Certified Data Management Professional (CDMP): key terms

Flashcards for this exam Certified Data Management Professional (CDMP) guide

• Accumulating Snapshot Fact TableA fact table with one row per process instance, such as an order, that is updated as the process passes milestones.Advanced
• Atomic ValueA value that cannot be meaningfully divided further for the purposes of the data model.Foundational
• AttributeA property that describes an entity, implemented as a column in a table.Foundational
• Boyce-Codd Normal Form (BCNF)A stricter version of 3NF in which every determinant (left side of a functional dependency) is a candidate key.Advanced
• Bridge TableA table that resolves a many-to-many relationship between a fact and a dimension, or handles multivalued dimensions.Advanced
• Bus MatrixA Kimball planning grid that lists business processes (rows) against conformed dimensions (columns) to design an integrated warehouse.Advanced
• Business GlossaryA governed list of business terms and their agreed definitions, owners and relationships, linked to the physical data assets that implement them.Intermediate
• Candidate KeyAny minimal set of columns that could uniquely identify every row; one is chosen as the primary key.Intermediate
• Cardinality (Data Modeling)The number of instances of one entity that can relate to instances of another: one-to-one, one-to-many or many-to-many.Foundational
• Conceptual Data ModelA high-level model that shows the main business entities and their relationships, without attributes or technical detail.Foundational
• Conformed DimensionA dimension shared with consistent keys, attributes and meaning across multiple fact tables or data marts.Intermediate
• Crow's Foot NotationAn ERD notation that uses symbols at line ends to show cardinality: a three-pronged 'crow's foot' for many, a bar for one and a circle for zero.Intermediate
• DAMA-DMBOKThe Data Management Body of Knowledge published by DAMA International, which defines data management knowledge areas with data governance at the center.Intermediate
• Data AccuracyThe data quality dimension measuring how well data values reflect the real-world entities or events they describe.Foundational
• Data AnomaliesInsert, update and delete anomalies: integrity problems caused by redundant data in unnormalized tables.Intermediate
• Data ArchivingMoving data that is no longer actively used to lower-cost, long-term storage while keeping it retrievable for compliance or reference.Foundational
• Data CatalogAn inventory of an organization's data assets with metadata, descriptions, ownership, classifications, lineage and search for discovery.Intermediate
• Data CleansingDetecting and correcting or removing inaccurate, incomplete, duplicate or improperly formatted data.Foundational
• Data CompletenessThe data quality dimension measuring whether all required data values and records are present.Foundational
• Data ConsistencyThe data quality dimension measuring whether the same data agrees across records, systems and time.Foundational
• Data ContractA formal agreement between data producers and consumers that specifies schema, semantics, quality expectations, SLAs and change management for a dataset.Advanced
• Data DemocratizationMaking data accessible to non-technical users across an organization, with appropriate governance.Foundational
• Data DictionaryA repository of metadata describing each table, column, data type, constraint and meaning in a database.Foundational
• Data FabricAn architecture that uses metadata, automation and integration services to provide unified, governed access to data across distributed sources.Advanced
• Data GovernanceThe framework of policies, roles, standards, processes and controls that ensures data is managed as an asset: secure, high quality, compliant and usable.Foundational
• Data Lifecycle Management (DLM)Managing data from creation and storage through use, sharing, archiving and secure deletion according to policy.Intermediate
• Data LiteracyThe ability to read, work with, analyze and communicate with data.Foundational
• Data MartA subset of a data warehouse focused on one business area or department, such as sales or finance.Foundational
• Data MeshA decentralized sociotechnical approach in which domain teams own and serve their data as products on a self-service platform, with federated governance.Advanced
• Data ObservabilityContinuous monitoring of data health across pipelines (freshness, volume, schema, distribution and lineage) to detect and resolve issues quickly.Intermediate
• Data ProductA curated, discoverable, trustworthy dataset or service owned by a domain team and published for others to consume.Intermediate
