Talend^® Data Catalog

Metadata is "data about data" — information that describes a data asset, such as its name, source, format, meaning, owner, and when it was created or last updated. It provides the context that makes raw data understandable and usable, without being the data itself. Well-managed metadata is what lets people and systems find, interpret, and trust data across an organization.

Metadata is commonly grouped into a few types. Technical metadata describes structure and format — tables, columns, data types, and schemas; business metadata captures meaning and context — definitions, business terms, and ownership; and operational metadata records processing details, such as when data was loaded, how it was transformed, and whether jobs succeeded. Together these types give a complete picture of what data is and how it's used.

Data discovery is the process of finding, exploring, and understanding the data available across an organization. It combines search, profiling, and often machine learning to surface relevant datasets and reveal their content, quality, and relationships. Effective data discovery lets analysts and business users locate the right data quickly, instead of relying on tribal knowledge or manual hunting.

Data classification is the process of organizing data into categories based on its type, content, or sensitivity — for example, tagging fields as personal, financial, or confidential. It's essential for security and compliance, because you can only protect and govern data appropriately once you know what it is. Modern tools automate classification, using patterns and machine learning to detect sensitive data such as personally identifiable information (PII).

Impact analysis is the practice of tracing forward through data flows to understand what will be affected if a particular dataset, column, or process changes. It answers the question "if I change this, what breaks downstream?" — helping teams avoid unintended consequences before they make a change. It's the complement of data lineage, which traces backward to where data came from.

Data curation is the ongoing work of organizing, annotating, and maintaining data so it stays accurate, well-described, and easy to use over time. It includes adding descriptions and business terms, tagging and classifying assets, certifying trusted datasets, and keeping documentation current. Increasingly collaborative, curation lets both business and technical users contribute their knowledge to make data more valuable for everyone.

A data catalog is a central, searchable place where an organization's data assets are indexed and described, so people can quickly find, understand, and trust the data they need. It automatically crawls many sources to collect metadata, then layers on business context, ownership, quality signals, and lineage. By bringing technical and business views together in one collaborative space, it powers self-service analytics and underpins data governance.

Data lineage is a record of data's journey through an organization — tracing where it originated, how it moved, and how it was transformed on the way to its destination. It can be followed backward to find a data point's source, or forward to see everything a change would affect. Lineage is essential for troubleshooting errors, verifying accuracy, and meeting audit and compliance requirements.

A business glossary is an agreed-upon dictionary of an organization's business terms and their definitions, ensuring everyone means the same thing by terms like "active customer" or "net revenue." It connects those shared definitions to the actual data assets that represent them, bridging business language and technical data. This shared vocabulary reduces confusion, aligns reporting, and strengthens governance.

Metadata management is the practice of collecting, organizing, and maintaining metadata so data stays discoverable, understandable, and governable across an organization. It brings together technical, business, and operational metadata — often in a catalog — and keeps it accurate as data changes. Strong metadata management is the foundation for data discovery, lineage, governance, and trustworthy AI.

Data governance is the framework of policies, roles, and processes that defines how data is managed, secured, and used across its lifecycle. It sets who is accountable for data, how it's classified and accessed, and the standards it must meet — turning data into a reliable, compliant asset. A catalog supports governance by making data assets, their owners, and their rules visible in one place.

Data profiling is the process of examining data to understand its structure, content, and quality — measuring things like value distributions, formats, patterns, completeness, and uniqueness. It reveals issues and characteristics that would otherwise stay hidden, which is why it's usually done before trusting or using a dataset. In a catalog, automated profiling helps describe and score data as it's discovered.