]]></title><description><![CDATA[Regular expressions, or Regex, allow you to search for patterns within text rather than looking only for one exact value.

In Alteryx, the RegEx tool has four output methods:

 * Match
 * Parse
 * Replace
 * Tokenize

They all use a Regex pattern, but they produce different outputs. This guide focuses on how to configure and use those four methods. For help building the pattern itself, see my longer Regex Reference Guide.


Configuring the RegEx Tool

 * Begin by adding the RegEx tool from the P]]></description><link>https://www.thedataschool.co.uk/holly-andersen/how-to-use-the-regex-tool-in-alteryx</link><guid isPermaLink="false">https://www.thedataschool.co.uk/holly-andersen/how-to-use-the-regex-tool-in-alteryx</guid><pubDate>Wed, 22 Jul 2026 00:00:00 GMT</pubDate><dc:creator>Holly Andersen</dc:creator><image>https://images.unsplash.com/photo-1548967199-79324abbe7dc?crop=entropy&amp;cs=tinysrgb&amp;fit=max&amp;fm=jpg&amp;ixid=M3wxMTc3M3wwfDF8c2VhcmNofDEwfHx0aWxlc3xlbnwwfHx8fDE3ODQ3MzIxMjF8MA&amp;ixlib=rb-4.1.0&amp;q=80&amp;w=2000</image></item><item><title><![CDATA[Joins and Unions in Alteryx]]></title><description><![CDATA[How the join tool works in Alteryx, what the different outputs are and how to get further join types using a union tool.]]></description><link>https://www.thedataschool.co.uk/sophie-yeung/joins-and-unions-in-alteryx</link><guid isPermaLink="false">https://www.thedataschool.co.uk/sophie-yeung/joins-and-unions-in-alteryx</guid><pubDate>Wed, 22 Jul 2026 00:00:00 GMT</pubDate><dc:creator>Sophie Yeung</dc:creator><image>https://images.unsplash.com/photo-1665512876792-1678ae34e489?crop=entropy&amp;cs=tinysrgb&amp;fit=max&amp;fm=jpg&amp;ixid=M3wxMTc3M3wwfDF8c2VhcmNofDExMHx8dHdvJTIwb3ZlcmxhcHBpbmclMjBjaXJjbGVzfGVufDB8fHx8MTc4NDczMTQ2OXww&amp;ixlib=rb-4.1.0&amp;q=80&amp;w=2000</image></item><item><title><![CDATA[SQL Day 1 in Python Pandas]]></title><description><![CDATA[Python’s pandas library offers all the same powerful data manipulation capabilities as SQL. In this post, I’ll walk through some basic translations from SQL over to Python, using the queries I ran during my first morning of SQL training as examples. We will be using both the pandas and numpy libraries.

Before we dive in, here are a few basic concepts to keep in mind:

 * Tables are DataFrames: In pandas, the equivalent of a SQL table is called a DataFrame.
 * Query Functions: I have written sep]]></description><link>https://www.thedataschool.co.uk/ben-hayward/sql-day-1-in-python-pandas</link><guid isPermaLink="false">https://www.thedataschool.co.uk/ben-hayward/sql-day-1-in-python-pandas</guid><pubDate>Wed, 22 Jul 2026 00:00:00 GMT</pubDate><dc:creator>Ben Hayward</dc:creator><image>https://images.unsplash.com/photo-1527118732049-c88155f2107c?crop=entropy&amp;cs=tinysrgb&amp;fit=max&amp;fm=jpg&amp;ixid=M3wxMTc3M3wwfDF8c2VhcmNofDF8fHBhbmRhfGVufDB8fHx8MTc4NDcxODMwMXww&amp;ixlib=rb-4.1.0&amp;q=80&amp;w=2000</image></item><item><title><![CDATA[Understanding Table Calculations in Tableau]]></title><description><![CDATA[Table calculations are a super useful part of Tableau, but they can often feel unpredictable.