• Data ProfilingExamining data to summarize its structure, content, completeness, distributions, patterns and anomalies before using it.Foundational
• Data ProvenanceThe documented origin and history of data, including where it came from, who created or changed it, and how.Intermediate
• Data Quality (DQ)The degree to which data is fit for its intended use, judged on dimensions such as accuracy, completeness, consistency, timeliness, validity and uniqueness.Foundational
• Data Quality DimensionsMeasurable aspects of data quality: accuracy, completeness, consistency, timeliness, validity and uniqueness.Foundational
• Data SiloData isolated within one department or system and not accessible or integrated with the rest of the organization.Foundational
• Data StandardizationConverting data into a consistent format and set of codes, such as ISO dates, country codes, units and naming conventions.Foundational
• Data SwampA data lake that has become unusable because of missing metadata, governance and quality controls.Foundational
• Data TimelinessThe data quality dimension measuring whether data is available and current enough for its intended use.Foundational
• Data UniquenessThe data quality dimension measuring whether each real-world entity is recorded only once.Foundational
• Data ValidityThe data quality dimension measuring whether values conform to defined formats, types, ranges and business rules.Foundational
• Data VaultA modeling method for agile, auditable warehouses that separates business keys (hubs), relationships (links) and descriptive history (satellites).Advanced
• Data Vault HubA Data Vault table that stores the unique list of business keys for a core business concept, with a hash key, load date and record source.Advanced
• Data Vault LinkA Data Vault table that records relationships or transactions between two or more hubs.Advanced
• Data Vault SatelliteA Data Vault table that stores descriptive attributes and their full change history for a hub or link.Advanced
• Data VirtualizationQuerying data in place across multiple sources through a unified layer, without physically copying it.Intermediate
• Database AuditingTracking and recording database events such as logins, queries, permission changes and data modifications for security and compliance.Intermediate
• Database NormalizationThe process of organizing tables to reduce redundancy and prevent update anomalies by applying normal forms.Foundational
• Degenerate DimensionA dimension key, such as an order or invoice number, stored in the fact table with no separate dimension table.Advanced
• DenormalizationIntentionally adding redundancy, for example by merging tables or duplicating columns, to speed reads and simplify queries.Intermediate
• Dimensional ModelingA data modeling technique that organizes analytic data into facts (measurable events) and dimensions (descriptive context).Intermediate
• Domain IntegrityThe rule that every column value comes from the column's valid set of values, enforced by data types, CHECK, NOT NULL and DEFAULT.Intermediate
• Duplicate Record DetectionIdentifying records that represent the same entity, through exact key matches or fuzzy matching of attributes.Intermediate
• Enterprise Data Warehouse (EDW)An organization-wide data warehouse that integrates data across all business areas to serve as a single source for analytics.Intermediate
• EntityA person, place, thing, event or concept about which data is stored; it usually becomes a table.Foundational
• Entity IntegrityThe relational rule that every table has a primary key and that no primary key value is NULL or duplicated.Intermediate
• Entity ResolutionDetermining when records from one or many sources refer to the same real-world entity and linking or merging them.Advanced
• Entity-Relationship Diagram (ERD)A diagram that shows entities, their attributes and the relationships between them, used to design databases.Foundational
• Factless Fact TableA fact table with dimension keys but no numeric measures, recording that an event or coverage relationship occurred.Advanced
• First Normal Form (1NF)A table is in 1NF when every column holds atomic (single) values, there are no repeating groups, and each row is unique.Foundational
• Fourth Normal Form (4NF)A table is in 4NF when it is in BCNF and has no non-trivial multivalued dependencies.Advanced
• Functional DependencyA relationship where the value of one attribute (or set) uniquely determines the value of another: X -> Y.Intermediate
• GrainThe level of detail one row of a fact table represents, such as one line item per order or one balance per account per day.Intermediate
