Big Data Needs Big Security

- David Canellos, president and CEO of PerspecSys (www.perspecsys.com):

“Big data” is currently one of the hottest topics in IT, technology and business. At its core, big data encompasses the idea that massive amounts of business data, if harnessed effectively and efficiently, can be mined to deliver critical business insights that can transform a business. A new class of companies, such as Zynga, is fuelling this ‘data boom’ by making it easier than ever for enterprises to perform analytics on massive stores of data. The data can include interactions with customers on Twitter, Facebook, LinkedIn, etc., as well as interactions from call centers and e-mail, and notes from traditional sales representatives. All vertical industries can potentially benefit from analytics on this type of data. (For example, McKinsey & Co. estimated that retail chains can use analytics to increase their margins by 60 percent.)

Big data’s potential should be of particular interest to organizations going through a cloud transition. Why? Because efforts to virtualize, centralize and standardize inevitably lead to data aggregation. And while the data can be harnessed to deliver tremendous business value, data aggregation comes with a security downside.

It goes without saying that centralized, extremely large volumes of data carry significant security risk, primarily because hackers love to focus on them. I cannot think of a more appealing target for a cybercriminal who wants to infiltrate your organization’s core information assets.

Are your Cloud Service Providers’ security standards up to the same level as your own? Enterprises have made tremendous investments over the past 10 years to ensure they have a hardened infrastructure and a set of policies to protect sensitive data stored and processed internally. Now they are scratching their heads thinking about how they can get that same security for their assets stored and transported via the cloud. Our advice?  Look for ways that let you leverage the cloud, but keep your sensitive data close to home.  Big Data in the Cloud – Sensitive Data at Home . . . that is a recipe for success.

When Big Data Meets Cloud Meets Infrastructure

- Lori MacVittie, senior technical marketing manager at F5 Networks (www.f5.com), says: 

In the past, almost all context was able to be deduced from the transport (connection) and application layer. The application delivery tier couldn’t necessarily “reach out” and take advantage of the vast amount of data “out there” that provides more insight into the conversation being initiated by a user. Much of this data falls into the realm of “big data” – untold amounts of information collected by this site and that site that offer valuable nuggets of information about any given interaction. 

"Because of its expanded computing power and capacity, cloud can store information about user preferences, which can enable product or service customization. The context-driven variability provided via cloud allows businesses to offer users personal experiences that adapt to subtle changes in user-defined context, allowing for a more user-centric experience."

-- “The power of cloud”, IBM Global Business Services

All this big data is a gold mine – but only if you can take advantage of it. For infrastructure and specifically application delivery systems that means somehow being able to access data relevant to an individual user from a variety of sources and applying some operational logic to determine, say, level of access or permission to interact with a service.

It’s collaboration. It’s integration. It’s an ecosystem.

It’s enabling context-aware networking in a new way. It’s really about being able to consume big data via an API that’s relevant to the task at hand. If you’re trying to determine if a request is coming from a legitimate user or a node in a known botnet, you can do that. If you want to understand what the current security posture of your public-facing web applications might be, you can do that. If you want to verify that your application delivery controller is configured optimally and is up to date with the latest software, you can do that.

What’s more important, however, is perhaps that such a system is a foundation for integrating services that reside in the cloud where petabytes of pertinent data has already been collected, analyzed, and categorized for consumption. Reputation, health, location. These are characteristics that barely scratch the surface of the kind of information that is available through services today that can dramatically improve the operational posture of the entire data center.

Imagine, too, if you could centralize the acquisition of that data and feed it to every application without substantially modifying the application? What if you could build an architecture that enables collaboration between the application delivery tier and application infrastructure in a service-focused way? One that enables every application to enquire as to the location or reputation or personal preferences of a user – stored “out there, in the cloud” – and use that information to make decisions about what components or data the application includes? Knowing a user prefers Apple or Microsoft products, for example, would allow an application to tailor data or integrate ads or other functionality specifically targeted for that user, that fits the user’s preferences. This user-centric data is out there, waiting to be used to enable a more personal experience. An application delivery tier-based architecture in which such data is aggregated and shared to all applications shortens the development life-cycle for such personally-tailored application features and ensures consistency across the entire application portfolio.

It is these kinds of capabilities that drive the integration of big data with infrastructure. First as a means to provide better control and flexibility in real-time over access to corporate resources by employees and consumers alike, and with an eye toward future capabilities that focus on collaboration inside the data center better enabling a more personal, tailored experience for all users.

