Choosing A Hadoop Data Format

Silvia Oliveros has a set of considerations to help you choose a file format for your data in Hadoop:

What does your pipeline look like, and what steps are involved?

Some of the file formats were optimized to work in certain situations. For example, Sequence files were designed to easily share data between Map Reduce (MR) jobs, so if your pipeline involves MR jobs then Sequence files make an excellent option. In the same vein, columnar data formats such as Parquet and ORC were designed to optimize query times; if the final stage of your pipeline needs to be optimized, using a columnar file format will increase speed while querying data.

At first, I’d suggest just using delimited files, as it’s easiest that way.  Once you have developed a bit of Hadoop maturity, then it makes sense to think about whether rowstore formats (like Parquet and Avro) or columnstore formats (like ORC) make sense for a particular data set.

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