What are some common dimensional modeling patterns for big data scenarios?

Data Vault 2.0 excels in big data scenarios due to its adaptability and scalability. It offers a flexibility and agility ideal for handling large volumes of diverse data, ensuring efficient historical tracking and integration. Its normalized structure suits complex, evolving environments, promoting robust data governance and effective data warehouse solutions. Additionally, in the Information Mart area, a Star Schema can be layered over the Raw/Business Vault. This allows for simplified querying and analysis, blending the best of both models for comprehensive data handling.

In my experience, star schemas are one of the best ways to represent your data to a business audience as it's easy to understand and data visualization tools such as Power BI are primed for such data models. That said, star schemas should be used only once your data has been analyzed and all the useful data has been identified in your Big Data ecosystem. Typically Big Data scenarios implies that all data is not yet well understood, so you will need to first analyze and identify the key data elements useful for reporting prior to structuring your data into a star schema.

Star schema is basically a way to organize the data within dimensional data warehouse.You will see on most of the data warehouse projects. Fact table at the center of the star connected to dimension tables. This Enable better query performance and helps users and developer to understand the model much easier.It's mix of normalized and denormalized tables. Demensions are usually stored as denormalised form, while fact tables are usually normalized.

The simplicity and easy-to-understand structure of the Star Schema make it excellent for end-user reporting tools. However, it's not flexible when dealing with changing data sources or when detailed historical tracking is required. Simply use it if you are new to data modeling, and when the primary requirement is fast and efficient querying. It's ideal for scenarios where end-user reporting and ease of use are priorities.

Remember that snowflake schema = fact table + normalized dimensional tables from star schema base; as it still acts as a star scheme all time; so you can apply same patterns for big data context, some patterns are: ?? Have different semantical versions of dimensional table based in requirements for business; for example, datetime dimension changes its values but not primary key based in geographic location of user data. ?? Have different security version of dimensional table based in requirements for users: for reporting case, you have a raw dimensional table; but analysis and exploration case, you have a encrypted sensitive data dimensional table to use for business team.

?? Galaxy schema is chosen based in this premise: "one business area required multiple related facts to analyze from several points view related semantic or functional equivalent processes to support a complex analysis process". ?? Take the example of sales, inventory and returns; it can support several process like "what is the amount for X product during x season?" or "How sales was during last week?"; but the focus is revenue process from financial area. Some patterns are: ?? Define limit about scope of data modeling: cover an unique complex analysis per model. ?? Be consistent in fact tables when it's shared between data modeling with differents business process focus. Example: inventory fact is used in revenue and logistic analysis.

Data vault is not so dimensional, it is actually more normalized approach; the main reasons i based to say is 2 points: ?? Dimensional modeling approch just use fact and dimensional concept to create entities and concepts of real world; and data vault schema don't use these concepts: fact concept don't cover hub concept and link concept doesn't exist in dimensional approach! ?? Data vault is based in intersection table by relationship; this approch use 5NF. ?The "dimensional" part in this model is about the syntactic structural equivalence each other: hub concept use satellite concept like fact concept use dimensional concept to get contextual details over time. Data vault generate a star schema but it's a exceptional case, no regular.

Data vault is Not dimensional modeling pattern. Data vault may be ideal when you barely have any information about the businesses procesess and the data and may change very frequently. It is most expensive out of all modeling patterns as far as compute is concerned!

From my experience, combining the Star Schema and Data Vault 2.0 offers a robust approach for data warehousing in big data scenarios. Use Data Vault 2.0 for the foundational layer to handle complex, changing data sources and ensure data integrity with an excellent audit trail. This design helps track moving data over time, such as inventory transactions. For the reporting layer, implement a Star Schema to provide fast, efficient querying and ease of use for end-user reporting tools. This hybrid approach leverages the strengths of both methodologies: the flexibility and historical tracking of Data Vault and the simplicity and performance of the Star Schema.

There is a challenge here: the data lake haven't constraints on fields like traditional databases; so your dimensional modelling patterns that use data lake as storage; it must implement programmatically these constraints. For data quality issues within the dimensional model; the storage can't help it like defense layer: take it in mind always.