It’s a common refrain across the industry that network infrastructure needs to be smarter, make more intelligent decisions, and leverage available information to do it. But actually integrating that data in a way that makes it possible for organizations to actually codify operational logic is something that’s rarely seen.

Until now. 

A Unified View of the Data Center

- Christophe Bertrand, senior director of corporate & product marketing at Hitachi Data Systems (http://www.hds.com), says:

Data center administrators are facing a myriad of challenges when it comes to managing the explosive growth of data coming into and moving across organizations today. Data capacity, applications and virtual servers are all growing at exponential rates and IT departments are struggling to store and manage all of that digital content, while keeping operational expenses in check. Furthermore, the threat of failing to meet data- or content-based service level objectives (SLO) for customers could result in financial and legal penalties for an organization. 

This means IT administrators are bogged down and forced to deal with housekeeping tasks, to reign in all that data, reducing their productivity and taking their focus away from higher level activities that could advance the business, ensure compliance, or deliver new, value added services and applications to employees.

Dissecting the Data Problem

IDC is predicting the biggest challenge for IT administrators will come from the type of data expected to grow the most – unstructured data, which will come into organizations over internet protocols as files or objects. These collections or “stores” of unstructured data will grow into hundreds and thousands of petabytes and billions of objects, requiring larger file systems and scalable block storage systems. However, these systems will not be enough. The growth of unstructured data will require the integration and management of file, block and object data. This convergence will translate into greater storage efficiencies by eliminating three major costs:

·         backup for data protection;

·         extracting, transforming, and loading (ETL) for data analysi;s; and,

·          managing silos of file, block and object data.

Plotting a Solution Among the Myriad of Options

To address this, many in the storage industry are putting a renewed focus on unified storage. While the idea of unified storage is not a new one, the market requirements and customer challenges have intensified since the first wave of unified storage products entered the market. What remains to be seen is how effective the unified storage offerings of today will be at addressing the increasingly more stringent SLA/SLO requirements of enterprise customers.   

In many ways, traditional unified storage is a bit of a “Jack of all trades…master of none”, and typically targeted at the lower tiers of the market. Most unified storage products today tend to be strong in one data type (either block or file) and weak in another. Many mid-market and enterprise users today need less complex and more unified infrastructures. What customers are really looking for is a “no compromise” approach to unified storage with equal block and file performance, scalability and reliability – with a single management framework. This approach to unified storage will help businesses of all sizes effectively address the many challenges related to managing data, including handling its growth, managing costs, simplifying complexity and meeting service level objectives.

The needs of the end user must come first. One of the top concerns of CIOs and IT administrators when discussing unified storage is they want the ability to have a unified view of their data center assets. This starts with the management of those assets. A truly unified approach allows IT to view and manage block, file and objects, all from a single place. This goes beyond unifying the management of assets within a particular product suite or stack of products, but across an entire suite of disparate solutions. By focusing on unified management, customers can manage and deploy their storage in a single solution, access block, file and object views, receive a unified dashboard, and access reporting tools across their infrastructure.

End users typically buy unified storage as a way to overcome the complexity of their infrastructure. However, while most unified platforms can theoretically handle large capacities of data, in reality scale creates tradeoffs: performance degradation, inability to protect the data effectively, inability to handle large files systems. Balanced scalability is critical, not just capacity scalability.

Organizations today want to simplify their acquisition models and gain new levels of flexibility when it comes to their storage solutions. What’s important to keep in mind when thinking about unified storage is whether or not the product is supported by a single management software platform for all data types, and how well the product integrates with the rest of the vendor’s portfolio. Is it part of a shared management framework, or another silo that adds to complexity?

The world has changed and re-examining what and where unified platforms fit is key. At the end of the day, unified storage products should be designed to eliminate silos, not cause more complexity and inefficiencies in the data center. 

Emergent Behavior and the ‘Big Data’ Question

- Scott Paly, co-founder and CEO for Global DataGuard (www.globaldataguard.com), says:

There has been a lot of buzz recently about ‘big data’ and how it may offer hope in catching cyber-thieves as they attempt to invade a company’s sprawling enterprise network. At the recent RSA conference in San Francisco, analysts commented on why and how they believe a market for security algorithms will emerge in response to the math-intensive analysis needed to spot anomalies in the ‘big data’ world of network security.

