Apache Arrow is a columnar format for storing tabular data in memory. It works well with the Apache Parquet format when doing data analysis.

Arrow

Apache Arrow works well as an in-memory data format. Arrow data can be shared between multiple libraries without copying, serialization or desearialization.

Apache Arrow and Apache Parquet are often used together. Tabular data can be stored in Apache Parquet format, which compresses well, and converted to Apache Arrow for analysis. Arrow does not compress as well as Parquet, but performs better for data analysis in RAM.

Apache Parquet is a columnar format for storing tabular data and compresses well. It compresses well and works well with the Apache Arrow data format when doing data analysis.

A Parquet file stores data in a columnar format. Parquet files often have high compression ratios. This makes them small compared to a CSV file or JSON file that stores the same data.

Parquet files also deserialize nicely into Apache Arrow format. The Arrow format is used by many in-memory analytics systems. This makes Parquet a more efficient format for data systems than CSV files

Parquet

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Parquet files store tabular data in a coumnar format. This format allows the data to compress efficiently, and it is efficient to query from data systems.

Data systems can query small parts of a Parquet file without having to read the whole file. This process is known as predicate pushdown.

Each column of a Parquet file can be read independently. If only one column is required from the Parquet file, then the querying system can choose to only read that column from the disk. This is not possible with other storage formats like CSV files.

Parquet files store metadata about chunks of rows. This includes the max and min values for each column. This enables data systems to query specific row ranges from a Parquet file without reading the entire file from a disk.

For example, if one column in a Parquet file is sorted by timestamp, and a data system only needs data from the last 3 weeks. Then the system can read row batches that have data from the last three weeks. This reduces the amount of data that needs to be read from disk.

Parquet files are stored in a binary format. This means that they cannot be opened and viewed by most computer applications. 

Parquet files are well supported by some data systems, but they are not very well supported by software applications on Windows or Mac.

The easiest way to read data from a Parquet file is to convert it to a CSV file.  A lot of applications support CSV files.

It can be difficult to analyze data in Parquet files without writing software or converting them to CSV first. One reason I built We Do Data Science is to enable people to analyze data in Parquet files without needing to write software. 

You can start analyzing the data in a Parquet file by uploading it to your datasets and then clicking explore.

Parquet is really useful when you want to upload or download a table of data, but it is not very well supported by other websites or applications.

If you want to upload your data to another website or application, then CSV is a good alternative.

Convert Arrow To Parquet

How to Convert from Arrow to Parquet