# Athena Usage Extractor
[]
* Athena helps you analyze unstructured, semi-structured, and structured data stored in Amazon S3. Examples include CSV, JSON, or columnar data formats such as Apache Parquet and Apache ORC. You can use Athena to run ad-hoc queries using ANSI SQL, without the need to aggregate or load the data into Athena.
* Athena usage is simple python library that allows you to extract all usage information
## Installation
[]
# AthenaUsageExtractor
* Athena helps you analyze unstructured, semi-structured, and structured data stored in Amazon S3. Examples include CSV, JSON, or columnar data formats such as Apache Parquet and Apache ORC. You can use Athena to run ad-hoc queries using ANSI SQL, without the need to aggregate or load the data into Athena.
* Athena usage is simple python library that allows you to extract all usage information
## Installation
ac
```bash
pip install athena-usage-metrics-extractor
```
## Usage
```python
import sys
from AthenaUsageExtractor import AthenaUsageExtractor
def main():
helper = AthenaUsageExtractor(
aws_region='us-east-1',
aws_access_key='XXXXX',
aws_secret_key='XXXXX'
)
response = helper.get_usage_for_date(date='2022-08-12', workgroup='primary')
while True:
try:
data = next(response)
print(data)
except StopIteration as e:
break
except Exception as e:
break
main()
```
## Json format Returned
```json
{
"QueryExecutionId":"490024e6-3e89-4ec4-9ffd-b1302a77d33d",
"Query":"<YOU WILL GET THE QUERY USER RAN >",
"StatementType":"DML",
"WorkGroup":"primary",
"OutputLocation":"<AWS S3 Output Path >",
"Database":"default",
"SelectedEngineVersion":"AUTO",
"EffectiveEngineVersion":"Athena engine version 2",
"EngineExecutionTimeInMillis":"14045",
"DataScannedInBytes":"59597591861",
"TotalExecutionTimeInMillis":"14292",
"QueryQueueTimeInMillis":"214",
"QueryPlanningTimeInMillis":"960",
"ServiceProcessingTimeInMillis":"33",
"State":"SUCCEEDED",
"SubmissionDateTime":"2022-08-12 13:56:07.837000-04:00",
"CompletionDateTime":"2022-08-12 13:56:22.129000-04:00"
}
```
## Authors
* **Soumil Nitin Shah**
## Soumil Nitin Shah
Bachelor in Electronic Engineering |
Masters in Electrical Engineering |
Master in Computer Engineering |
* Website : https://soumilshah.herokuapp.com
* Github: https://github.com/soumilshah1995
* Linkedin: https://www.linkedin.com/in/shah-soumil/
* Blog: https://soumilshah1995.blogspot.com/
* Youtube : https://www.youtube.com/channel/UC_eOodxvwS_H7x2uLQa-svw?view_as=subscriber
* Facebook Page : https://www.facebook.com/soumilshah1995/
* Email : shahsoumil519@gmail.com
* projects : https://soumilshah.herokuapp.com/project
I earned a Bachelor of Science in Electronic Engineering and a double master’s in Electrical and Computer Engineering. I have extensive expertise in developing scalable and high-performance software applications in Python. I have a YouTube channel where I teach people about Data Science, Machine learning, Elastic search, and AWS. I work as data collection and processing Team Lead at Jobtarget where I spent most of my time developing Ingestion Framework and creating microservices and scalable architecture on AWS. I have worked with a massive amount of data which includes creating data lakes (1.2T) optimizing data lakes query by creating a partition and using the right file format and compression. I have also developed and worked on a streaming application for ingesting real-time streams data via kinesis and firehose to elastic search
## License
This project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details