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In Hive, the process for storing and accessing a Map with key-value pairs of String data type involves the following steps:

1. Create a table with a column of type Map<string,string>.
2. Define the key and value fields in the Map column.
3. Insert the data into the table by specifying the key-value pairs in the Map column.
4. Query the table by referencing the key and value fields in the Map column.

For example, to create a table with a Map column in Hive:

CREATE TABLE mytable (id INT, mymap MAP<STRING, STRING>);

To insert data into the table:

INSERT INTO mytable VALUES (1, map('key1', 'value1', 'key2', 'value2'));

To query the data:

SELECT id, mymap['key1'] FROM mytable WHERE id = 1;

In Spark, the process is similar:

1. Create a DataFrame with a column of type MapType(StringType, StringType).
2. Define the key and value fields in the Map column.
3. Insert the data into the DataFrame by specifying the key-value pairs in the Map column.
4. Query the DataFrame by referencing the key and value fields in the Map column.

For example, to create a DataFrame with a Map column in Spark:

val mydf = Seq((1, Map("key1" -> "value1", "key2" -> "value2"))).toDF("id", "mymap")

To query the data:

mydf.select($"id", $"mymap"("key1")).where($"id" === 1).show()