Data Pipeline

KAI data pipeline process typically consists of several stages:

1. Data Extraction:

   • In this stage, we collect data from structured, semi-structured, and unstructured data from various sources both internal and external including databases, web services, logs, or external APIs. The extraction process involves remote connectivity establishing, querying, reading, or pulling data from these sources.

2. Data Transformation:

   • Once extracted, we cleaned, normalized, or transformed data to the required format that fit the desired schema. Transformation steps may include cleansing missing, duplicating, filtering out irrelevant data, joining datasets, or aggregating information.

3. Data Loading:

   • Transformed data is loaded into a destination, such as a data warehouse, data lake, or analytical database. This is where the data becomes available for analysis.

4. Data Processing:

   • In some cases, we require additional data processing or computation to be performed as part of the pipeline. This could involve running machine learning models, calculations, or generating derived datasets.

5. Monitoring and Error Handling:

   • We always monitor data pipelines for errors and failures. When issues occur, we handle and fix the error very carefully so production would not be hampered. Auto alerts are generated when dependencies are not found to build a response file as well as always check the logs to find the route cause and fix accordlingly.

6. Data Validation:

   • Data validation checks are performed to ensure that data quality and integrity are maintained throughout the pipeline. Data that doesn’t meet validation criteria can be flagged for review.

7. Orchestration:

   • Data pipeline orchestration involves managing the sequence and dependencies of various pipeline tasks. Tools like Apache Airflow, KAFKA or cloud-based orchestration services help automate this process