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This involves a series of semi-automated or automated operations implemented through data engineering pipeline frameworks. Strengths: It offers parallel processing, flexibility, and built-in capabilities for various data tasks, including graph processing. Strengths: Fault-tolerant, scalable, and reliable for real-time data processing.
A typical data pipeline involves the following steps or processes through which the data passes before being consumed by a downstream process, such as an ML model training process. DataIngestion : Involves raw data collection from origin and storage using architectures such as batch, streaming or event-driven.
Aggregation : Combining multiple data points into a single summary (e.g., Normalisation : Scaling data to fall within a specific range, often to standardise features in Machine Learning. Encoding : Converting categoricaldata into numerical values for better processing by algorithms. calculating averages).
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