getChan commented on code in PR #66:
URL: https://github.com/apache/datafusion-site/pull/66#discussion_r2045138173
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content/blog/2025-04-17-user-defined-window-functions.md:
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+---
+layout: post
+title: User defined Window Functions in DataFusion
+date: 2025-04-17
+author: Aditya Singh Rathore
+categories: [tutorial]
+---
+
+<!--
+{% comment %}
+Licensed to the Apache Software Foundation (ASF) under one or more
+contributor license agreements. See the NOTICE file distributed with
+this work for additional information regarding copyright ownership.
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+(the "License"); you may not use this file except in compliance with
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+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
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+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+{% endcomment %}
+-->
+
+
+Window functions are a powerful feature in SQL, allowing for complex
analytical computations over a subset of data. However, efficiently
implementing them, especially sliding windows, can be quite challenging. With
[Apache DataFusion]'s user-defined window functions, developers can easily take
advantage of all the effort put into DataFusion's implementation.
+
+In this post, we'll explore:
+
+- What window functions are and why they matter
+
+- Understanding sliding windows
+
+- The challenges of computing window aggregates efficiently
+
+- How to implement user-defined window functions in DataFusion
+
+
+[Apache DataFusion]: https://datafusion.apache.org/
+
+## Understanding Window Functions in SQL
+
+
+Imagine you're analyzing sales data and want insights without losing the finer
details. This is where **[window functions]** come into play. Unlike **GROUP
BY**, which condenses data, window functions let you retain each row while
performing calculations over a defined **range** —like having a moving lens
over your dataset.
+
+[window functions]: https://en.wikipedia.org/wiki/Window_function_(SQL)
+
+
+Picture a business tracking daily sales. They need a running total to
understand cumulative revenue trends without collapsing individual
transactions. SQL makes this easy:
+```sql
+SELECT id, value, SUM(value) OVER (ORDER BY id) AS running_total
+FROM sales;
+```
Review Comment:
Why did you put the query in here? It seems unrelated to the context.
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