Data Vault 2.0 Simplified: Hands-on Guide to Agile DWH

Explore how a data warehouse acts as a centralized, historical, integrated, subject oriented repository that supports decision making through BI dashboards, ad hoc queries, machine learning, and collaboration.

Explore data vault, a flexible, scalable data warehouse method for historical, auditable data. Learn its hub-and-spoke architecture with hubs, satellites, and links, plus hashing and surrogate keys for agile integration.

Discover the advantages of Data Vault 2.0, including flexibility, schema agility, non-volatile storage, subject-oriented data, scalability, parallel loading, cloud-native capabilities, data integration, and governance.

Explore the disadvantages of Data Vault 2.0, including increased complexity, a steeper learning curve, specialized implementation needs, a flexible schema with numerous tables, and potential overengineering.

Explore the Data Vault 2.0 modeling process, from discovery and data source inventory to hubs, links, satellites, and information marts, with testing, documentation, and iterative refinement.

Explore how hash keys enable surrogate key generation for hubs and links in data vault 2.0, replacing business keys with unique, platform-agnostic identifiers, supporting parallel loading and scalable distribution.

Explore hubs as core, immutable building blocks in a Data Vault 2.0 warehouse that centralize business entity keys, link related data, ensure integrity, with hash keys and load dates.

Identify hubs in a data vault 2.0 model by selecting core business concepts: customer, product, and order, and assign business keys and alternate keys, while applying naming conventions.

Explore how satellites in Data Vault 2.0 store descriptive and historical attributes for hubs and links, enabling temporal analysis, data governance, and flexible attribute management.

Identify satellites for customer, product, and order details in a data vault 2.0; use load date and hub hash keys, and separate age group and gender as slowly changing dimensions.

Explore identifying satellites in Data Vault 2.0 by turning book, author, and member data into satellites linked to hubs, with rent details, due dates, and return status.

Understand how links in data vault 2.0 connect hubs to relationships between business entities, from simple to many-to-many. Learn practices for naming, attribute storage in satellite tables, and documenting relationships.

Learn how many-to-many relationships between hub tables are resolved in Data Vault 2.0 with associative link tables, ensuring flexible modeling as business rules evolve.

Identify the unit of work and linked data in Data Vault modeling, ensuring consistency between arriving and stored data, and avoid normalization that creates multivalued dependencies.

Explore how the unit of work governs link creation between customer, product, and order hubs, showing how incorrect normalization can introduce extra records and break data integrity.

Identify links in Data Vault 2.0 using unit of work through link entities like order-customer and order-product, with hash key, load date, timestamp, and record source attributes.

Explore a library management case study to identify links between rent and member, and build hub and link tables for book rent and author relationships in a data vault context.

Explore a coffee shop case study using Data Vault 2.0 to model hubs, satellites, and links for coffee, customer, employee, and order, enabling profitability analysis by coffee variety.

Explore the typical data vault 2.0 architecture, from staging and raw data vault to business vault and information marts, with hubs, links, satellites, and business rules.

Explore the staging layer in Data Vault 2.0, covering data extraction, raw data intake, basic quality checks, validation, partitioning, audit logging, and deferring transformations to the raw vault.

Explore the raw data vault, the heart of data vault 2.0, detailing hubs, links, satellites, non-volatile historical data, and hash-partitioned loading for scalable, auditable provenance.

Learn how the data vault 2.0 business layer bridges raw data and business needs, using point-in-time and bridge tables, with data integration, aggregation, historical tracking, and quality checks.

Explore the information mart layer, the final Data Vault 2.0 layer, designed for business-friendly reporting and analysis with dimensional models, aggregated data mart concepts, and governance and security.

Learn why link on link is discouraged in Data Vault 2.0 and how hubs and links uphold core modeling principles. Consider denormalization, additional hubs, or satellite extensions as alternatives.

Explore how to avoid link-on-link in Data Vault 2.0 through a hub-centric model, using student, course, and exam hubs, and enrollment and completion link tables.

Explore same as links, connecting hubs or links for the same entity across systems with different identifiers. Learn how they improve data quality and enable analytics in Data Vault 2.0.

Explore how same as link enables integrating customer data from two sources in Data Vault 2.0 by using hub and satellite tables and a master data mapping table.

Explore hierarchical links that model parent-child relationships within the same hub, linking parent and child entities in a hierarchical structure such as employees to managers or countries to states.

Explore a concrete example of hierarchical links in data vault 2.0 using an employee manager hierarchy and a hub and satellite model with an employee manager hire link.

