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Monday, March 30th, 2015
| Time | Event |
| 12:00p | Apple’s FoundationDB Deal Sends Waves of Concern Across Open Source World Apple’s recent acquisition of formerly open source FoundationDB has stirred a larger debate. Even before news of the acquisition went public, the startup reportedly turned off software downloads from its website and announced to users that it would no longer provide support for the NoSQL database software, leaving those who were using it and engaged in the open source project in a tight spot. FoundationDB didn’t have many known customers, but the open source world was upset. The acquisition is a microcosm of what’s going on in the industry at large. To some, open source and proprietary worlds are converging; to others, they are clashing. Open source is now mainstream, and many companies that have historically stuck mainly to proprietary tech have been getting involved in open source projects in meaningful ways or buying smaller players that have built businesses around open source. More such acquisitions are probably on the horizon. How they will affect open source projects is of great concern. In each of these deals between two companies, there is a third party present: the community. Commercial needs and community needs are sometimes at odds. A lot of the big-vendor momentum behind open source projects is driven by popularity of the projects themselves among users. Users can influence the direction of open source technologies without being locked into specific vendors’ platforms. “You won’t be looking to a specific vendor for your roadmap,” as Rackspace CTO John Engates put it in a past interview with Data Center Knowledge. A successful open source project around a piece of technology usually creates an ecosystem of users and vendors which in turn encourages interoperability. Again, the user benefits. Not everybody thinks Apple’s FoundationDB acquisition is a blow to the open source movement. To Adam Wray, CEO of Basho, a NoSQL database company, it just illustrates that the need for databases that can scale with modern distributed applications is real. “Apple is acutely aware of the importance of being able to reliably scale to meet the real-time data needs of today’s global applications,” he said. “The news of Apple’s intent to acquire FoundationDB greatly amplifies these points to a growing number of IT and engineering leaders.” Peter Goldmacher, vice president of strategy at Aerospike, another open source NoSQL database vendor, is thinking along the same lines. “There is an industry mindset that exists with people who care about the open source space that there is a new movement afoot, and the relational database is under threat by a newer generation of NoSQL players, accepting conventional wisdom that these guys are going to take workloads off of Oracle and revolutionize the space,” he writes. “Apple is the most well-known and trusted name in technology today, and their acquisition of FoundationDB is a sign that newer players like Cassandra aren’t going to be able to meet increasingly staggering workloads.” When one open source company buys another, it’s not as problematic for the open source community. Docker, for example, has maintained that building a successful ecosystem around its technology is key to the company’s success. So far, it has been expanding through targeted acquisitions of Docker-related open source-y projects. Red Hat has made several open source acquisitions and shows success isn’t dependent on making tech proprietary. “If we were doing this as a proprietary company, it would be a path to lock-in,” Red Hat’s senior vice president of infrastructure Tim Yeaton said in an earlier discussion with DCK. “Every single thing we do is all open source.” |
| 3:30p | Is Big Data a Bubble Set to Burst? As Co-Founder & Director of Business Development at SiSense, Adi Azaria is a passionate entrepreneur, author, and an established thought leader in innovative technology. The term “Big Data” has become increasingly popular over the past few years. For a while, it appeared as though you couldn’t throw a stick (or a Google search) without running into someone speaking of its wondrous possibilities. Just think about all the data being generated, collected and statistically analyzed by modern organizations, and the ways it would seemingly revolutionize our everyday lives.  