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

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

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.

A data pipeline framework is a structured system that enables the movement and transformation of data within an organization.

A data pipeline is a series of processes that move data from one system to another.

Data pipelines architecture automates the collection, processing, and transfer of data from various sources to destinations for analysis or storage

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

A data catalog is a centralized repository that provides an organized inventory of data assets within an organization.

Data engineering focuses on the practical application of data collection and processing techniques.

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

A data platform is a system to manage, process, store, and analyze data from various sources.

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

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

Data observability refers to the ability to fully understand the health and state of data in an organization.

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

ELT is a data integration process that extracts raw data, loads it into a data warehouse, and transforms it within the warehouse for large data sets.

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

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

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

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