A number of growing opportunities for Hadoop-MapReduce are emerging from a changing environment where Big Data affects IT budgets in two ways:

• Necessity to accommodate exponentially increasing amounts of data (processing, storage, analysis);
• Progressively cost-prohibitive pricing models imposed by established IT vendors.

Based on surveys gathered across a wide range of industries and geographies, this report answers the following questions:

• What applications are driving adoption of Hadoop-MapReduce?
• What verticals are early Hadoop-MapReduce adopters?
• What are the largest vertical markets for Hadoop-MapReduce deployment?
• Where is the money in the Hadoop-MapReduce market?
• Where is the growth?
• What are business opportunities in the Hadoop-MapReduce market?

The report provides detailed year-by-year (2013 – 2018) forecasts for the following Hadoop-MapReduce market segments:

• Hadoop-MapReduce market segments by geographic region: Americas, EMEA and Asia/Pacific;
• By software, hardware and services: commercially supported Hadoop-MapReduce-related software, Hadoop-MapReduce hardware and appliances, Hadoop-MapReduce services (consulting, integration, middleware and support), training & outsourcing;
• By data tiers (amount of data managed by organization).

Hadoop-MapReduce Market Forecast 2013-2018, Tabular Analysis, February 2013, Pages: 37, Figures: 34, Tables: 7, Single User Price: $5,950.00
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Hadoop-MapReduce Market Forecast 2013-2018, Tabular Analysis, February 2013, Pages: 37, Figures: 34, Tables: 7, Global Site License: $9,950.00
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Table of Contents

List of Tables
Table 1 – HadoopMapReduce Market Drivers and Inhibitors
Table 2 – Most Probable Hadoop-MapReduce Deployment Scenarios
Table 3 – Latest Market Developments: Mergers and Acquisitions, Venture Capital Funding, Product Offers 15
Table 4 – Hadoop-MapReduce Market Forecast 2013-2018, $Mln
Table 5 – Hadoop-MapReduce Market 2013-2018 by Geographic Region, $Mln
Table 6 – Hadoop-MapReduce Market 2013-2018 by Software, Hardware and Services, $Mln
Table 7 – Hadoop-MapReduce Market 2013-2018 by Data Tiers (by amount of data managed by organization), $Mln

The ERP software giants pursue a double strategy in acquiring HR software companies:

• expanding and integrating the new best-in-class HR management functionality into their comprehensive product/service offerings;
• and introducing broader ERP product/service offerings through HR management loophole.

Despite lack of growth in such traditional segments of the HR software market as payroll, time and attendance, and benefits management, these segments still represent a big and lucrative chunk of the market. The growth in the HR software market is driven by talent management sectors (recruiting, training, performance management/BI, and leadership/succession management) as well as by shift to software-as-a-service (SaaS) model.

The report provides detailed year-by-year (2013 – 2018) forecasts for the following HR software market segments:

• HR software market segments by geographic region: Americas, EMEA and Asia/Pacific;
• By HR administration software – payroll, time and attendance, and benefits management;
• By talent management software – recruiting, training, performance management/BI, and leadership/succession management.
• By company size tiers;
• By deployment model – licensed on premise, licensed hosted, and software-as-a-service (SaaS).

Human Resources (HR) Software Market Forecast 2013-2018, Tabular Analysis, February 2013, Pages: 26, Figures: 20, Tables: 9, Single User Price: $5,950.00
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Human Resources (HR) Software Market Forecast 2013-2018, Tabular Analysis, February 2013, Pages: 26, Figures: 20, Tables: 9, Global Site License: $9,950.00
Reports are delivered in PDF format within 24 hours.
Analysis provides quantitative market research information in a concise tabular format. The tables/charts present a focused snapshot of market dynamics.

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Table of Contents

List of Tables
Table 1 – Major Market Developments: Mergers and Acquisitions, Venture Capital Funding, Product Offers
Table 2 – Niche Players to Watch
Table 3 – HR Software Market Forecast 2013-2018, $Mln
Table 4 – HR Software Market 2013-2018 by Geographic Region, $Mln
Table 5 – HR Software Market 2013-2018 by HR Administration and Talent Management, $Mln
Table 6 – HR Administration Software Market Segment 2013-2018, $Mln
Table 7 – Talent Management Software Market Segment 2013-2018, $Mln
Table 8 – HR Software Market 2013-2018 by Company Size Tiers, $Mln
Table 9 – HR Software Market 2013-2018 by Deployment Model, $Mln

• There is no such thing as good enough or fast enough in business intelligence.
• Moore’s law does not keep pace with Big Data.
• Faster business analytics directly affects the bottom line of business and managerial decision making at all levels of enterprise.