Interest in Big Data over time (source: Google Trends). Click to enlarge. In early 2015, however, it seems this trend might be reversing, and the business world is growing disenchanted with data. Today the discussion revolves around whether Big Data will ever be able to live up to its promise, with an increasing number of skeptical voices being heard. The sentiment seems to be gearing toward overall disappointment. As someone who has been involved in the business intelligence industry for the past 10 years, I actually see this as a blessing: Indeed, the time has come for the Big Data bubble to burst. I’m not saying this because I believe that data analysis and BI software tools have ceased to be valuable assets for organizations. On the contrary, data analytics is still far from reaching its full potential. It is already delivering real, proven value to businesses, and will proceed to become even more so as the technology that drives it becomes more efficient and accessible. This has nothing to do with Big Data, though; because by and large, the term Big Data is nothing more than a catchphrase. The Need for More Precise TerminologyThe problem is, no one seems sure how to define Big Data, and variations differ. Some might refer to any dataset at terabyte-scale as “Big Data”, while others will rely on the 3\4\7 Vs module (typically Volume, Veracity, Variety, Veracity). Most of the talk around Big Data today, however, is generated by vendors, and they won’t even bother giving a definition. More than likely, their current capabilities will invariably be described as Big Data analytics. When a term that is commonly used lacks a clear-cut definition, different expectations are going to be lumped onto it. Disappointment is naturally inevitable. Perhaps it would be beneficial if we stopped talking about Big Data as a term, and just stated exactly what types of data we are talking about: structured or unstructured, one million rows or 100 million, homegrown or harvested externally, and so on. This could help us understand what it is—and isn’t—and set realistic expectations when dealing with specific datasets. We could determine if they should be processed according to actionable timeframes, or in the types of insights and value that can be derived. Approaching the Plateau of ProductivityGartner’s 2014 Hype Cycle report, released last August, positioned Big Data as one of the technologies well on the way to its “trough of disillusionment”—meaning the initial inflated expectations of it were beginning to recede and the industry’s high hopes for transformative powers are somewhat fading.  Gartner hype cycle for 2014 (source: Gartner). Click to enlarge. According to Gartner, Big Data is expected to reach its plateau of productivity, i.e. take its spot as part of the core activity of mainstream business within five to 10 years. However, another interesting aspect to note is the other data-driven technologies and fields that are also on the rise: from prescriptive analytics to the quantified self to wearable devices, and, of course, the Internet of Things, which is cited as one of the main reasons behind Hitachi’s $500 million acquisition of Pentaho. This tells us one thing: data is not going anywhere. There’s absolutely no denying that more data is available, and innovative new ways of analyzing and using it are evolving. So, while it might be as good a time as any to lay the overhyped “Big Data” term to rest, there is definitely a bright future for data analytics as well as the technologies and practices that derive from it. Industry Perspectives is a content channel at Data Center Knowledge highlighting thought leadership in the data center arena. See our guidelines and submission process for information on participating. View previously published Industry Perspectives in our Knowledge Library. |
| 4:54p | HP’s Big Data Platform Haven Gets Better at Languages HP has updated its big data platform Haven with new analytics and predictive capabilities. The platform is tuned for enterprises with lots of data and lots of different kinds of data, and the update expands the amount of things users can do with it. The release expands variety of data that can be ingested and analyzed through a new connector framework. It also exposes relationships and connections in diverse data via a new Knowledge Graphing feature and features better speech-recognition and language-identification capabilities. Haven is a bundle of HP analytics, hardware, and services, with some components of the platform available on-demand as of late last year. The company announced its big data platform in 2013, Haven becoming an umbrella name for multiple technologies. The new connector framework brings analytics for both unstructured and structured data, combining context-aware unstructured data analytics of HP IDOL with SQL-based analytics capabilities of HP Vertica. Some examples of enterprise cloud and data sources include Microsoft Exchange, SharePoint, Oracle, and SAP enterprise applications and cloud services like Box, Salesforce.com, and Google Drive. Vertica also works with major Hadoop distributions. The Knowledge Graphing feature analyzes valuable relationships and connections in your data. It enables advanced and contextually aware semantic search within diverse data sources such as social media content feeds. Nittingham Trent University in the U.K., a university with 28,000 students, used Haven to proactive