Generate checkpoint file key

Load SQL template from file

Log pipeline metrics and send alerts

Insert data in batches

Build a Kafka sink. :param topic: The topic to write to. :param bootstrap_servers: The bootstrap servers. :return: The Kafka sink.

Build a Kafka source. :param bootstrap_servers: The bootstrap servers. :param topics: The topics to read from. :param group_id: The

Check if MinIO connection is working

Yield successive chunks of given size from data_list.

Create table and indexes based on schema definition

Extract payload from nested record structure

Generate a unique hash for a record based on business keys

Return a dictionary of available jobs with lazy loading

Get field name from field definition, handling both schema formats.

Validate raw data using both Great Expectations and custom validation

Create SQLAlchemy engine with proper configuration.

Check if a string is a valid timestamp.

Add derived columns based on existing data

Task group for the bronze layer of the data pipeline.

bucketQuantile calculates the quantile 'q' based on the given buckets. The buckets will be sorted by upperBound by this function (i.e. no sorting need

Calculate metrics per user session

Remove duplicate records based on record_hash

Check if a Kafka topic exists.

coalesceBuckets merges buckets with the same upper bound. The input buckets must be sorted.

Create useful views for analysis

Create category dimension table

Create date dimension table

Create product dimension table

Create user dimension table

Create fact table with foreign keys to dimensions

Create and return the job pipeline

Create a table with the proper schema for feature storage.

### Data Pipeline DAG This DAG processes data through three layers: * Bronze: Raw data ingestion and validation * Silver: Data trans

Deploy all configured connectors

Deploy a specific connector type

Add metadata to a record

The assumption that bucket counts increase monotonically with increasing upperBound may be violated during: * Recording rule evaluation of histogram_

Create MinIO bucket if it doesn't exist

Flatten nested record structure

Load configuration from Airflow Variables

Use the S3 paginator to get all keys without loading them all at once.

Get the job class, loading it only when requested

Create and return a MinIO client

Task group for the gold layer of the data pipeline.

Initialize feature tables in PostgreSQL if they don't exist.

Load processed keys from checkpoint file

Load dimensional model into Data Warehouse

Load the latest model version and its category mappings

Load and prepare sample data from parquet file

Load training data from DWH :param row_limit: Number of rows to load :return: Dictionary with training data

Patch logging proxy objects to handle encoding attribute

Convert DataFrame to serializable format

End of copied code.

Read SQL query from file.

Save processed keys to checkpoint file

Save training results and metrics

Task group for the silver layer of the data pipeline.

Replace environment variables in the config

Train final model with best parameters

### ML Training Pipeline with Hyperparameter Tuning This DAG handles the end-to-end training process: 1. Load training data 2. Tune h

Transform the validated data

Convert event_time to datetime and add derived time columns

Tune XGBoost hyperparameters using Ray Tune

Validate a field's type against its schema definition.

Validate a field's value constraints.

Validate a single record against business rules

Write a batch of data to PostgreSQL table.

Get online features for prediction

Initialize PostgreSQL connection parameters.