March 2019
Beginner to intermediate
182 pages
4h 6m
English
Content preview from Hands-On Big Data Analytics with PySpark
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Summary
In this chapter, we learned how to save data in plain text format. We noticed that schema information is lost when we do not load the data properly. We then learned how to leverage JSON as a data format and saw that JSON retains the schema, but it has a lot of overhead because the schema is for every record. We then learned about CSV and saw that Spark has embedded support for it. The disadvantage of this approach, however, is that the schema is not about the specific types of records, and tabs need to be inferred implicitly. Toward the end of this chapter, we covered Avro and Parquet, which have columnar formats that are also embedded with Spark.
In the next chapter, we'll be working with Spark's key/value API.
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.