From f0e4508c23a5dcedaa3f8b58f58ddb7e379aadb7 Mon Sep 17 00:00:00 2001 From: twoentartian Date: Mon, 9 Sep 2024 20:59:59 +0200 Subject: [PATCH] Fix broken links. --- src/getting-started/apache-spark/dataframe-and-dataset.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/getting-started/apache-spark/dataframe-and-dataset.md b/src/getting-started/apache-spark/dataframe-and-dataset.md index 06d60a5..33e27fd 100644 --- a/src/getting-started/apache-spark/dataframe-and-dataset.md +++ b/src/getting-started/apache-spark/dataframe-and-dataset.md @@ -9,7 +9,7 @@ is where DataFrames originate from. Spark has an optimized SQL query engine that can optimize the compute path as well as provide a more efficient representation of the rows when given a schema. From the [Spark SQL, DataFrames and Datasets -Guide](https://spark.apache.org/docs/3.1.2/sql-programming-guide.html#overview): +Guide](https://spark.apache.org/docs/latest/sql-programming-guide.html#overview): > Spark SQL is a Spark module for structured data processing. Unlike the basic > Spark RDD API, the interfaces provided by Spark SQL provide Spark with more @@ -85,7 +85,7 @@ StructField(numD,DoubleType,false), StructField(numE,LongType,false), StructField(numF,DoubleType,false)) ``` -An overview of the different [Spark SQL types](https://spark.apache.org/docs/3.1.2/api/scala/org/apache/spark/sql/types/index.html) +An overview of the different [Spark SQL types](https://spark.apache.org/docs/latest/api/scala/org/apache/spark/sql/types/index.html) can be found online. For the timestamp field we need to specify the format according to the [Javadate format](https://docs.oracle.com/javase/8/docs/api/java/text/SimpleDateFormat.html) —in our case `MM/dd/yy:hh:mm`. Tying this all together we can build a Dataframe