identify underperforming students so the university can intervene before a problem develops. “With HP Haven’s unified design platform and industry-leading predictive analytics capabilities, NTU estimates their tutors are able to intervene six to eight weeks earlier than without the big data solution,” Howard Hall, group managing director of DTP Group, an HP partner, said in a statement. “Our tutors have said the system has resulted in positive changes in workgroup interaction.” Speech-to-text and language identification capabilities work with over 20 languages. The company said this is powered by advanced deep neural network technology and is the result of tens of thousands of hours of audio sampling via the self-learning neural network. Other upgrades include Targeted Query Response and IDOL Search Optimizer. Targeted Query Response helps refine and customize search results based on specific sets of criteria, while Search Optimizer is for understanding the types of searches being conducted by users and gauging the quality of results returned. “Businesses can reap the full power of enterprise data and big data computing across virtually any data type, from free-text-based document search to high-resolution broadcast video surveillance analytics,” Jeff Veis, a vice president of marketing at HP, said in a press release. “Further, with the HP Haven Big Data Platform, they don’t have to rely on specialized data scientists and costly, complex integration projects to do so.” |
| 5:25p | Interoute Lands New Investors to Fuel Cloud Acquisitions Cloud provider Interoute has taken on new private-equity investors as it prepares to make more cloud acquisitions. Funding from U.K.-based Aleph Capital Partners and U.S. firm Crestview Partners will help pay for acquisitions across Europe and the U.S. Aleph and Crestview have agreed to buy a 30-percent stake in Interoute from minority shareholder Emirates Communications. The size of the investment has not been disclosed. Interoute will build out its network, data center, and cloud platform. The European Infrastructure-as-a-Service provider launched close to 10 new zones last year, including its first two in the U.S. The equity will help it continue to aggressively expand, both organically and through acquisition. The European data center provider market is undergoing a lot of consolidation, the biggest recent merger being Interxion and TeleCity in the data center space. More European cloud acquisitions are expected, as market remains fragmented. Additional equity helps Interoute become a potential roll-up play rather than a potential acquisition. The company emphasizes the network in its cloud. The company’s cloud platform is location-sensitive, meant to make compliance with European data-sovereignty laws easier. In-country data needs and data sensitivity is a partial contributor to the European cloud market’s fragmentation. Interoute’s specialization positions it well to act as a roll-up. “Interoute seeks to more than double its revenues in the next 5 years, underpinned by demand for its enterprise networked cloud services,” said Interoute CEO Gareth Williams in a press release. “Adding new markets, capabilities and additional customers to experience these services will be the focus of the acquisitions. We are thrilled to have found ambitious tech-savvy partners who want to help us reach that goal.” |
| 6:24p | Video: Apple Data Center Expansion in Reno in High Gear Apple is quickly expanding its data center campus in Reno, Nevada. Its first building on the territory of Reno Technology Park went up in 2013, and today, there are four data center modules, an administration facility, and an equipment building at the site. A Washoe County planning official told us in August the company’s approach to data center build-out at the site was to expand in 25,000-square-foot interconnected modules called “data clusters.” At the time, Apple had two completed clusters at the site and was close to finishing two more. It had also applied for planning permission to build another four. Besides Apple data center facilities, the company is planning to build a massive solar array at the site. It has publicly committed to powering its operations with 100-percent renewable energy, and in 2013 its data centers for the first time used more energy than any of its other facilities. Other Apple data center locations are in Maiden, North Carolina, Prineville, Oregon, and Newark, California. The company also leases data center space from specialized providers but has not disclosed those facilities’ locations. The Apple Insider blog has produced a video of a recent flyover above the Apple data center in Reno that illustrates progress at the site over the past two years or so: |
