2021Unpublished venueRequires access

The Challenges of NoSQL Data Warehousing

Lucija Petricioli, Luka Humski, Boris Vrdoljak

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Abstract

Data warehouses are an important part of decision support systems in business. The volume of data currently being created can at times push the capabilities of relational data warehouses to their limits. A possible step forward is to use NoSQL solutions to model data warehouses, since they were made for the ever-increasing amount of data that various platforms deal with. However, simply deciding a data warehouse should be based on a NoSQL approach does not mean the problem has been solved. The flexibility of NoSQL leads to a host of new problems, such as how to perform various OLAP operations on a data warehouse that does not have a fixed schema or how and when to compute aggregate values. This paper provides an overview of various solutions that have been theorized and presented along with their advantages over relational data warehouses, as well as their drawbacks.

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Data warehouses are an important part of decision support systems in business. The volume of data currently being created can at times push the capabilities of relational data warehouses to their limits. A possible step forward is to use NoSQL solutions to model data warehouses, since they were made for the ever-increasing amount of data that various platforms deal with. However, simply deciding a data warehouse should be based on a NoSQL approach does not mean the problem has been solved. The flexibility of NoSQL leads to a host of new problems, such as how to perform various OLAP operations on a data warehouse that does not have a fixed schema or how and when to compute aggregate values. This paper provides an overview of various solutions that have been theorized and presented along with their advantages over relational data warehouses, as well as their drawbacks.

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Available abstract

Data warehouses are an important part of decision support systems in business. The volume of data currently being created can at times push the capabilities of relational data warehouses to their limits. A possible step forward is to use NoSQL solutions to model data warehouses, since they were made for the ever-increasing amount of data that various platforms deal with. However, simply deciding a data warehouse should be based on a NoSQL approach does not mean the problem has been solved. The flexibility of NoSQL leads to a host of new problems, such as how to perform various OLAP operations on a data warehouse that does not have a fixed schema or how and when to compute aggregate values. This paper provides an overview of various solutions that have been theorized and presented along with their advantages over relational data warehouses, as well as their drawbacks.

Key concepts: NoSQL, Data warehouse, Online analytical processing, Computer science, Database, Data cube, Dimensional modeling, Flexibility (engineering)

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