Brute force approach of throwing more CPU computing power at the BI problems doesn’t scale up and hasn’t for a very long time. There is a diminishing return on deploying more CPU power to process ever increasing data volume. Three data warehouse acceleration technologies are currently emerging and much of the necessary knowledge, infrastructure and experience are already in place, though not widely. These technologies are in-memory computing, FPGA (field-programmable gate arrays) and GPGPU (general-purpose computing on graphics processing units).

The report covers data warehouse (DW) acceleration products, technologies and services. The report provides detailed year-by-year (2015 – 2020) forecasts for the following data warehouse acceleration market segments:

• New DW appliances by acceleration technologies: in-memory computing, FPGA and GPGPU;
• DW acceleration market by R&D, DW acceleration services (planning, customization, support), upgrade of existing DW systems by accelerators (software & hardware), accelerator middleware, and training.

The report also provides projected DW acceleration market share by geography: Americas, EMEA, and Asia/Pacific.

Data Warehouse Acceleration Market Forecast 2015-2020, Tabular Analysis, February 2013, Pages: 13, Figures: 12, Tables: 3, Single User License Price: $5,950.00
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Analysis provides quantitative market research information in a concise tabular format. The tables/charts present a focused snapshot of market dynamics.

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Data Warehouse Acceleration Market Forecast 2015-2020, Tabular Analysis, February 2013, Pages: 13, Figures: 12, Tables: 3, Global Site License Price: $9,950.00
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Analysis provides quantitative market research information in a concise tabular format. The tables/charts present a focused snapshot of market dynamics.

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Table of Contents

List of Tables
Table 1 – Global Data Warehouse Acceleration Market Forecast 2015-20120, $Mln
Table 2 – New Data Warehouse Appliances* by Accelerator Technologies**: Market Forecast 2015-2020, $Mln 9
Table 3 – Data Warehouse Acceleration: R&D, Upgrade, Services, Middleware and Training Segments; Market Forecast 2015-2020, $Mln

Q1. What is your business model? Combination of free and enterprise licenses? Providing free open source database and charging for support/service? Is this business model sustainable in the long run?

Couchbase Inc. is the company behind the Couchbase open source project. We provide source code, a free community edition, and a paid enterprise edition.

Open Source. We provide the open source under the Apache 2.0 license.

Community Edition. We offer a Couchbase Server Community Edition that is free to download and use as you wish for production and non-production uses. Support for the Community Edition is provided by the community through the usual channels like forums, IRC, etc. There is, of course, no Service Level Agreement (SLA) associated with this support.

Enterprise Edition. We offer a Couchbase Server Enterprise Edition subscription that costs $2,499/node/year for 8×5 support and $4,499/node/year for 24×7 support. A “node” is a physical server or virtual server (regardless of the number of CPUs). The Enterprise Edition comes with an SLA where we guarantee response times for best practice expertise and technical support. It also comes with a commercial license. The only product difference between the Community and Enterprise Editions is that bug fixes and hot bug fixes are immediately available in the Enterprise Edition and experience some time lag before they get integrated into the Community Edition.

Sustainability of Business Model. Yes, we believe this model is sustainable in the long term. Customers typically use the Community Edition to learn about and experiment with the software and then develop their application if it is a good fit for their use case. When they are ready to go into production, they typically purchase the Enterprise Edition because they want best practice expertise and technical support when they go “live” with their application. While not all users of the Community Edition move to the Enterprise Edition when they go live, a high percentage do because the cost to their business of having their application go down for any length of time is far greater than the cost of the Enterprise Edition subscription.

Q2. What is your position on ACID compliance for NoSQL? Any plans to make Couchbase ACID compliant?

NoSQL (and Couchbase) provides a different set of tradeoffs than RDBMS. Those tradeoffs make a lot of sense for some applications and don’t for others – it really depends on the nature of the application, the profile of the data used by the application, and the tradeoffs you are willing to make. For some applications and data types, 100% durability is paramount regardless of the performance impact. For other applications and data types, relaxing durability is perfectly acceptable if performance can be significantly increased. For some applications, atomicity can easily be handled because all the data you want to manipulate can be put into a single document. For other applications, support for complex transaction is necessary to provide atomicity.

Couchbase’s goal is not to be 100% ACID compliant in the way that people using RDBMS are familiar with the term. Our goal is to provide a solution that provides the right amount of atomicity, consistency, isolation, and durability within the context of other tradeoffs like performance and scalability. We believe a growing number of developers will view these tradeoffs as better suited to the needs of their applications. Likewise some developers will prefer the ACID tradeoffs that relational databases make and will stick with a RDBMS approach.

