Glossary

Apache Spark is an open-source, distributed computing framework that enables fast and efficient processing of large datasets

Data build tool (dbt) is a development framework for analysts that enables them to build, test, and document data pipelines using only SQL

A data catalog is a centralized repository that stores and organizes metadata about an organization's data assets

Data discovery is the systematic process of finding and gathering relevant data assets from multiple sources

Unlock deeper insights with data governance. Discover how to manage data for quality, compliance, and better decisions

Data integrity refers to maintaining and assuring data accuracy, completeness, and consistency across different systems and applications

Learn about data lake, its components, benefits & challenges. Explore different types & how they empower data-driven decisions

Data lineage is a foundational aspect of data management, offering organizations a holistic view of their data's journey

Data management empowers engineers to ensure quality, optimize pipelines, & collaborate for data-driven success

Learn how data modeling structures data for efficient storage, retrieval, and analysis while ensuring data integrity, performance, and scalability

Data observability is the process of understanding the health, performance, and potential issues within a data ecosystem.

Data quality refers to the accuracy, completeness, consistency, timeliness, and relevance of data in an organization

Data validation is the process of ensuring that data is accurate, clean, and valuable. It involves verifying the integrity and correctness of data to ensure it meets specific standards, rules, and requirements

A data warehouse is a centralized repository that integrates multiple sources and allows organizations to analyze their data

Learn what is ETL (Extract, Transform, Load) & the role it plays in data engineering. Explore how ETL streamlines data integration, improves data quality, and more

Discover the essentials of Master Data Management (MDM) and how it ensures accurate, consistent, and accessible data across your organization

Metadata, often called "data about data," encompasses the information that describes the characteristics, content, and context of data assets

Pull Requests: The core building block of collaborative coding. Click here to learn how they streamline code reviews, integrations, and more

Schema evolution refers to the modifications made to the structure or definition of a data schema.

Unstructured data refers to information that does not adhere to pre-defined data models or organizations