Gartner analyst Neil MacDonald pointed out that the ‘bad’ attacker intent on hiding his or her actions is an anomaly to the generally ‘good’ behavior of network users inside the network. These cyber-thieves are getting past traditional defenses, such as intrusion-prevention systems, firewalls, and anti-virus software, in order to infiltrate and steal highly sensitive data. Such attacks are often referred to as an Advanced Persistent Threat (APT), and are driven by hackers who are able to effectively hide their malevolent presence within networks. According to MacDonald, “we just don't know what ‘goodness’ and ‘badness’ looks like in terms of network activity. You have to know what goodness looks like to understand deviations from goodness." In his opinion, ‘big data’ offers new possibilities for security analysis, and he believes that security tools will have to evolve in order to meet this need.

Global DataGuard agrees and is already tackling the APT problem by developing new technology to address ‘big data’ analysis and correlation.

A Challenge for IT Management

Every IT department head that I’ve spoken with agrees that the majority of network security technology available today is reactive in nature, and that most enterprise security systems are comprised of loosely integrated or discrete ‘best of breed’ security offerings that focus on various critical aspects of network security but do not have the ability to retain and correlate suspicious traffic for more than a few minutes. What these individuals tell me they need is the ability to detect reconnaissance activity leading up to an attack – before a breach occurs – and they want a historical context and depth of analysis in order to more quickly detect a breach after it occurs.

Global DataGuard’s response has been to develop an architecture-based security system that utilizes network behavior analysis and correlation to enable IT personnel to manage, monitor, analyze, and correlate discrete security events, alerts, logs, and reports into actionable security threats across application subsystems. The goal is to help a company identify and actively respond to what some analysts refer to as ‘bad’ network activity. Combined with newly developed emergent behavior technology – which I’ll discuss in a moment – this type of unified, network behavior analysis-based system can effectively address the ‘big data’ conundrum.

How Emergent Behavior Technology Works in Identifying APTs

For several years, Global DataGuard has used network behavior analysis as a key component of our architecture-based approach to security, enabling IT managers to identify and respond to security threats that other products may not detect, including Advanced Persistent Threats (APTs). APTs are complex systems that mix specialized utilities and human behavior. Hackers understand how systems engineers like to work and use evasion techniques that avoid these common behaviors. For example, systems engineers like to study the behavior of the elements in order to understand the behavior of the system through reconstruction. Unfortunately, this approach doesn’t work when dealing with non-linear (or complex) systems, and the developers of APTs know this and use it to their advantage.

As mentioned earlier, nearly all network security technology is reactive in nature and comprised of disparate applications and appliances. This is why it is virtually impossible to track the type of low-level network activity that occurs over long periods and may be an indicator of an Advanced Persistent Threat. Here is where emergent behavior technology comes in to play. Although APTs are difficult to identify, the theft of data can never be completely invisible. By using emergent behavior technology within a behavioral-based unified security system, IT managers have at their disposal a tool that can more accurately determine very small changes within complex network relationships that may be indicators of an APT.

Global DataGuard’s emergent behavior technology uses advanced pattern matching across distributed systems to examine the network as a whole and identify bit level changes that are unique to each network. In this way, Global DataGuard’s security system can view the entire network as a ‘flow of bits’ that can be used to find unusual or altered operation of lower-level systems that may indicate an APT. This technology provides the capability of overcoming some of the limitations of signature and anomaly detection methods.

A look at the Future of Network Security

Global DataGuard believes that emergent behavior, as part of a unified approach to security, is a technology ‘next step’ for the security industry. We’ve already seen significant performance enhancements within our own network behavior analysis-based UES system, which is capable of performing predictive analysis by retaining and correlating suspicious raw packet data for a rolling 14-30 days and signature alerts and behavioral profiles for six months or longer, based on a customer’s specific requirements.

Because Global DataGuard’s architecture-based security system is both adaptive and predictive, it can provide IT managers and their staff with easier deployment and management of their company’s network security ecosystem, as well as provide greater efficiency in labor and detection ability, while offering lower acquisition costs than discrete security solutions. These products and services, in essence, are designed from the ground up to address compliance-specific requirements related to the integration of processes, technology, service, and reporting. Using a modular approach, they can be customized based on a company’s specific network requirements – from a few security applications to a complete system – providing IT managers and their staff with greater efficiency in labor and detection ability, lower acquisition costs, and easier deployment and management of their network security environment, whether it’s premise-based, virtual, or a cloud/on-premise network.