Q3. Your current product/marketing strategy targets mostly online applications. Can you give us examples of Couchbase deployment in other verticals: government, data warehouses, business intelligence, genetic databases, image/video databases, financial applications?

Couchbase is an operational database for interactive, online applications. We’re not a data warehouse or an analytics platform. We have a number of connectors to these types of platforms so our customers can take advantage of the benefits of those types of products. While it’s not likely that you would store a large video/image library in Couchbase, it is definitely a use-case to store the metadata associated with video/image library in Couchbase. Likewise, huge amounts of genetic information is often analyzed on an analytics platform like Hadoop and the results of the analysis are then kept in an operational database like Couchbase where interactive applications can leverage the results of these analyses with sub millisecond latencies.

Q4. What is your opinion of Hadoop-MapReduce future? How do you see interaction between Couchbase and Hadoop-MapReduce framework?

Hadoop Map Reduce is a great big-data analysis framework. It is well suited for deep analysis of the kind of data often initially captured or served by NoSQL databases. However, it is inherently a batch oriented technology, limiting it to uses where it is okay that results are updated relatively infrequently and are not based on “current” data.

Couchbase offers a Hadoop connector in partnership with Cloudera. The connector allows data to be easily moved from a Couchbase Server cluster to a Hadoop cluster and analysis results to be written back into Couchbase Server. In these configurations Couchbase Server is utilized to keep the active data, which needs to be accessed at low latency and to receive writes or updates at high performance. The Hadoop cluster is then used to run deep analysis on the captured data.

In the advertisement-serving industry this technique is used to have Couchbase Server serve user profiles and capture real-time user activity at low latency, while the Hadoop cluster does the analysis of the captured data to update the profile targeting information.

Couchbase Server also supports incremental Map Reduce natively. If chaining of multiple map-reduce steps is not required, incremental map reduce allows map reduce analysis to be applied to data stored in Couchbase Server in real time, as the data changes. Results are incrementally updated, so unlike the batch-oriented map reduce approach used in Hadoop, current results are always available.

Q5. Structured or unstructured data? Which type of data makes the stronger case for Couchbase?

Most of the recent explosion of data is unstructured and semi-structured data. This type of data doesn’t fit very well with the highly structured schemas of relational databases. Document databases like Couchbase are schema-less (or maybe more accurately schema-inferred) databases so they work much better with unstructured and semi-structured data. As a result, if you have unstructured data it likely makes sense to check out Couchbase (or other NoSQL databases) because there is a good chance it will be a better choice than a relational database alternative.

Document databases are also good at storing structured data however so, depending on your use case, they may also make sense for this type of data. This is particularly true when your structured data is changing rapidly. When the data you want to store changes, a schema-based relational database forces you to go the DBA to figure out how to accommodate the change in the schema, move your data from the old schema to the new one, etc. and this can be a lengthy process. The schema-less approach found in document databases makes this much easier and more flexible – you just add the new fields to your documents.

Q6. What scenario do you envision for interaction between relational and non-relational databases?

For 40 years everyone used relational databases and only relational databases. We think that’s changing. We’re moving to a situation where multiple database technologies will be available and developers will pick the database that fits best with the data profile and use case of their application. For example, many companies have moved to a Service Oriented Architecture where each service can pick the database that fits best with that particular service. The classic strength of relational databases is where complex transactions are required, for example debit/credit type applications. We certainly have customers that use relational databases alongside Couchbase for these types of things.

Relational databases currently are the underpinning for 95%+ of the database industry. We think in 10-15 years this percentage will be dramatically smaller. But relational databases do many things well and we don’t think they are going away – they are just going to be one of many alternatives.

Q7. What future do you envision for Couchbase? IPO, acquisition by major database player?

The NoSQL industry is a big market opportunity, is growing fast, and I think will spawn companies that grow to be multi-hundred-million dollar companies in the not too distant future. As a result, I think an IPO is certainly possible for companies like Couchbase. NoSQL is also a very disruptive technology that I believe big, existing database companies like Oracle, IBM, Microsoft will find increasingly strategic to their business. As a result, I fully expect acquisitive companies like these (and others like VMware, EMC, Red Hat, etc.) to consider acquiring NoSQL companies at high valuations. I also think there are opportunities for mergers between leading new database companies to form new, larger next-generation database companies.

Couchbase’s goal is to build a large, independent company that can ultimately go public.

