Tag: NoSQL Databases

The realities of polyglot persistence in mainstream enterprises

Ritesh Ramesh describes how NoSQL and Hadoop get used in retail environments.

Solving a familiar e-commerce search problem with a NoSQL document store

Mark Unak and Sanjay Agarwal explain how document stores can help deliver precise e-commerce catalog search results.

Security at the level of key-value pairs in a NoSQL database

Adam Fuchs of Sqrrl describes the benefits of data-centric security analytics.

How NoSQL is changing enterprise data management

Oliver Halter discusses how CEOs and CIOs are being forced to tolerate some data inconsistency.

Scaling online ad innovations with the help of a NoSQL wide-column database

Vaibhav Puranik and Ken Weiner of GumGum discuss the challenges and benefits of open source databases for in-image advertising.

How enterprise graph databases are maturing

Martin Van Ryswyk and Marko Rodriguez of DataStax explore the challenges and benefits of big data analytics with graphs.

Filling in the gaps in NoSQL document stores and data lakes

Matthias Brantner describes the role database virtualization and a business-user query interface can play in heterogeneous environments.

Creating a body language of online learning with graph databases

Sean York of Pearson discusses how graph technology becomes a medium for enriching online environments.

Database futures: How Apache Spark fits in to a larger unified data architecture

Mike Franklin of the University of California, Berkeley, discusses the goals behind Spark and a more unified cloud-data ecosystem.

Creating a big data canvas with NoSQL

Tom Foth describes how analytics platforms can benefit from a blend of database types.

How NoSQL key-value and wide-column stores make in-image advertising possible

Online ad innovators must process hundreds of terabytes a day at the lowest possible cost. How do they do it?

The promise of graph databases in public health

One of the main advantages of a NoSQL graph store is web-scale discovery. The graph store is one of many innovations creating a sea change in database technology: explore the promise and upheaval caused by these new technologies.

In database evolution, two directions of development are better than one

NoSQL database technology is maturing, but the newest Apache analytics stacks have triggered another wave of database innovation.

The rise of immutable data stores

Some innovators are abandoning long-held database principles. Why?

Using document stores in business model transformation

Healthcare providers are finding they need data collection and analysis capabilities that are different from those that relational databases deliver.

Enterprises hedge their bets with NoSQL databases

Without a scalable data architecture, the customer experience suffers. Imagine you’re a retailer offering tens of thousands of products online. You have rich descriptions that include numerous attributes for each product. In standard relational databases, these attributes exist in silos, are poorly described, and cannot be indexed for maximum usefulness. So, if you’re using only a standard relational database and a conventional enterprise search engine, customers who search “17-inch laptop” will retrieve many false positive results that aren’t laptops. NoSQL1 document databases provide the capability to address this problem. With the help of data tagged in Extensible Markup Language (XML) or structured JavaScript Object Notation (JSON), NoSQL offerings such as MarkLogic and MongoDB enable more refined indexing by attribute. Dozens, hundreds, or even thousands of product attributes can serve as search filters (or facets) that a query engine such as XQuery can use to deliver far more relevant results. “Usually the product has features that can easily be attributed with standard values,” says Mark Unak, CTO of Codifyd, a consultancy that helps clients to optimize their use of e-commerce website data. For example, a customer can filter on the brand name of the laptop, and the results will include only …

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