Data Management Tips for 2012

- Stephen Chan, VP of Business Development and Co-Founder of ZL Technologies (www.zlti.com), says:

In a recent study, Gartner found that enterprise data is expected to grow over 650% over the next five years. As the proliferation of big data continues to surge at a staggering rate, the challenges associated with management is expected to increase directly. Traditional methods will prove futile or too costly. The market is shifting and organizations are learning that the best approach to tackle the exponential influx of data is with a unified solution that utilizes single silo storage.

Here are some other trends to watch out for in the coming year:

Unstructured data is expanding. As we live in an increasingly digitalized world, more and more types of unstructured data are being introduced each day. In addition to emails and files, social media like Twitter, Facebook, SMS, BlackBerry messages, and other data types are rapidly entering the mix. But even as the types of data increase, maintaining all types in a unified silo become more and more important.

Email: The tail that wags the dog. While 80% of corporate data stored is unstructured, 80% of this unstructured data is comprised solely of email. It is the single largest data type within the organization. As a result, email archiving will become even more critical, ensuring that non-record emails are retained until properly classified or declared as a record. Unified control of emails at every stage is crucial to litigation, governance, records management, and business intelligence efforts because it provides the single source of truth for the business.

Stop keeping it forever. With the increase in data generated, there will also be increased attention on data disposition and lifecycle management. Proper classification and disposition policies must be established so that data is not stored forever. Unified control of lifecycle policies is the only way to ensure their effectiveness.

SuperSizing the Data Center: Big Data and Jumbo Frames

- Lori MacVittie, senior technical marketing manager at F5 Networks (www.f5.com), says:

For many of the same reasons IPv6 migration is moving slower than perhaps it should given the urgent need for more IP addresses (to support all those cows connecting to the Internet) is the sheer magnitude of such an effort. Without the ability for IPv6-only nodes to talk to IPv4-only nodes, there’s a lot of careful planning that has to happen around the globe to ensure success and continued communication between the two incompatible protocols.

In many ways, Jumbo Frames – despite performance advantages – suffer from the same technological incompatibility. Remember that Jumbo Frames – 9000 bytes – are incompatible with regular old sized Ethernet frames (1500 bytes). It makes sense for much the same reasons – you simply can’t stuff 9000 bytes into a frame designed to hold 1500. And one of the basic rules of Ethernet is that the smallest MTU (maximum transmission unit) used by any component in a network path determines the maximum MTU for all traffic that flows along that path.

And yet the benefits of Jumbo Frames have been demonstrated many times. It reduces fragmentation overhead (the process of splitting data into chunks small enough to fit into a 1500 byte frame) which translates into lower CPU overhead on hosts. It also allows for more aggressive TCP dynamics, which results in greater throughput and better responses to some kinds of loss. But even though Jumbo Frames can deliver an increase in throughput along with a simultaneous decrease in CPU utilization they are rarely, if ever, used in a data center network.
That, however, is changing.

You might recall some predictions with respect to 10GB adoption in the data center:
"We expect the Ethernet Switch market to experience two significant years of market growth in 2013 and 2014 from the migration of servers towards 10 Gigabit Ethernet," said Alan Weckel, Senior Director of Dell'Oro Group. "We believe that in 2013, most large enterprises will upgrade to 10 Gigabit Ethernet for server access through a mix of connectivity options ranging from blade servers, SFP+ direct attach and 10G Base-T.

-- Data Center to Drive Ethernet Switch Revenue Growth through 2016, According to Dell'Oro Group Forecast

Historically in the switching market the deployment of 10G in the core networks and the use of Jumbo Frames went pretty much hand-in-hand. Until recently, however, 10GB just wasn’t making its way into the data center (costs were too high) and the only place Jumbo Frames were really seen was within storage networks, particularly in conjunction with FCIP implementations.

For the most part, a lack of support within the data center infrastructure and no real urgency for the efficiency gains that come from Jumbo Frames (and the fact that the Internet is not using Jumbo Frames from end-to-end, which pretty much kills the value proposition) meant enterprise organizations looked at Jumbo Frames with a “someday, but not right now” attitude. But with the increasing adoption of virtualization and movement of 10G networks into datacenters (in part driven by virtualization), Jumbo Frames are becoming more of a reality for a larger population of organizations.

