Awk and Sed aren't very accessible to most people who did not grow up learning those tools.
The whole point of tools built on top of Hadoop (Hive/Pig/HBase) is to make large scale data processing more accessible (by hiding the map-reduce as much as possible). Not everyone will want to write a Java map-reduce in Hadoop. However, many can write a HiveQL statement or Pig textual script. Amazon Redshift brings it even farther - they are a Postgres compatible database, meaning you can connect your Crystal Reports/Tableau data analysis tool to it, treating it like a traditional SQL database.
I think the author's point was that the example in question was orders of magnitude smaller than "big data" and that it was more efficient to process it on a single machine, not that Hadoop and friends aren't easy to use.
The whole point of tools built on top of Hadoop (Hive/Pig/HBase) is to make large scale data processing more accessible (by hiding the map-reduce as much as possible). Not everyone will want to write a Java map-reduce in Hadoop. However, many can write a HiveQL statement or Pig textual script. Amazon Redshift brings it even farther - they are a Postgres compatible database, meaning you can connect your Crystal Reports/Tableau data analysis tool to it, treating it like a traditional SQL database.