This is because table calculations depend on the structure of the view. Unlike a normal calculation, which works with the underlying data, a table calculation works across the marks Tableau has already created.

So before asking what calculation you need, ask: What does one mark represent in this view?


Start with the Marks

Dimensions control the level of detail and therefore the number of marks in t]]></description><link>https://www.thedataschool.co.uk/holly-andersen/understanding-table-calculations-in-tableau</link><guid isPermaLink="false">https://www.thedataschool.co.uk/holly-andersen/understanding-table-calculations-in-tableau</guid><pubDate>Wed, 22 Jul 2026 00:00:00 GMT</pubDate><dc:creator>Holly Andersen</dc:creator><image>https://www.thedataschool.co.uk/content/images/2026/07/ChatGPT-Image-Jul-22--2026--02_55_57-PM.png</image></item><item><title><![CDATA[Cache and Run in Alteryx]]></title><description><![CDATA[When building a workflow that connects to an API or scrapes data from the web, you probably don't want to repeat the request every time you test a later step.

Repeated requests can cause you to:

 * Hit the API’s rate limit
 * Use up your request quota or usage allowance
 * Consume paid API credits
 * Put unnecessary strain on the website or service
 * Potentially have your access temporarily blocked

This is where Cache and Run Workflow can help.

Caching runs the workflow up to a selected too]]></description><link>https://www.thedataschool.co.uk/holly-andersen/cache-and-run-in-alteryx</link><guid isPermaLink="false">https://www.thedataschool.co.uk/holly-andersen/cache-and-run-in-alteryx</guid><pubDate>Wed, 22 Jul 2026 00:00:00 GMT</pubDate><dc:creator>Holly Andersen</dc:creator><image>https://images.unsplash.com/photo-1515974256630-babc85765b1d?crop=entropy&amp;cs=tinysrgb&amp;fit=max&amp;fm=jpg&amp;ixid=M3wxMTc3M3wwfDF8c2VhcmNofDEzfHxzYWZlfGVufDB8fHx8MTc4NDcyOTkyNXww&amp;ixlib=rb-4.1.0&amp;q=80&amp;w=2000</image></item><item><title><![CDATA[What I Learned About Dynamic Zone Visibility in Tableau]]></title><description><![CDATA[Watched The Information Lab's webinar on Dynamic Zone Visibility and it changed how I build dashboards. Instead of cramming everything on screen, you toggle sections on/off — and the layout actually reclaims the space. ]]></description><link>https://www.thedataschool.co.uk/mila-kholodiy/dynamic-zone-visibility-4</link><guid isPermaLink="false">https://www.thedataschool.co.uk/mila-kholodiy/dynamic-zone-visibility-4</guid><pubDate>Wed, 22 Jul 2026 00:00:00 GMT</pubDate><dc:creator>Mila Kholodiy</dc:creator><image>https://images.unsplash.com/photo-1626947466156-cc845e20d93c?crop=entropy&amp;cs=tinysrgb&amp;fit=max&amp;fm=jpg&amp;ixid=M3wxMTc3M3wwfDF8c2VhcmNofDEwMHx8ZHluYW1pY3xlbnwwfHx8fDE3ODQ3Mjc2NTB8MA&amp;ixlib=rb-4.1.0&amp;q=80&amp;w=2000</image></item><item><title><![CDATA[Drive Time Selection in Tableau Desktop]]></title><description><![CDATA[How it works:



Drive Time Selection lets you draw an isochrone polygon directly on a map that reflects how far you can realistically travel within a given time, based on actual road networks and traffic conditions.

Simply start by having location-based marks with longitude/latitude in columns/rows. For the example below, I used Superstore data and placed postcode on Detail.