Q8. Do you see any synergy in custom hardware configuration enhancing Couchbase capabilities? Any future product offerings including hardware? Couchbase appliance?

I don’t expect Couchbase to offer hardware or appliances. We haven’t seen strong demand for this to date. We do however expect to integrate our products more tightly with various hardware products. For example, we are working with Fusion i/o to integrate our product more tightly with their SSD solution. We are also working with networking companies to integrate more closely with their products. I think you’ll see even more of this from us in the future.

Q9. What is Couchbase’s competitive advantage in the long run? How does Couchbase stand against competing NoSQL technologies?

It’s hard to tell what our competitive advantages will be in the long run since that depends as much on what our competitors do as what we do. What we have focused on to date and where we think we have significant competitive differentiation is in performance, scalability, and always-on 24x7x365 capability.

High Performance. Performance is usually measured in terms of read/write latencies and the throughput per server. Couchbase consistently delivers sub millisecond latencies for reads and writes. This allows app developers to meet the demanding responsiveness requirements of application users. Couchbase also provides much higher throughput than other NoSQL products and that throughput scales linearly with the number of servers in your cluster. This keeps the ops people happy because a smaller number of servers are required to meet throughput requirements. Some benchmarks have already been made public to support these claims and more benchmarks will be made public shortly. (See Understanding the performance benchmark published by Cisco and Solarflare using Couchbase Server)

Easy Scalability. A lot of people think all NoSQL products provide easy scalability simply because they are “NoSQL” but that really isn’t the case. Adding or removing capacity should be a single, simple operation whether you’re growing to 5, 25, 50, or more servers. Every node in the cluster should be the same and simple to set up. The approach to sharding should balance load evenly across a cluster to avoid “hot spots”. And you shouldn’t have to change your application as your database cluster grows, i.e. application-level sharding doesn’t count. Couchbase provides all of these capabilities together with the monitoring and alerting tools you need to watch the health of your cluster. We think we’re recognized in industry as a leader in scalability.

Always On 24x7x365. When applications are down users get frustrated and revenue opportunities are lost. Increasingly customers are demanding that the data tier be up and running 24x7x365. That means your app needs to stay available during software upgrades, hardware maintenance, and hardware failures. Online backup and restore also needs to be provided as well as datacenter disaster recovery support. Couchbase allows you to do all this and we think we are significantly differentiated in this area.

Q10. What is new in the Couchbase 2.0 release and when will it be available?

The 2.0 release is a very important one for Couchbase. The current (1.8) release of Couchbase is a key-value database. It provides all the high performance, easy scalability, and always-on capabilities that I talked about above. The Couchbase Server 2.0 release turns the product into a document database that provides developers with a rich set of indexing and querying capabilities. These are the kinds of features that many developers are accustomed to using when they are using relational databases and are a requirement for many projects. While we have been very successful as a key-value database, this new capability will significantly expand our market opportunity and provide MongoDB with direct competition for the first time.

With the 2.0 release we are also expanding our differentiation in “easy scalability” by offering Cross Data Center Replication (XDCR). This is an increasingly important requirement for many customers. This allows you to scale by putting the same database in multiple data centers around the world, keep them all synchronized, and provide better application performance because you are serving users from local datacenters. It also provides a necessary disaster recovery capability.

The 2.0 release is available today as a developer preview (called nightly builds) for developers to use. We will go to beta in early September and will release the product in early November.

• June 2012 Cloudant launches NoSQL data layer service for Windows Azure;
• May 2012 10gen secures $42 million in venture funding;
• January 2012 Amazon launches DynamoDB, a new NoSQL data service;
• January 2012 Oracle announces the availability of Oracle Big Data Appliance and partners with Cloudera to provide an Apache Hadoop distribution and tools for the Big Data Appliance;
• December 2011 Joyent launches SmartMachine Appliance for MongoDB;
• November 2011 Cloudera Inc., the provider of Apache Hadoop-based data management software and services, raises $40 million;
• November 2011 Basho, the company behind Riak, raises $5 Mln;
• November 2011 Danish Government licenses Basho’s proprietary enterprise NoSQL data Storage software to operate nationwide medical prescription card program;
• October 2011 Oracle announces availability of Oracle NoSQL Database.
• September 2011 Neo Technology, the NoSQL database developer behind Neo4j, raises $10.6 Million.

