data-pipeline

To build an ETL data pipeline, you extract data from sources like CSV files, transform it by removing incomplete records to ensure quality, and load the valid entries into a destination format such as JSON or a PostgreSQL database. This process uses Python libraries for formatting and database interaction.

Data quality during batch processing is ensured by implementing data validation checks at each stage of the pipeline. This approach validates data formats like JSON and Parquet, filtering out incomplete or inconsistent records before loading them into the final destination.

Yes, you can schedule data pipeline execution using cron or Python scheduling libraries. This allows you to automate daily batch processing tasks, such as processing daily sales reports, without manual intervention.

Idempotent data pipelines prevent duplicate side effects by ensuring that rerunning a job produces the same result without duplicating data. This means if a pipeline fails midway, you can safely rerun the entire process without corrupting the final dataset.

Yes, this data pipeline approach supports streaming data alongside batch processing. It also handles various file formats including Parquet, CSV, and JSON, allowing flexible data extraction, transformation, and loading across different processing modes.

You need Python libraries capable of handling CSV, JSON, and Parquet file formats for data transformation, alongside database interaction libraries to load the processed data into a PostgreSQL database, ensuring clean and consistent records.