Explore non historized (transactional) links that connect hubs without satellites, capturing immutable data for high-volume transactions like sales, payments, and calls. Learn their structure, benefits, drawbacks, and use cases.

Examine a non historized link between sensor and location hubs in data vault 2.0, using a timestamped composite key for real-time monitoring and historical trends.

Learn how non-descriptive links use a two-column link table to capture simple entity relationships, boosting simplicity, efficiency, and normalization in Data Vault 2.0.

See how non-descriptive links model many-to-many employee–project relationships in Data Vault 2.0 using separate employee and project hubs and a dedicated employee project relationship link.

Explore computed aggregate links in data vault 2.0, connecting hubs with pre-calculated aggregates to improve query performance, support specific business questions, and reduce data volume in satellites while balancing maintenance.

Explore how to model a computed aggregated link in Data Vault 2.0, connecting class hub and subject hub with a class subject average marks satellite to enable analysis.

Explore Data Vault 2.0 exploration links for ad hoc querying, exploratory analysis, and hypothesis testing without altering the core model; enable flexible, temporary data access and query optimization.

Explore how a data vault 2.0 exploration link connects customer segments to products through transactions, using hub, link, and satellite structures to inform marketing strategies.

Explore overloaded satellites in Data Vault 2.0, why separate satellites per source is preferred, and the risks of a single satellite, including primary key, delta loading, and data quality issues.

Explore overloaded satellites when employee data from the employment and project sources populate an employee hub and separate name satellites, prioritizing employment data to resolve duplicates.

Explore multi-active satellites in data vault 2.0, enabling multiple active records per business key, with overlapping attribute values, concurrent updates, and temporal flexibility for historical and real-time analysis.

Explore data vault 2.0 multi-active satellites with an employee project example, showing how hubs and a link enable concurrent roles tracked over time in the employee project role satellite.

Explore status tracking satellites for audit trails from change data capture, used when sources send changes. Capture delete, insert, and update events for hubs and links, with load date.

Illustrate how status tracking satellites in data vault 2.0 use a customer hub and satellites for customer names and record status to track inserts, updates, and deletes.

Data Vault 2.0 effectivity satellites that track the validity of hub relationships over time using start and end dates in a link table, enabling historical analysis of dynamic relationships.

Explore an effectivity satellite example in data vault 2.0, tracking subscription validity with start and end dates across customer and product hubs and links for accurate historical reporting.

Explore record tracking satellites in data vault 2.0 to detect deleted or missing keys from full dump format, with flags and load dates that reveal data origin and freshness.

Learn how a record tracking satellite, linking a product hub to source satellites from manufacturer, quality, and review systems, uses a record present flag to determine active status.

Explore computed satellites in Data Vault 2.0 to pre-calculate derived data, boost query performance, and enrich analytics with KPI-like metrics while maintaining lineage and flexible granularity.

Illustrates how to build a data vault 2.0 computed satellite to pre-calculate customer purchase metrics from order data, improving query performance and enabling rapid insights into high-value customers.

Explore point-in-time tables in data vault 2.0 to snapshot hub or link data at specific dates, handling overlapping active states and improving historical query performance and auditing.

Explore point-in-time tables in Data Vault 2.0 by modeling hubs and satellite tables, showcasing history handling as customer data changes across days from multiple sources.

Learn to build a point in time table with a customer hash key and snapshot date, loading max satellite dates to enable as of queries.

Explore bridge tables in Data Vault 2.0 to consolidate hub and link keys and boost query performance. Simplify joins and preserve business context across hubs and links.

Explore a data vault 2.0 bridge table example in Erwin, linking hubs and satellites to simplify daily sales analytics and reduce complex sql queries.

Explore no history reference tables in data vault 2.0, which store immutable or slowly changing reference data in a central location to simplify the model and boost query performance.

Explore a no history reference table example, the country reference, storing country id, country name, and country code in a data vault 2.0 model for rarely changing country data.

Explore history reference tables in Data Vault 2.0, designed for moderate change frequency, to enable historical analysis and audit trails, with guidance on structure, loading, versioning, and purging.

Explore a history reference table in a Data Vault 2.0 model to track category descriptions over time, capturing id, description, start date, and end date as descriptions evolve.

Apply data vault 2.0 to model a global restaurant chain, defining core concepts like branch, raw material, dish, and customer visit, with hubs, links, and transaction line item.

Create a Data Vault 2.0 model with hubs for branch, payment method, raw material, dish, customer, loyalty tier, vendor, employee; add links, satellites, and effectivity satellites to organize history.