Data pipeline

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 A data pipeline is a system that moves data from a source to a destination. During this process, the data is transformed and optimized to become analyzable. Data pipelines automate many manual tasks, making continuous data loading more efficient. The steps typically involve extracting data from various sources, transforming it to a suitable format, and loading it into a destination for further analysis.

There are three types of Pipelines:

• Extract, transform, load (ETL)
• Extract, load, transform (ELT) 
• Reverse ETL processes

• Data Collection: It's crucial to collect raw data from various sources, such as databases, APIs, and sensors. This ensures a continuous flow of information into the AI/ML platform, a key factor in its functionality and effectiveness. 

• Data Ingestion: Ingest the collected data into the system, enabling it to be processed and analyzed by the AI/ML platform [6].

• Data Transformation: Clean, normalize, and format the data to make it suitable for analysis and modeling. This step ensures data quality and consistency. 

• Data Storage: Store the transformed data in data lakes, warehouses, or databases to facilitate easy access and retrieval by AI/ML algorithms 

• Data Validation: Verifying the integrity and accuracy of the data is a critical step. It ensures the data meets the required standards and is free of errors or anomalies, maintaining its integrity and reliability for AI/ML model development.

• Data Summarization: Aggregate and summarize the data to create meaningful insights and reduce the volume of data for faster processing 

• Pattern Detection: Identify patterns and trends in the data, which can inform the development of AI/ML models and improve their performance 

• Data Delivery: Deliver the processed data to the AI/ML platform for model training, validation, and deployment