Data Pipeline

Definition:
A data pipeline is a series of processes and tools that automate the movement and transformation of data from various sources to a destination
where it can be stored, analyzed, and used for decision-making.

It typically involves the following stages:

1. Data Ingestion:Collecting data from various sources such as databases, APIs, flat files, or streaming data.

2. Data Processing: Cleaning, transforming, and enriching the data to ensure it is in the right format and quality for analysis.
This can include tasks like filtering, aggregating, and joining data.

3. Data Storage: Storing the processed data in a data warehouse, data lake, or another type of storage system where it can be easily accessed for analysis.

4. Data Analysis & Visualization Using tools and techniques to analyze the stored data and create visualizations, reports, and dashboards for decision-making.

5. Data Monitoring and Maintenance Continuously monitoring the pipeline to ensure it runs smoothly and efficiently, and performing maintenance tasks to fix any issues and optimize performance.


Example:


Learn more about this Data Pipeline here



Data Literacy