[Python] High memory usage writing pyarrow.Table with large strings to parquet

My case of datasets stored is specific. I have large strings (1-100MB each).

Let's take for example a single row.

43mb.csv is a 1-row CSV with 10 columns. One column a 43mb string.

When I read this csv with pandas and then dump to parquet, my script consumes 10x of the 43mb.

With increasing amount of such rows memory footprint overhead diminishes, but I want to focus on this specific case.

Here's the footprint after running using memory profiler:
```java

Line # Mem usage Increment Line Contents
================================================
4 48.9 MiB 48.9 MiB @profile
5 def test():
6 143.7 MiB 94.7 MiB data = pd.read_csv('43mb.csv')
7 498.6 MiB 354.9 MiB data.to_parquet('out.parquet')

```
Is this typical for parquet in case of big strings?

**Environment**: Mac OSX
**Reporter**: [Bogdan Klichuk](https://issues.apache.org/jira/browse/ARROW-7305)
#### Related issues:
- [[C++] Research jemalloc memory page reclamation configuration on macOS when background_thread option is unavailable](https://github.com/apache/arrow/issues/23308) (relates to)
#### Original Issue Attachments:
- [50mb.csv.gz](https://issues.apache.org/jira/secure/attachment/12989053/50mb.csv.gz)

_{**Note**: *This issue was originally created as [ARROW-7305](https://issues.apache.org/jira/browse/ARROW-7305). Please see the [migration documentation](https://github.com/apache/arrow/issues/14542) for further details.*}