The process of combining data from different sources into a unified view, ensuring consistency and accuracy.

An open-source platform to programmatically author, schedule, and monitor workflows, facilitating automation in data pipelines.

A powerful data visualization tool that enables users to create interactive and shareable dashboards for insightful data analysis.

The graphical representation of information and data, making complex data more accessible and understandable to users.

The measure of data's accuracy, completeness, reliability, and relevance, crucial for effective data analysis.

The use of technology to automate complex business processes and functions, enhancing efficiency and reducing manual effort.

A directed graph with no cycles, used in Apache Airflow to represent workflows and task dependencies.

The process of detecting and correcting corrupt or inaccurate records from a dataset to improve data quality.

Procedures implemented to ensure that data meets specified quality standards before it is processed or analyzed.

Methods applied to improve the efficiency and performance of data pipelines, ensuring faster processing and resource management.

The process of creating data element mappings between two distinct data models, essential for data integration.

The method of accessing data from external sources through Application Programming Interfaces (APIs), facilitating data integration.

Standardized structures for organizing and storing data, such as CSV, JSON, and XML, crucial for compatibility.

Points in a process that slow down the overall workflow, often identified during performance analysis.

Guidelines for creating effective dashboards that communicate data insights clearly and intuitively.

Strategies for managing errors in workflows, ensuring that processes can recover and continue functioning.

The ability of a visualization to allow users to engage with data, such as filtering or drilling down for more details.

Quantifiable measures used to assess the quality of data, including accuracy, completeness, and consistency.

The ability to effectively communicate ideas and findings to an audience, crucial for showcasing data insights.

Personal logs where students document their learning experiences and insights throughout the course.

A process where students evaluate each other's work, providing constructive feedback to enhance learning.

Regular assessments during the course to evaluate student understanding and application of key concepts.

The capability of a data pipeline to handle increased loads without compromising performance.

The process of gathering and organizing all relevant materials and findings from a project for presentation.

Established methods and techniques recognized as the most effective in the industry, guiding data pipeline development.