Darren Person, Chief Architect of Elsevier, evaluates Amazon DynamoDB as a distributed data store of choice:

“Elsevier is a $3 billion enterprise that provides science and health information to more than 30 million scientists, students and medical professionals worldwide. Each year we publish thousands of books, nearly 2,000 journals and more than 250,000 articles, which means our datasets are constantly and rapidly changing. We are always evaluating new technologies that will enable us to handle our large, varying workloads. Operating a distributed data store on our own is orders of magnitude more complicated and expensive to manage than traditional databases. DynamoDB delivers a high-performance service that can be easily scaled up or down to meet our needs, helping us eliminate complexity and lower costs.”

Max Schireson, president of 10gen, quoted in the company’s press release (MongoDB Extends Leadership in NoSQL), says that NoSQL is carving its niche in the huge RDBMS market:

“With enterprise customers such as Disney, Viacom and Telefonica, as well as Oracle and IBM entering the NoSQL market, we see NoSQL taking an increasing share on the $20 billion database market.”

The gap exists between technology readiness and technical community readiness. This gap opens the window of opportunities for third-party technology vendors to capitalize on providing system integration services including electrical balancing, energy peak management, interconnect technologies, battery/ultracapacitor hybrid solutions, low voltage energy harvesting sensor systems, etc. The ultracapacitor market is fast growing and expected to reach $3.5 Billion in 2020, according to a new market study Ultracapacitor Market Forecast 2015-2020.

The missing element that can drastically boost the ethanol market is a car engine optimized for high ethanol blends. Technically it’s hardly a novelty. The Ford Model T, produced from 1908 through 1927, was the first commercial vehicle capable of running on pure ethanol. The Ford Model T was fitted with a carburetor with adjustable jetting, allowing use of gasoline or ethanol, or a combination of both. A couple of years ago Ricardo Inc., a large auto engines manufacturer, had unsuccessfully tried to launch the so-called Ethanol Boost Direct Injection engine, or EBDI, customized to make the most out of ethanol’s properties, probably too early for the market.

What we need now is not another small fix for existing gasoline engines. We need badly a built from scratch new car engine optimized for maximal efficiency at the highest possible ethanol content. We need a new concept of car/engine combination optimized for high ethanol blends which is capable to compete on par with economics of gasoline car in terms of $/mile. That would be a real game changer.

Crude oil represents 76% plus 6% of refinery costs in the price structure of gasoline and respectively 67% plus 11% in the price structure of diesel. That already places about 80% of the petroleum-based fuel price beyond U.S. control. The refinery costs will not change significantly. Increased domestic oil production will not affect significantly world oil crude price. Taxes offer price flexibility only in the upward direction.

On the other hand, the ethanol price has three major components – feedstocks, enzyme and conversion, all of them greatly affected by technology advances paving the way for lower prices at the pump in the long run.

Relevant info:
Global Biofuel Production Forecast 2015-2020
What Energy Policy is Weird?
Artificially Low U.S. Gas Prices Hurt U.S. Economy in the Long Run
What Critical Components Should Be in Place for Biofuel Market to Happen?

• U.S. Federal Cybersecurity Market Forecast 2013-2018
• U.S. Federal IT Business Continuity and Disaster Recovery Market Forecast 2013-2018
• Deep Packet Inspection (DPI): U.S. Government Market Forecast 2013-2018

Another topic of the workshop was rapidly emerging cyber warfare segment, little known until the Stuxnet affair. A new type of cyber warfare Stuxnet specifically targets computer systems that control electricity, water treatment, nuclear and chemical plants, pipelines, communications networks, transportation systems and other critical infrastructure, and it is unique in its complexity, flexibility, and resilience. Neither its creator nor its target is known. Stuxnet was called a “game changer” at a hearing of the Homeland Security and Governmental Affairs Committee on the implications of the recently discovered malware potentially far more destructive than any previously known cyber threat.

While corn and sugarcane will remain the staple feedstock of the ethanol production, the future belongs to cellulosic and algae-sourced ethanol. The same goes for biodiesel – cellulosic and algae-sourced biodiesel will gradually find their way to the liquid fuel infrastructure. These are conclusions of a recently released market report Global Biofuel Production Forecast 2015-2020. The U.S. has a significant technological advantage in development, manufacture and infrastructure of advanced algae-sourced and cellulosic biofuels, says the report.

Debates between “fossil” and “green” camps on role and significance of biofuels in the U.S. energy policy often overlook two important factors:

• Geopolitical and economical leverage of biofuels far exceeds their direct impact, even at a relatively small rate of substitution;
• Technology and economy of biofuel production are improving very fast.

Just a decade ago, it was barely feasible to develop biofuel economy with oil price below $30 per barrel. In a decade the world has already reached the level of biofuel production comparable with oil production in 1930 – 1950. Even the next five years may prove crucial to crack the absolute hold of petroleum over the world economy.