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Unlike a data warehouse that stores enterprise-wide data, a data mart includes information related to a particular department or subject area. For instance, a sales data mart may contain data related to products, clients, and sales only. Read this blog to develop a better understanding of these departmental data repositories.

Data Vault 2.0 modeling methodology has gained immense popularity since its launch in 2013. It’s a hybrid model that combines the benefits of Third Normal Form (3NF) and star schema architectures, making it a dream solution for data warehousing engineers. But is it worth implementing for your data warehouse architecture? The answer isn’t straightforward, as there are many factors to consider. So, let’s dive in and explore whether Data Vault 2.0 is right for you.

Data munging is the process of preparing raw data for reporting and analysis. It incorporates all the stages prior to analysis, including data structuring, cleaning, enrichment, and validation. The process also involves data transformation, such as normalizing datasets to create one-to-many mappings. It is also known as data wrangling.