Setting up drive time and using it:



Personally, Street view makes the most sense, as it lets you identify whether t]]></description><link>https://www.thedataschool.co.uk/jude-royall/drive-time-selection-in-tableau-desktop</link><guid isPermaLink="false">https://www.thedataschool.co.uk/jude-royall/drive-time-selection-in-tableau-desktop</guid><pubDate>Wed, 22 Jul 2026 00:00:00 GMT</pubDate><dc:creator>Jude Royall</dc:creator><image>https://www.thedataschool.co.uk/content/images/2026/07/Still-image.png</image></item><item><title><![CDATA[Cross Tab and Transpose in Alteryx]]></title><description><![CDATA[When reshaping data, you will often need to convert columns into rows or rows into columns.

The names used for this process vary between tools:

 * In Tableau Prep, you might see Pivot Columns to Rows and Pivot Rows to Columns.
 * In Power Query, these operations are called Unpivot and Pivot.
 * In Alteryx, the equivalent tools are called Transpose and Cross Tab.

Fortunately, the Alteryx tool icons give you a helpful visual clue:

 * Transpose converts columns into rows.
 * Cross Tab converts ]]></description><link>https://www.thedataschool.co.uk/holly-andersen/cross-tab-and-transpose-in-alteryx</link><guid isPermaLink="false">https://www.thedataschool.co.uk/holly-andersen/cross-tab-and-transpose-in-alteryx</guid><pubDate>Wed, 22 Jul 2026 00:00:00 GMT</pubDate><dc:creator>Holly Andersen</dc:creator><image>https://www.thedataschool.co.uk/content/images/2026/07/ChatGPT-Image-Jul-22--2026--02_25_42-PM.png</image></item><item><title><![CDATA[DAX vs. Table Calculations: What Rebuilding My Power BI Dashboard in Tableau Would Actually Require]]></title><description><![CDATA[Potential rebuilding my Power BI dashboard in Tableau, the cards and maps ported over easily. The running total didn't. DAX re-filters the model at every point; Tableau just adds up rows in an already-built grid. Same output, completely different engine underneath — and different ways it breaks.]]></description><link>https://www.thedataschool.co.uk/mila-kholodiy/powerbivstableau</link><guid isPermaLink="false">https://www.thedataschool.co.uk/mila-kholodiy/powerbivstableau</guid><pubDate>Wed, 22 Jul 2026 00:00:00 GMT</pubDate><dc:creator>Mila Kholodiy</dc:creator><image>https://images.unsplash.com/photo-1520808276357-84e6e526d33d?crop=entropy&amp;cs=tinysrgb&amp;fit=max&amp;fm=jpg&amp;ixid=M3wxMTc3M3wwfDF8c2VhcmNofDV8fGRpZmZlcmVuY2UlMjByZWR8ZW58MHx8fHwxNzg0NzI1NjU5fDA&amp;ixlib=rb-4.1.0&amp;q=80&amp;w=2000</image></item><item><title><![CDATA[Connecting dbt to Snowflake Using Key-Pair Authentication]]></title><description><![CDATA[When I recently completed the dbt Fundamentals course, I needed to connect dbt to a Snowflake account.

If you've connected the two platforms before, you may remember being able to authenticate using a username and password. However, this is no longer supported for new connections to Snowflake. These now now require one of the newer authentication methods, such as key-pair authentication.


What is key-pair authentication?

Instead of authenticating with a password, you create two cryptographic ]]></description><link>https://www.thedataschool.co.uk/martin-regan/connecting-dbt-to-snowflake-using-key-pair-authentication</link><guid isPermaLink="false">https://www.thedataschool.co.uk/martin-regan/connecting-dbt-to-snowflake-using-key-pair-authentication</guid><pubDate>Wed, 22 Jul 2026 00:00:00 GMT</pubDate><dc:creator>Martin Regan</dc:creator><image>https://images.unsplash.com/photo-1571975795053-950dbde7dacc?crop=entropy&amp;cs=tinysrgb&amp;fit=max&amp;fm=jpg&amp;ixid=M3wxMTc3M3wwfDF8c2VhcmNofDIzfHxrZXl8ZW58MHx8fHwxNzg0NzI1MDcwfDA&amp;ixlib=rb-4.1.0&amp;q=80&amp;w=2000</image></item></channel></rss>