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.

The  tab-separated values tsv file format is similar to the csv (Comma Separated Values) file format. The main difference is that values are separated by a tab instead of a comma. 

Both csv and tsv files often have a header row with column names at the top of the file. Then there are rows with one value for each of the columns.

The TSV converter can convert your tsv files into JSON, CSV, or Parquet. 

How To Convert TSV files to Another File Format

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TSV conversion will happen automatically and your converted file will download

TSV files can be opened with most text editing applications. They can also be opened for data analysis using microsoft excel or by uploading to your datasets. Uploading your tsv files to microsoft excel might cause them to be converted into xls files. 

CSV and tsv files are both useful for storing tabular data. There are a lot of applications that can open and edit them on your computer. 

The main drawback of csv and tsv files is that they do not compress very efficiently. To store  tabular data efficiently you can use the Parquet file format. The tsv to parquet converter can convert your tsv file into a parquet file. The main drawback of parquet files is that not many applications can open and read them.

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