Parquet and HDFS are two different file formats for storing data in Hadoop clusters. Parquet stores data in a flat columnar format, which is more efficient in terms of storage and performance compared to the traditional row-oriented approach.
ORC, Avro, and Parquet are the three optimized file formats for use in Hadoop clusters.
Parquet and Avro are the two most popular formats, and a benchmark study by SlideShare found that Parquet is more efficient in terms of storage and performance than Avro.
Parquet, an open-source file format for Hadoop, stores nested data structures in a flat columnar format .
Compared to a traditional approach where data is stored in a row-oriented approach, Parquet file format is more efficient in terms of storage and performance.
Big Data File Formats
The big data community has settled on three optimized file formats for use in Hadoop clusters: Optimized Row Columnar (ORC), Avro, and Parquet. These formats provide compression, scalability, and parallel processing, but also have their own advantages and disadvantages. Nexla's Data Convertor is a tool for managing data and converting formats, and provides a whitepaper on the three formats.
Big Data File Formats Demystified
Choosing an HDFS data storage format- Avro vs. Parquet and more - Sta…
Parquet provides significant benefits for sparse reads of large datasets, but is it ... I have heard some folks argue in favor of Avro vs Parquet. Such ...
Should you use Parquet?
Feather vs Parquet The obvious question that comes to mind when discussing parquet, is how does it compare to the feather format. Feather is optimised for ...
Understanding the Parquet file format
We created Parquet to make the advantages of compressed, efficient columnar data representation available to any project in the Hadoop ecosystem.