Using Apache Parquet to store data
Are you a data scientist using CSV files to store your data? What if I told you there is a better way? Can you imagine a
file format to save your datasets?
Do not get me wrong. I love CSVs. You can open them with any text editor, inspect them and share them with others. They have become the standard file format for datasets in the AI/ML community.
However, they have a little problem…
CSV files are stored as a list of rows (aka row-oriented), which causes 2 problems: — they are slow to query → SQL and CSV do not play well together. — they are difficult to store efficiently → CSV files take a lot of disk space.
Is there an alternative to CSVs? Yes!
Apache Parquet is a columnar storage format available to any project […], regardless of the choice of data processing framework, data model or programming language.
As an alternative to the CSV row-oriented format, we have a column-oriented format: Parquet.
Parquet is an open-source format for storing data, licensed under Apache. Data engineers are used to Parquet. But, sadly, data scientists are still lagging behind.
How is Parquet format different from CSV? Let’s imagine you have this dataset.
Internally, the CSV file stores the data based on its rows
Parquet, on the other hand, stores the data based on its columns.
Why column-storing is better than row-storing you ask? 2 technical reasons and 1 business reason.
Tech reason #1: Parquet files are much smaller than CSV In Parquet, files are compressed column by column, based on their data type, e.g. integer, string, date. A CSV file of 1TB becomes a Parquet file of around 100GB (10% of the original size)
Tech reason #2: Parquet files are much faster to query Columnar data can be scanned and extracted much faster. For example, an SQL query that selects and aggregates a subset of columns does not need to scan the other columns. This reduces I/O and results in faster queries.
Business reason #3: Parquet files are cheaper. Storage services like AWS S3 or Google Cloud Storage charge you based on the data size or the amount of data scanned. Parquet files are lighter and faster to scan, which means you can store the same data at a fraction of the cost.
And now the cherry on top of the cake: Working with Parquet files in Pandas is as easy as working with CSVs
- Wanna read a data file?
Stop doing: pd.read_csv('file.csv')
instead do: pd.read_parquet('file.parquet')
- Wanna save data to disk?
Stop doing: df.to_csv('my_file.csv')
instead, do : df.to_parquet('my_file.parquet')
- Trick: Wanna transform all your old CSV files into Parquet? Simple. pd.read_csv('my_file.csv').to_parquet('my_file.parquet')
In summary, Parquet is better than other storage formats because it provides several advantages such as:
- Columnar storage layout which allows for efficient compression and encoding of data, resulting in smaller storage size and faster read/write operations.
- Schema evolution support, which allows for adding or removing columns from a table without requiring a full rewrite of the data.
- Predicate pushdown support, which allows for filtering data at the storage level rather than reading and filtering in the application level, resulting in faster query execution.
- Widely supported by various big data processing frameworks such as Apache Spark, Hive, and Impala.
- Parquet is designed for high-performance big data processing and storage, which is suitable for big data storage and data analytics.
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