Consider the following support and recommendations for jumbo frames within VMware’s documentation:

TCP Segmentation Offload and Jumbo Frames:
Jumbo frames must be enabled at the host level using the command-line interface to configure the MTU size for each vSwitch. TCP Segmentation Offload (TSO) is enabled on the VMkernel interface by default, but must be enabled at the virtual machine level.
-- ESX 4.0 Config Guide, page 57

Optimizing vMotion Performance
Use of Jumbo Frames is recommend for best vMotion performance.
-- Page 188 vSphere 4.0 System Admin Guide

vSphere 4 Performance
Jumbo Frames is one of the suggested means of improving CPU performance with respect to vSphere
-- CPU Performance Enhancement Advice (Table 22-6, page 278)

Add in cloud computing and a desire to more quickly move big data (virtual machines) over the WAN to cloud providers for a variety of business initiatives – a process in which the number of frames sent and low latency is key to success - and Jumbo Frames suddenly start looking a lot more like a requirement than a “Yeah, yeah, we’ll get to that eventually. Maybe.”

Virtualization and cloud computing are transformative technologies. As some have often – and loudly – reminded us, the network is part of the data center, and indeed an integral part of the data center. While we tend to focus on the management and provisioning and automation of the data center and its cultural impact, we should not overlook the impact that these technologies and the changes they bring are having – and will have – on the network.

If cloud and virtualization and consumerization and emerging technologies like HTML5 are going to transform the data center, that’s going to necessarily include the network. Ultimately, support for Jumbo Frames will be a requirement – a checkbox item – for every component in the data center.

SDN and The Evolution of Data Center Networks for Big Data

- Sandy Orlando, vice president of marketing with IP Infusion (www.ipinfusion.com), says:

The tidal wave of Big Data is washing over today’s businesses. Big Data is not only measured in the quantity of data traversing the network, but according to Forrester, it is defined as the techniques and technologies that make handling data at extreme scale economical.

Big Data will drive new productivity growth and revenue potential. For example, McKinsey Global Institute predicts that Big Data will result in significant financial value across all sectors, generating over $300 billion in US health care, over $100 billion for service providers, and 60 percent increase in margins for retail.

How can today’s networks cost-effectively handle the high volume of interactive, multimedia traffic? Just adding more bandwidth will not solve the problem. The value chain of network providers spanning the mobile network, through Carrier Transport, to the Data Center must rethink networking to make it more cost effective and efficient to accommodate Big Data.

Big Data Requires Big Changes in the Network
How much data are we actually talking about with Big Data? The size of the datasets varies by sector, but they range from a few dozen terabytes to multiple petabytes. Moreover, Big Data introduces new technologies such as Hadoop and MapReduce. Unlike previous data transfer technologies that moved gigabytes of data in a single job, MapReduce can move multi-petabytes of data.

Big Data requires that the networking industry accelerate change, moving from legacy technologies in data transport such as SONET/SDH to a high-value-per-bit and lower-cost-per bit technologies such as Carrier Ethernet. In the data center, networking switching is moving rapidly to 10-40 Gbps and Ethernet is rapidly replacing older Storage Area Networking (SAN) technologies.

Migrating to Ethernet as an underlying transport will benefit all aspects of the transmission of Big Data. However, Ethernet on its own is not sufficient to handle the torrent of Big Data. Network architectures also need to change to become flatter and more flexible. For example, in today’s typical data center, the network architecture consists of one or more L3 core routers, multiple L3 access routers, L2 aggregation switches, load balancers, as well as top-of the rack switches. A hierarchical network forces data center operators to oversubscribe network resources up to 200:1. Taking advantage of new 10 and 40 Gbps Ethernet and higher performance silicon will improve price performance, but true innovation comes from creating a flatter network using software-defined networking technologies.

The Future of Big Data Networking is Software
The key to delivering higher-performance, more optimized networks is a software-defined networking architecture using a centralized control plane and fast forwarding data planes based on merchant silicon. The Open Networking Foundation (ONF) is championing the standardization of this approach.

