Nested JSON data processing using Apache Spark with Coding

1. Nested JSON Data processing using Apache Spark

2. Instructions for use Let us read a public JSON dataset available on the internet. Extract required fields from nested data, and analyze the dataset to get some insights. Here I’m using the Baby names public data set available on the internet for this demo. What are we performing in this demo? ╺ Read data from the URL using scala API ╺ Convert the read data into a dataframe ╺ Extract the required fields from the nested JSON dataset ╺ Analyze the data by writing queries ╺ Visualize the processed data 2

3. Let us read a public JSON dataset available on the internet. Extract required fields from nested data, and analyze the dataset to get some insights. Here I’m using the Baby names public data set available on the internet for this demo. After this, we use the jsonString Val created above and create a dataframe using Spark API. We need to import spark.implicits to convert Sequence of Strings to a Dataset, and then we create a dataframe out of it. 3

4. Now let us see the schema of the JSON using printSchema method:

5. Now let us see the schema of the JSON using printSchema method: |-- data: array (nullable = true) | |-- element: array (containsNull = true) | | |-- element: string (containsNull = true)) Also, it contains metadata about the data, let’s not worry about it, for now. But you can have a look at it when you run this in your machine. Mainly it contains columns field information in metadata, which I have extracted for you to have a better understanding of the data we will work on. 5

6. We have below fields within an Array of data that we are going to analyze. Sid meta ╺ ╺ Id Year ╺ ╺ Position first_name ╺ ╺ created_at County ╺ ╺ created_meta Sex ╺ ╺ updated_at Count ╺ ╺ updated_meta ╺ 6

7. But how we can extract these data fields from JSON? Now let’s select data from the jsonDF dataframe we created. It looks something like this 7

8. Now we have to extract the fields within this data. To do this, let us first create a temporary view of this dataframe and use explode function to extract Year, Name, County, and gender fields. To use explode method, we should first import spark sql functions. 8

9. Now let us see the schema of the insightData. 9

10. Let me show you the contents of insightData datafrmae using the display method available in Databricks. 10

11. Now let us write a query to see what is the most popular first letter baby names to start within each year. insightData.select("year","name").createOrReplaceTempView("yearname") val dis=spark.sql("select year,firstLetter,count,ranks from (select year,firstLetter,count ,rank() over (partition by year order by count desc) as ranks from (select year, left(name,1) as firstLetter, count(1) as count from yearname group by year ,firstLetter order by year desc,count desc)Y )Z where ranks=1 order by year desc") 11

12. Now let’s visualize this data using the graphs available in Databricks. 12

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16. Thank you 16