IBM’s Acquisition of Cloudant And The Walmart Effect In Tech

Last week, IBM announced an agreement to acquire NoSQL database as a service vendor Cloudant for an undisclosed sum. An active contributor to the Apache CouchDB project, Cloudant delivers a JSON document database-based platform that claims high availability, scalability and elasticity amongst its attributes. Cloudant customers can take advantage of its JSON-based database as a service to store and mine structured and unstructured data from a variety of sources. Because the JSON database format is so widely used by developers of mobile and web applications, IBM’s acquisition of Cloudant stands to strengthen its positioning with respect to the development of applications for mobile devices in conjunction with the build out of its OpenStack-based cloud solution for the enterprise. The acquisition of Cloudant will be central to IBM’s MobileFirst solutions as well as its Worklight application for developing mobile applications. From an industry perspective, the acquisition represents a huge coup for the NoSQL space in general. CouchDB has historically not had the traction of MongoDB, Cassandra and Couchbase, so we should expect brand name tech companies to make similar offerings for the likes of MongoDB in the ensuing few months. Moreover, IBM’s acquisition of Cloudant testifies to the increasing emergence of cloud and big data behemoths with solutions for both hosting infrastructure, as well as database solutions that accommodate enterprise needs for scalability and the ability to store unstructured data. Cloudant CEO Derek Schoettle surmised the significance of Cloudant’s contribution to IBM’s SoftLayer cloud platform as follows:

Cloudant’s decision to join IBM highlights that the next wave of enterprise technology innovation has moved beyond infrastructure and is now happening at the data layer. Our relationship with IBM and SoftLayer has evolved significantly in recent years, with more connected devices generating data at an unprecedented rate. Cloudant’s NoSQL expertise, combined with IBM’s enterprise reliability and resources, adds data layer services to the IBM portfolio that others can’t match.

Schoettle notes that IBM is extending its infrastructure innovations to the “data layer” and as such, follows in the footsteps of Amazon Web Services and EMC/VMware spin-off Pivotal, which similarly deliver a combination of cloud and big data solutions in their platform and product offerings. The notable consequence of this convergence of cloud and big data product offerings is that only large enterprises with the requisite capital and resources can afford to cobble together combined cloud-big data product offerings. As a result, cloud startups and smaller data vendors will need to continue to compete by way of their agility, responsiveness, consultative support and superior technology. In effect, the IBM acquisition of Cloudant signals a Walmart effect in technology, of sorts, whereby large, well capitalized vendors have the ability to create marts of diverse data and analytics products that threaten the viability of cloud, big data and analytics startups in the same way that massive retailers such as Walmart threaten the viability of independent stores or small chains. Oracle’s recent acquisition of Blue Kai, a big data management platform geared toward marketing, constitutes another example of the way in which tech giants are continuing to integrate diverse data products into increasingly heterogeneous product portfolios. The question that remains unanswered, however, is whether the emerging Walmart technology maze is sufficiently easy to navigate that enterprises opt to partner either with one vendor for all of their technology needs, or whether they feel more comfortable shopping from a diverse range of technology vendors in order to avoid vendor lock-in and locate products that richly respond to the specificities of their industry-vertical and customer needs.

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Glassbeam SCALAR Delivers Machine Data Analytics And BI For Internet Of Things

Today, machine data analytics vendor Glassbeam announces the release of Glassbeam SCALAR, a cloud-based platform that specializes in machine data analytics for the internet of things. In contrast to machine data analytics vendors such as Splunk and Loggly that focus on analytics related to machine data specific to data center and cloud-based IT environments, Glassbeam concentrates on complex, multi-structured log data from medical devices, sensors and automobiles in addition to data center devices.

The SCALAR platform leverages open source Big Data technologies such as Cassandra and Solr to deliver actionable business intelligence derived from structured and unstructured data. Meanwhile, SCALAR’s parallel asynchronous engine allows the platform to scale horizontally to process massive amounts of machine data that are interpreted by way of its dynamic schema functionality and semantic technologies that enrich the platform’s ability to make connections between source data elements.

