Map raw data file to formatted table @param table_name: name of table @param df: DataFrame of raw data @param report_columns: List of col

Load a gzipped json file from the specified location, either local or s3

Compares two given replays using aggregated_data/ from S3 @param bucket: str, S3 bucket location @param replay1: str, replay id 1 @param

End to end data collection, parsing, analysis and pdf generation @param replay: str, replay id from replay.py @param cluster_endpoint: str, t

saving the extracted logs in S3 location in the following format: connections.json, copy_replacements.csv, SQLs.json.gz :para

Change the log level for the default (stdio) logger. Logfile always logs DEBUG.

Setup logging configuration

create a queue for passing jobs to the workers. the limit will cause put() to block if the queue is full

Returns a representation of the entry suitable as an entry in the Redshift Manifest JSON representation.

Iterates through sql/ and executes UNLOAD with queries on provided cluster @param unload_location: S3 bucket location for unloaded data @para

Unloading the system table if the unload location and unload iam user is mentioned in the extract.yaml file. :param source_cl

validate filters and set defaults. The object just needs to provide a supported_filters() function.

Validate that all required configuration parameters are present based on cluster_type

Validate log results from get_logs() :param connections: number of connection logs :param audit_logs: number of audit logs :r