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Data visibility refers to how accessible, understandable, and useful data is within an organization.

A data pipeline automates the transfer of data between systems and its subsequent processing.

Data engineering is the practice of designing, building, and maintaining the infrastructure necessary for collecting, storing, and processing large-scale data.

Data orchestration refers to the automated coordination and management of data movement and data processing across different systems and environments

Data orchestration tools manage data workflows, automating the movement and transformation of data across different systems.

Data quality testing involves evaluating data to ensure it meets specific standards for accuracy, completeness, consistency, and more.

Data reliability refers to the consistency and dependability of data over time.

Data transformation converts data between formats, reorganizes it, combines sources, or modifies values to meet analytical needs.

ETL stands for extract, transform, load, and represents a process used to consolidate data from various sources into a unified data warehouse.

An ETL (extract, transform, load) pipeline is a data processing system that automates the extraction of data from various sources.

ETL (Extract, Transform, Load) tools are software solutions that help organizations manage and process data from multiple sources.

A machine learning pipeline is a systematic process that automates the workflow for building machine learning models.

Data quality refers to the condition and usefulness of a set of values of qualitative or quantitative variables