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ETL job from CSV to Parquet in AWS S3

Project description

# S3 Parquetifier
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S3 Parquetifier is an ETL tool that can take a file from an S3 bucket convert it to Parquet format and
save it to another bucket.

S3 Parquetifier supports the following files
- [x] CSV
- [ ] JSON
- [ ] TSV

## Instructions

### How to install

To install the package just run the following

```python
pip install s3-parquetifier
```

### How to use it

S3 parquetifier needs an AWS Account that will have at least read rights for the target bucket
and read-write rights for the destination bucket.

You can read the following article on how to set up S3 roles and policies [here](https://docs.aws.amazon.com/IAM/latest/UserGuide/reference_policies_examples_s3_rw-bucket.html)

#### Running the Script

```python
from s3_parquetifier import S3Parquetifier

# Call the covertor
S3Parquetifier(
aws_access_key='<your aws access key>',
aws_secret_key='<your aws secret key>',
region='<the region>',
verbose=True, # for verbosity or not
source_bucket='<the bucket's name where the CSVs are>',
target_bucket='<the bucket's name where you want the parquet file to be saved>',
type='S3' # for now only S3
).convert_from_s3(
source_key='<the key of the object or the folder>',
target_key='<the key of the object or the folder>',
file_type='csv' # for now only CSV,
chunk_size=100000, # The number of rows per parquet
dtype=None, # A dictionary defining the types of the columns
skip_rows=None, # How many rows to skip per chunk
compression='gzip', # The compression type
keep_original_name_locally=True # In order to keep the original filename or create a random when downloading the file
)
```

### Adding custom pre-processing function

You can add custom pre-processing function on your source file. Because this tool is designed for large files the preprocessing
is taking place on every chunk separately. If the full file is needed for the preprocessing then a local preprocessing is needed in the source file.

In the following example, we are going to add custom columns on the chunk with some custom values.
We are going to add the columns `test1, test2, test3` with the values `1, 2, 3` respectively.

We define our function bellow named `pre_process` and we also define the arguments for the function `kwargs`.
The chunk DataFrame is not needed in the kwargs, it is taken by default. You have to pass your function as an argument in
`pre_process_chunk` and the arguments in `kwargs` in the `convert_from_s3` method.

```python
from s3_parquetifier import S3Parquetifier


# Add three new columns with custom values
def pre_process(chunk, columns=None, values=None):

for index, column in enumerate(columns):
chunk[column] = values[index]

return chunk

# define the arguments for the pre-processor
kwargs = {
'columns': ['test1', 'test2', 'test3'],
'values': [1, 2, 3]
}

# Call the covertor
S3Parquetifier(
aws_access_key='<your aws access key>',
aws_secret_key='<your aws secret key>',
region='<the region>',
verbose=True, # for verbosity or not
source_bucket='<the bucket's name where the CSVs are>',
target_bucket='<the bucket's name where you want the parquet file to be saved>',
type='S3' # for now only S3
).convert_from_s3(
source_key='<the key of the object or the folder>',
target_key='<the key of the object or the folder>',
file_type='csv' # for now only CSV,
chunk_size=100000, # The number of rows per parquet
dtype=None, # A dictionary defining the types of the columns
skip_rows=None, # How many rows to skip per chunk
compression='gzip', # The compression type
keep_original_name_locally=True, # In order to keep the original filename or create a random when downloading the file
pre_process_chunk=pre_process, # A preprocessing function that will pre-process the each chunk
kwargs=kwargs # potential extra arguments for the pre-preocess function
)
```

## ToDo

- [ ] Add support for JSON
- [ ] Add streaming from url support
- [ ] Add support to handle local files too


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