A key component of the Glassbeam SCALAR platform is Glassbeam Studio, a tool that allows developers to transform unstructured machine data into structured formats for the purpose of performing a deeper dive into incoming data. Glassbeam Studio positions developers to transform data into Glassbeam’s proprietary semiotic parsing language (SPL) for understanding machine data before it is digested by the Glassbeam SCALAR platform and transformed into visual analytics as illustrated below:

Glassbeam’s output consists of visually rich, business intelligence dashboards that enable customers to make proactive, strategic decisions by way of a dynamic, real-time dashboard that illustrates trends and key attributes of source data. Key features of the Glassbeam platform in relation to the larger landscape of machine data sources and modalities of deployment are as follows:

Given that the internet of things remains to be fully realized, the release of Glassbeam SCALAR represents a bold attempt to carve out an early niche in the space by focusing on business intelligence in relation to machine data. Nevertheless, the underlying technology platform impresses by way of the breadth of its functionality as exemplified by Glassbeam Explorer, the platform’s new search and discovery tool for understanding, analyzing and visualizing machine data. Expect Glassbeam to continue to innovate and differentiate itself from the rest of the machine data analytics space, even as it captures market share related to data center devices such as servers, routers and firewall technologies. In the meantime, the race is on with respect to who will capture the early foothold in the internet of things Big Data analytics space as the industrial internet matures and gradually renders itself more and more ubiquitous in the lives of enterprises and consumers alike. Pivotal One has already placed its bets in the Internet of Things analytics space thanks to a $105 million investment from GE, but the landscape still remains wide open.

Apache Releases Version 1.2 Of NoSQL Database Cassandra

On Wednesday, the Apache Software Foundation announced the release of Cassandra version 1.2, the high performance, highly scalable, Big Data distributed NoSQL database. Cassandra is capable of managing thousands of data requests per second and is used by organizations such as Adobe, Cisco, Constant Contact, Digg, Disney, eBay, Netflix, Rackspace and Twitter.

Key components of the latest release include the following:

Virtual nodes and clustering across virtual nodes
•Node to node communication
•Atomic batches
•Request tracing
•Version 3 of the Cassandra Query Language (CQL) to simplify the modeling of applications, enable more powerful mapping and facilitate superior database design

Jonathan Ellis, Vice President of Apache Cassandra, reflected on the significance of the Cassandra 1.2 release as follows:

We are pleased to announce Cassandra 1.2. By improving support for dense clusters —powering multiple terabytes per node— as well as simplifying application modeling, and improving data cell storage/design/representation, systems are able to effortlessly scale petabytes of data.

Here, Ellis notes that one of the key functionality upgrades specific to Cassandra consists of enhanced support for dense clusters featuring several terabytes per node. The conjunction of the platform’s improved support for dense clusters with its streamlined application modeling capability and superior design abilities allows for vastly improved scalability for petabytes of data.

Cassandra users expressed particular enthusiasm for the virtual node and atomic batch components of the new release. Software developer Kelly Sommers elaborated on the significance of Cassandra 1.2’s improved handling of virtual nodes as follows:

In Cassandra v1.2 the introduction of vnodes will simplify managing clusters while improving performance when adding and rebuilding nodes. v1.2 also includes many new features, performance improvements and further heap reduction to alleviate the burden on the JVM garbage collector.

Virtual nodes improves performance, notes Sommers. Meanwhile, reducing the burden on the JVM garbage collector similarly enables notable performance enhancements as detailed by a recent blog post by Twitter, which noted how JVM garbage collector optimization significantly reduced CPU time for Twitter.com, separate from any direct reference to Cassandra.

Improved performance, increased scalabilty and simplified application development represent the three recurring themes from user experiences of the Cassandra 1.2 release. In contrast to Hadoop, Cassandra is known for its ability to handle massive amounts of real-time operational data whereas Hadoop is famed for its ability to deal with batch-based volumes of data. The latest release means that Big Data just got even bigger by virtue of Cassandra 1.2’s performance enhancements and application modeling and database design simplifications.