Key software networking vendors, such as IP Infusion, have been offering modular, portable routing/switching software for more than a decade to leading network equipment manufacturers. Leveraging the innovations provided by SDN, network equipment providers can optimize the price-performance of Ethernet networks. Implementing a hybrid approach to SDN, preserves existing investment in legacy infrastructure and ensures a smooth transition to this new network paradigm.

Software Defined Networking is in its infancy, but the growing demands of Big Data will continue to drive new innovations in networking. Today, service providers and data center operators can exploit Ethernet to offer more economically attractive mobile and Big Data transport services. In the near future, the network equipment industry will accelerate the adoption of SDN to support Big Data. The successful companies will ride the wave of Big Data, transform their networks, and capitalize on new business opportunities.

Is Your Organization Hadoop-Ready?

- Jack Norris, vice president of marketing at MapR Technologies (www.mapr.com), says:

With the Internet now touching more than two billion people daily, every call, tweet, e-mail, download, or purchase generates valuable data. There is also a wealth of machine-generated data such as sensor data, video images, genomic data, etc that is growing at an even faster rate. Companies are increasingly relying on Hadoop to unlock the hidden value of this rapidly expanding data, and to drive increased growth and profitability. A recent IDC study confirmed that data is growing faster than Moore’s Law. The implication of this growth rate is that however you’re processing data today will require doing it with a larger cluster tomorrow.
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Put another way, the speed of data growth has changed the bottleneck. The network is the bottleneck. It takes longer to move data over the network than it takes to perform the analysis. A new computing paradigm is emerging to address this inefficiency -- performing data and compute together so only the results are shared over the network. The promise of Hadoop is the ability to effectively analyze large amounts of data with a new paradigm.

If you’re beginning the evaluation and selection of Hadoop, organizations need to understand the criteria that mean the most to their business or activity. There are a few distributions from which to choose when selecting Hadoop. Key questions to ask include:

• How easy is it to use?

How easily does data move into and out of the cluster?
Can the cluster be easily shared across users, workloads, and geographies?
Can the cluster easily accommodate access, protection, and security while supporting large numbers of files?

• How dependable is the Hadoop cluster?

Can it be trusted for production and business-critical data?
How does the distribution help ensure business continuity?
Can the cluster recover data from user and application errors?
Can data be mirrored between different clusters?

• How does it perform?

Is processing limited to batch applications?
Does the namenode create a performance bottleneck?
Does the system use hardware efficiently?

In order for Hadoop to be effective for a broad group of users and workloads, it must be easy to use, provision, operate and manage at scale. It should be easy to move data into and out of the cluster, provision cluster resources, and manage even very large Hadoop clusters with a small staff. It is advisable to look for real-time read/write data flows via the industry standard Network File System (NFS) protocol. Hadoop distributions are also limited by the write-once Hadoop Distributed File System (HDFS). Like a conventional CD-ROM, HDFS prevents files from being modified once they have been written, and files cannot be read before they are closed.

As data analysis needs grow, so does the need to effectively manage and utilize expensive cluster resources. It is often useful for organizations to have separate data sources and applications leveraged by the same Hadoop cluster. Ways to segment a cluster by user groups, projects, or divisions are also useful. The ability to separate a physical cluster into multiple, logical Hadoop clusters is very useful. A distribution should also be designed to work with multiple clusters and multi-cluster management. It is critical to look for simple installation, provisioning and manageability,

Data processing demands are becoming increasingly critical and these demands require the selection of a distribution that provides enterprise class reliability and data protection. Hadoop provides replication to protect against data loss, but for many applications and data sources, snapshots are required to provide point-in-time recovery to protect against end-user and application errors. Full business continuity features including remote mirroring, is also required in many data centers to meet recovery time objectives across data centers.

Data center computing is going through one of the largest paradigm shifts in decades. Are you ready for the change? Are you ready for Hadoop?

About Jack Norris, vice president of marketing, MapR.
Jack has over 20 years of enterprise software marketing experience. He has demonstrated success from defining new markets for small companies to increasing sales of new products for large public companies. Jack’s broad experience includes launching and establishing Aster Data, driving Rainfinity (EMC) to a market leadership position, and leading marketing and business development for an early-stage cloud storage software provider.

MapR Technologies
MapR delivers on the promise of Hadoop, making managing and analyzing Big Data a reality for more business users. The award-winning MapR Distribution brings unprecedented dependability, speed and ease-of-use to Hadoop combined with data protection and business continuity, enabling customers to harness the power of Big Data analytics