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

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

Data quality platforms work by automating the processes involved in identifying and correcting data errors. This automation reduces manual effort and minimizes the risk of human error. 

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

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

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

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

Data engineering tools are software applications and platforms that assist in building, managing, and optimizing data pipelines.

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

dbt (data build tool) seeds are static CSV files stored within your dbt project that are loaded into your analytics warehouse as database tables.

Ingestion capabilities are important to collect structured, semi-structured, and unstructured data. By ensuring data arrives in a consistent, well-organized manner, organizations can eliminate bottlenecks associated with data processing.

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

A data engineering workflow involves a series of structured steps for data management, from data acquisition to applications for organizational data users.

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

A dbt Python model is a type of transformation within the dbt (data build tool) ecosystem that lets developers write business logic using Python, instead of SQL.

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

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

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

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