Marktechpost

JUNE 13, 2024

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.

ETL 126

ETL Machine Learning Data Ingestion Big Data 126

The MLOps Blog

MARCH 15, 2023

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. Data Ingestion : Involves raw data collection from origin and storage using architectures such as batch, streaming or event-driven.

ETL 59

ETL Categorization Data Integration Automation 59

Pickl AI

OCTOBER 10, 2024

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 categorical data into numerical values for better processing by algorithms. calculating averages).

ETL 52

ETL Data Quality Machine Learning Business Intelligence 52