How is Data pipeline built

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Data pipeline is built by extracting, transforming, and loading data from various sources to a destination for analysis and reporting.

Data extraction: Collect data from different sources like databases, APIs, logs, etc.
Data transformation: Clean, filter, and transform the data to make it usable for analysis.
Data loading: Load the transformed data into a destination such as a data warehouse or database for further processing.
Automation: Use tools like Apache Airflow, Apache NiFi, or custom scripts to automate the pipeline.
Monitoring: Implement monitoring and alerting to ensure the pipeline runs smoothly and efficiently.
Scalability: Design the pipeline to handle large volumes of data and be scalable as the data grows.
Example: Extracting customer data from a CRM system, transforming it into a standardized format, and loading it into a data warehouse for analytics.

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