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Data warehouse is one of such potential areas, so this paper is devoted to creating of NoSQL- based DW using document-based NoSQL data stores. The first chapter describes classical DWs and identifies benefits to be expected if using NoSQL technologies instead of RDBMS.
Easily load your MongoDB data into your data warehouse It is a document-oriented NoSQL database, and that makes it really easy to store and work with no relational data. MongoDB is commonly used as the database to store product data and product interactions with your customers.
Key takeaway: Oracle Database is best for enterprise companies looking to leverage machine learning to improve their business insights. Oracle Database offers data warehousing and analytics to help companies better analyze their data and reach deeper insights.
A database is any collection of data organized for storage, accessibility, and retrieval. A data warehouse is a type of database the integrates copies of transaction data from disparate source systems and provisions them for analytical use.
Data Warehouses have very little in common with NoSQL - the main similarity is that any two data warehouses can have very different philosopohies or conventions just like any two NoSQL systems can be nearly unrelated.
The only concept they share is that they are both used to analyze large amounts of data.
NoSQL solutions usually manage relatively limited schemas with large cardinality in few entities, while data warehouses typically have lots of facts and dimensions (in a dimensional model) or lots of entities in a 3NF model. DW systems usually manage multiple lines of business and attempt to combine that data.
DW systems typically have reporting abilities in SQL which allows you to access all the data in a standard way. NoSQL systems are typically more code-based - for instance Map/Reduce.
Ayende Rahien explain it well in his blog:
http://ayende.com/blog/4552/nosql-and-data-warehousing
"For data warehousing, I think that the relational / OLAP world has significant advantages, mostly because in many BI scenarios, you want to allow the users to explore the data, which is easy with the SQL toolset, and harder with NoSQL solutions. But when you get too large (and large in OLAP scenarios is really large), you might want to consider limiting the users’ options and going with a NoSQL solution tailor to what they need."
My favorite quote from the deck: "document-databases are far superior to relational databases for business intelligence cases. Not only that, but mongoDB and some common sense lets you replace multimillion dollar IBM-level enterprise solutions with open-source awesomeness. All this in a rapid, agile way." http://www.10gen.com/presentations/mongodc-2011/time-series-data-storage-mongodb
Also: "Map/Reduce may yet be your killer app that can be the panacea for all your Business Intelligence ailments. This is very serious stuff. If Google has bet its house on it and has made this the foundation for their search technology, then you better believe that this is very strong medicine." http://www.infogain.com/company/perspective-big-data.jsp
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