An open-source platform for orchestrating complex data workflows, allowing users to programmatically author, schedule, and monitor workflows.

A series of data processing steps that involve data collection, transformation, and storage, facilitating the flow of data from source to destination.

A subset of artificial intelligence that enables systems to learn from data and improve performance over time without explicit programming.

The use of statistical techniques and machine learning to analyze historical data and predict future outcomes.

A representation of a workflow in Apache Airflow, where nodes represent tasks and edges represent dependencies between them.

A data processing framework that involves extracting data from sources, transforming it into a suitable format, and loading it into a destination.

The process of teaching a machine learning model to make predictions based on input data by adjusting its parameters.

Quantitative measures used to assess the performance of a machine learning model, such as accuracy, precision, and recall.

Methods used to make machine learning models available for use in production environments.

The capability of a data pipeline to handle increased load or volume of data without performance degradation.

The process of improving the efficiency and effectiveness of data workflows, reducing time and resource consumption.

A method used in data pipelines to create multiple paths for data processing based on specific conditions.

The process of connecting various data sources to a data pipeline to ensure seamless data flow.

The automatic process of updating machine learning models with new data to maintain their accuracy over time.

A point in a data pipeline where the flow of data is restricted, causing delays and inefficiencies.

Designing systems and processes to remain effective and relevant in the face of evolving technology and data requirements.

Essential software and frameworks that facilitate the implementation and management of data pipelines and machine learning.

Practical use cases that demonstrate how theoretical concepts in data engineering and machine learning are applied in industry.

A collaborative evaluation process where colleagues assess each other's work to provide constructive feedback.

The transfer of data between different stages or components in a data pipeline.

Common obstacles faced when combining different technologies and workflows within a data pipeline.

A detailed document that summarizes findings, challenges, and best practices related to a specific topic or project.

An overview design of a system that outlines its main components and their interactions.

The setup of individual tasks within a workflow, defining how they operate and interact with other tasks.

Written records that outline the processes, decisions, and configurations related to a project, essential for future reference.