Metadata-Version: 2.1
Name: athena-usage-metrics-extractor
Version: 1.2.0
Summary:  Athena usage is simple python library that allows you to extract all usage information for given date range and for given workgroup 
Home-page: https://github.com/soumilshah1995/AppleStock
Author: Soumil Nitin Shah
Author-email: shahsoumil519@gmail.com
License: MIT
Description: # Athena Usage Extractor 
        
        [![license](https://img.shields.io/github/license/mashape/apistatus.svg?maxAge=2592000)]
        
        * 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
        
        [![license](https://img.shields.io/github/license/mashape/apistatus.svg?maxAge=2592000)]
        
        
        # 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 masterâ€™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
        
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