• Inmon MethodologyBill Inmon's top-down approach that builds a normalized (3NF) enterprise data warehouse first, then derives dimensional data marts.Intermediate
• Junction TableAn associative table that resolves a many-to-many relationship by holding foreign keys to both related tables.Intermediate
• Junk DimensionA dimension that combines miscellaneous low-cardinality flags and indicators into one table to avoid cluttering the fact table.Advanced
• Kimball MethodologyRalph Kimball's bottom-up approach that builds a data warehouse from dimensional star schemas linked by conformed dimensions.Intermediate
• Logical Data ModelA detailed, technology-independent model that defines entities, attributes, keys, relationships and normalization.Intermediate
• Many-to-Many Relationship (Database) (M:N)A relationship where many rows in one table relate to many rows in another; it is resolved with a junction table.Intermediate
• Master DataCore, relatively stable business entities shared across processes, such as customers, products, employees and locations.Foundational
• MetadataData about data: technical (schemas, types), business (definitions, owners) and operational (load times, lineage, usage) information.Foundational
• Microsoft PurviewMicrosoft's unified data governance, security and compliance platform, including the Unified Catalog, Data Map, data lineage, sensitivity labels and DLP.Intermediate
• Multivalued Dependency (MVD)A constraint where one attribute determines a set of values for another attribute, independent of the other attributes.Advanced
• Natural KeyA key made from real-world business data that is already unique, such as an ISBN, VIN or email address; also called a business key.Intermediate
• Operational Data Store (ODS)An integrated, near-real-time store of current operational data from several systems, used for operational reporting.Intermediate
• OptionalityWhether participation in a relationship is mandatory (minimum one) or optional (minimum zero).Intermediate
• Orphan RecordA child row whose foreign key points to a parent row that does not exist.Foundational
• Partial DependencyA dependency in which a non-key attribute depends on only part of a composite primary key.Intermediate
• Periodic Snapshot Fact TableA fact table that records the state of something at regular intervals, such as daily inventory or monthly account balances.Advanced
• Physical Data ModelThe implementation-specific model with actual tables, columns, data types, indexes, partitions and constraints for a chosen DBMS.Intermediate
• Reference DataStandardized code sets and classifications used to categorize other data, such as country codes, currency codes and status values.Foundational
• Referential IntegrityThe guarantee that every foreign key value matches an existing key in the referenced table, so no orphaned rows exist.Foundational
• Reverse ETLSyncing modeled data from the warehouse back into operational tools such as CRM, marketing and support systems.Intermediate
• SCD Type 2A slowly changing dimension method that inserts a new row for each change, with effective dates and a current-row flag, preserving full history.Intermediate
• SCD Type 3A slowly changing dimension method that adds a column to store the previous value alongside the current one, keeping limited history.Advanced
• Second Normal Form (2NF)A table is in 2NF when it is in 1NF and every non-key column depends on the whole primary key, not part of it.Intermediate
• Self-Service BIEnabling business users to create their own reports and analyses from governed data without relying on IT for each request.Foundational
• Single Source of Truth (SSOT)The practice of having one authoritative source for each data element or metric so that everyone uses consistent numbers.Foundational
• Slowly Changing Dimension (SCD)Techniques for handling changes to dimension attributes over time, such as a customer moving city.Intermediate
• SuperkeyAny set of one or more columns whose combined values uniquely identify each row, whether or not it is minimal.Advanced
• Surrogate KeyA system-generated identifier with no business meaning, such as an auto-increment integer or GUID, used as a primary key.Intermediate
• Third Normal Form (3NF)A table is in 3NF when it is in 2NF and no non-key column depends on another non-key column (no transitive dependencies).Intermediate
• Transaction Fact TableA fact table with one row per discrete event at the most atomic grain, such as each sale or each click.Intermediate
• Transitive DependencyAn indirect dependency in which a non-key attribute depends on another non-key attribute rather than directly on the key.Intermediate