Data Warehousing

Rajyug has experienced Data engineers, providing complete range of Data warehousing solutions for On premise Data warehouse, Azure based Data warehouse and AWS based Data warehouse .

Our experts are well aware of Kimball approach ( Dimensional Modelling) ,Kimball approach ( CIF) and Data Vaulting Modelling.. Here is a brief details of all these modelling techniques.
Kimball approach is typically least complex, easiest and fastest to implement, provides best combination of loading and querying performance. It is ideally structured to support iterative agile development (subject area sprints) and would be optimal approach for most organisations.

Inmon’s key advantage is in completeness of enterprise data model – although it is very difficult to achieve in real life and requires substantial investment and long-term commitment.

Data Vault is innovative concept and it has merits when compliance demands are very high and auditing and traceability requirements frequently change.

Enterprise Data Warehouse CIF

Corporate Information Factory (CIF) data warehouse architecture was pioneered by Bill Inmon. It is also referred to as top-down approach because it represent complete view of enterprise data. Data is stored in 3rd normal form (3NF) closely resembling source system structures. To capture historic data changes timestamps are added to each table key columns. Model does not support direct reporting and requires a layer of dimensional data marts.

Dimensional Modelling

Kimball Dimensional Data Mart approach was developed by Ralph Kimball. It is sometimes called bottom-up approach because it recommends building integrated reporting data marts sequentially, based on business priority. Data is stored in star or snowflake structure: large fact tables representing numerical measures and counts are in 3NF, smaller dimensions are demoralized to 2NF. Model is optimized for direct reporting – it does not require any additional layers on top of it.

Data Vault Modelling

Data Vault is a data warehousing architecture developed by Dan Linstedt in 1990s. It is based on the concept of Hubs, Links and Satellites. Hubs represent source system business keys in master tables (e.g. Customer, Product, etc.). Links represent associations/transactions between Hubs with validity period. Satellites point to links and contain detailed attributes of transaction and their validity period. Data is loaded from source systems “as is”, with no quality checks, validation or cleansing. The structure is highly normalized (4NF+) and is not designed for direct reporting.Data Vault model relies on dimensional data marts (Kimball approach) to expose data to users.

Our Expertise on Azure Services

On Premise DataWarehouse

Data warehousing is the process of constructing and using a data warehouse. A data warehouse is constructed by integrating data from multiple heterogeneous sources that support analytical reporting, structured and/or ad hoc queries, and decision making.

AWS based Data Warehouse

We have delivered highly scaled Datawarehouse on AWS platform using Amazon Redshift. It is a columnar database which is a fully managed, scalable, fast, and cost-effective data warehouse solution.

Azure Based DataWarehouse

Our highly skilled professionals have expertise in building large scale data warehousing solutions on Azure Synapse. Azure Synapse is a limitless analytics service that brings together enterprise data warehousing and Big Data analytics.