The future of big data is very, very fast

There are only two certainties in big data today: It won't look like yesterday's data infrastructure, and it'll be very, very fast.

This latter trend is evident in the rise of Apache Spark and real-time analytics engines, but it's also clear from the parallel rise of real-time transactional databases (NoSQL). The former is all about lightning-fast data processing, while the latter takes care of equally fast data storage and updates.

The two together combine to "tackle workloads hitherto impossible," as Aerospike vice president Peter Goldmacher told me in an interview.

Hadoop turns 10, Big Data industry rolls along

It's hard to believe, but it's true. The Apache Hadoop project, the open source implementation of Google's File System (GFS) and MapReduce execution engine, turned 10 this week.

The technology, originally part of Apache Nutch, an even older open source project for Web crawling, was separated out into its own project in 2006, when a team at Yahoo was dispatched to accelerate its development.

Proud dad weighs in

Doug Cutting, founder of both projects (as well as Apache Lucene), formerly of Yahoo, and presently Chief Architect at Cloudera, wrote a blog post commemorating the birthday of the project, named after his son's stuffed elephant toy.

In his post, Cutting correctly points out that "Traditional enterprise RDBMS software now has competition: open source, big data software." The database industry had been in real stasis for well over a decade. Hadoop and NoSQL changed that, and got the incumbent vendors off their duffs and back in the business of refreshing their products with major new features.

Read More: http://www.zdnet.com/article/hadoop-turns-10-big-data-industry-rolls-along/

How relevant is NoSQL in the enterprise?

On Demand Watch our on-demand webcast where we look into whether NoSQL is a suitable fit for the enterprise.

Once upon a time, there was only one mainstream database architecture. Relational databases management systems (RDBMS), which stored information in tables and enabled access via a structured query language, were the only real show in town.

Then came NoSQL-based approaches, offering unprecedented scalability and alternative forms of access, which helped drive whole new ways of storing and querying information making viable a number of new workloads. Today, NoSQL is moving from specific use cases into more generally applicable areas and therefore broader deployments.

This raises a number of questions. What models are NoSQL approaches most appropriate for? What trade-offs have to be made relative to relational approaches? And what do decision-makers need to consider in advance so they don’t come unstuck down the line?

This is our last Regcast for 2015, and we promise give you answers to SQL or NoSQL questions.

Read More: http://www.theregister.co.uk/2015/12/18/how_relevant_is_nosql_in_the_enterprise/

The hidden costs of NoSQL

The NoSQL industry was developed quickly on the promise of schema-free design, infinitely scalable clusters and breakthrough performance. But there are hidden costs, including the added complexity of an endless choice of datastores (now numbering 225), the realization that analytics without SQL is painful, high query latencies require you to pre-compute results, and the inefficient use of hardware leads to server sprawl.

All of these costs add up to a picture far less rosy than initially presented. However, the data model for NoSQL does make sense for certain workloads, across key-value and document data types. Fortunately, those are now incorporated into multi-mode and multi-model databases representing a simplified and consolidated approach to data management.

Let’s take a closer look at the impetus for the NoSQL movement and the true impact of abandoning SQL.

Dawn and decline of the NoSQL movement

The popularity of NoSQL grew from the need to scale beyond what traditional disk-based relational databases could handle, and because high performance solutions from large database companies get very expensive very quickly. Coupled with data growth, developers needed a better way for the growing use of simple data structures like users and profile information associated with mobile applications. NoSQL promised an easy path to performance.

Another explanation for NoSQL popularity comes from the perception that SQL can be hard to learn. But Michael Stahnke, director of engineering at Puppet Labs, claims that is an early, and invalid argument, noting that, “instead you must learn one query language for each tool you use.”

Read More: http://www.networkworld.com/article/3019122/tech-primers/the-hidden-costs-of-nosql.html