# S3 / Object Storage
source: https://docs.chalk.ai/docs/s3

## Integrate with S3 compatible data sources.

### Reading .csv files

Chalk parses .csv files from s3 or the local file system
with the function DataFrame.read_csv(...).

If your .csv has headers, you can tell Chalk which
columns to parse into which features by providing
a mapping in the columns keyword argument:

```
DataFrame.read_csv(
    path="s3://your-bucket/path/to/file.csv",
    columns={"uid": User.uid, "fraud score": User.socure_score},
)
```

The first line of the file is taken to be the header
row, and subsequent lines are parsed into the provided
features.

The returned value matches the type:

```
DataFrame[User.uid, User.socure_score]
```

Alternatively, you can map the file into features by
column number in the .csv, and optionally skip rows
that might contain header fields or summary stats:

```
DataFrame.read_csv(
    path="s3://your-bucket/path/to/file.csv",
    columns={0: User.uid, 1: User.socure_score},
    skip_rows=1
)
```

### Reading .parquet files

Reading .parquet files works just like reading .csv files.
As with .csv, Chalk parses .parquet files from s3 or the
local file system, this time with the function
DataFrame.read_parquet(...).
Otherwise, the functionality provided is the same as provided
above for reading .csv files.

### Authentication

Chalk can connect to AWS S3 or GCP Cloud Storage
for reading .csv and .parquet files.
Chalk will use the application credentials that you
set up in the AWS and GCP integrations sections.