| 8:15p | Coho, Intel Rev Up Docker Containers with Flash and Hadoop Coho Data, a provider of Flash memory arrays, is now working with Intel on a way to redefine how I/O in the age of containers will be managed in the data center. The two companies have created a reference architecture through which Docker containers running on the distribution of Hadoop from Cloudera can now run directly on a Coho storage cluster based on Flash memory technologies. Coho CTO Andy Warfield says the goal is to be able to scale micro services based on containers across a storage cluster using a Data Plane Development Kit (DPDK) created by Intel. “We’ve built it from the ground up for containers,” says Warfield. “We’re essentially using HDFS (Hadoop Distributed File System) as a protocol.” Warfield says the fundamental problem that two companies are trying to address is that on the one hand processors are faster than ever. On the other side, network bandwidth is increasing as well. But Warfield notes there hasn’t been a corresponding increase in storage I/O performance to eliminate the bottlenecks that will inevitably occur. The Flash storage arrays developed by Coho plug directly into a PCIe slot. Coho Data then built what it describes as a DataStream NFS datastore that can be accessed via a single IP address. The company says the DataStream platform enables data to be accessed over a linearly scalable NFS implementation that can scale from 180K IOPs across two Coho Data MicroArrays to 1.8 million IOPs across 20 MicroArrays. But Coho can also define multiple data profiles using, for example, containers rather than NFS. Each Data Profile directly connects to the DataStream DirectConnect application programming interfaces (APIs). In general, more primary storage is moving into Flash memory because it’s both faster than magnetic storage and requires a whole lot less effort to optimize I/O performance. Magnetic storage utilization rates are often kept low to optimize I/O performance across spinning media. Flash storage, on the other hand, may be more expensive than magnetic storage, but reductions in I/O management overhead make it a more appealing alternative for primary storage. Containers, such as Docker, further exacerbate I/O challenges by increasing the number of applications trying to access storage resources on a server. Where there might have been 30 virtual machines on a physical server it’s not uncommon to find 100 Docker containers. Unless new approaches to managing I/O access are found, it’s clear that emergence of containers will inevitably lead to I/O bottlenecks that will constrain the number of containers that can be deployed per physical server. Andreessen Horowitz-backed Coho came out of stealth in 2013 with an on-premise storage solution built on commodity hardware with sophisticated software-defined storage capabilities. Also that year, the company raised $25 million. |
| 8:44p | China Behind Sustained DDoS Attack Against GitHub: Researchers
This article originally appeared at The WHIR Code repository Github is still battling against a DDoS attack that began on Thursday that the company is calling the largest DDoS attack in its history. According to several reports, the attack appears to originate from China. According to a blog post by GitHub on Friday: “the attack began around 2AM UTC on Thursday, March 26, and involves a wide combination of attack vectors. These include every vector we’ve seen in previous attacks as well as some sophisticated new techniques that use the web browsers of unsuspecting, uninvolved people to flood github.com with high levels of traffic.” According to a report on Monday by Ars Technica, the DDoS is specifically targeting two GitHub projects that are anti-censorship in China: GreatFire and a Chinese language version of the New York Times. GitHub said that based on reports it has received the attack is intended to remove a specific class of content. As of 11:18 am, Github said that all systems are reporting at 100 percent. Security researchers at Insight Labs said the DDoS is being caused by some malicious JavaScript code that is being injected by a “device at the border of China’s inner network and the Internet” when people visit China’s Baidu search engine, according to Ars. GitHub kept users updated on its DDoS mitigation efforts and progress via its status Twitter account. An update around seven hours ago said that the DDoS attack had evolved and it is working to mitigate. Baidu has denied the attack, assuring that it has “ruled out the possibility of security problems or hacker attacks on [its] own products,” the company said in a statement. The DDos attack against GitHub comes after GreatFire.org mirror sites were hit by a sustained DDoS attack following an article published by the Wall Street Journal that described how anti-censorship groups use US cloud computing services to circumvent blocking by Chinese authorities. The attack was delivering up to 2.6 billion requests per hour at its peak. This story originally ran at http://www.thewhir.com/web-hosting-news/c |
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