Hadoop-NoSQL Software and Services Market Forecast,

Size: px
Start display at page:

Download "Hadoop-NoSQL Software and Services Market Forecast, 2014-2017"

Transcription

1 Wikibon.com - Hadoop-NoSQL Software and Services Market Forecast, by Jeff Kelly - 19 December / 12

2 Executive Summary The data management market is in the midst of revolutionary change. One need only glance at the business and technology press for evidence. Oracle, the dominant player in the relational database market, is missing new database license revenue targets quarter after quarter. Meanwhile, Teradata, a company synonymous with the enterprise data warehouse, has lost over $6 billion in market capitalization over the last two-plus years as its largest customers reduce their spend with the company. And SAP, the stalwart ERP and business intelligence vendor, is working hard to position HANA as the be-all-end-all in database technology with limited success. These struggles are the result of enterprise practitioners, both on the IT and business sides of the house, who are frustrated with the data management status quo. These frustrations include: 1. Cost. Data volumes are growing exponentially year-after-year while IT budgets remain flat. Simple mathematics dictates that the current data management paradigm is simply unsustainable from an economic standpoint. Enterprises are forced to devote more and more of their stagnant IT budgets to scale existing, traditional data management systems, leaving fewer funds to support innovation and value-add projects. 2. Performance. Traditional data management technologies are buckling under the weight of Big Data. Conversations with members of the Wikibon community make clear that as both data volumes and the complexity of analytic workloads increase, relational database management systems and related data management tools are unable to provide the level of performance required to meet demanding business conditions. 3. Agility. In addition to performance, traditional data management approaches require lengthy data preparation and data modeling work, making it virtually impossible for practitioners to adapt both analytical workloads and transactional applications at the pace required to keep up with end-use (workers, partners, customers, etc.) expectations. In years past, practitioners had little option but to stick with these traditional approaches to data management as there were few if any accessible alternatives on the market. But times have changed. Today, practitioners have a plethora of alternatives to choose from. The two most prominent of these have emerged from practitioners themselves (specifically practitioners are Web companies such as Google, Yahoo and Facebook) and the open source communities that have developed around them. The first is Hadoop - the open source Big Data framework for storing, processing and analyzing massive volumes of multi-structured data which has emerged as the de facto foundational technology in the modern data management stack. The second is NoSQL, a style of database that eschews relational structure for a more flexible approach that enables developers to build interactive, scalable data-centric applications incorporating data of virtually any type. As Hadoop and the various flavors of NoSQL have matured so have the commercial entities that are developing products and services around them and the enterprise practitioners that are deploying them. Nearly a full third of enterprise Big Data practitioners, for example, have deployed Hadoop in production environments, according to Wikibon s recent Big Data Analytics Adoption Survey. Practitioners are also keen on leveraging the cloud to support Big Data workloads, with more than 50% using cloud-based Big Data tools and technologies as part of existing projects. But it is still early days for Big Data in the enterprise. Over 40% of Big Data early adopters are still in the evaluation phase and close to 30% still experimenting with pilot projects. This reality is reflected in the current size of the Big Data market and its forecasted growth rate over the next four years. Specifically, total revenue generated by sale of Hadoop distribution software/support subscriptions and related professional services in 2014 is $621 million. The market is expected to nearly triple in size by 2017, however, reaching close to $1.7 billion as many pilot projects blossom into full-blown production deployments. As for the NoSQL market, total revenue for related software, subscription support and professional services reached $461 2 / 12

3 million in 2014 and is forecast to grow at even faster clip than the Hadoop market reaching over $1.6 billion in This growth is being driven in large part by demanding end-users that expect the same level of functionality and simplicity they get from consumer applications such as Google search. Combined, the Hadoop and NoSQL markets will reach over $3.3 billion in 2017 representing a 46% CAGR over the four year period (See Figure 1). Figure 1; Source: Wikibon 2014 What follows in the remainder of this report is more detailed analysis of both the Hadoop and NoSQL markets, including revenue breakdown by vendor, analysis of market drivers and market headwinds, a review of the major developments in each market over the last twelve months and their relative impact on market growth, and expectations for market developments in 2015 and beyond. Hadoop Market Forecast The market for Hadoop distribution software, support subscriptions, cloud services and related professional services topped $620 million in 2014 as measured by vendor revenue. This is up from $387 million 2013, a year-over-year growth rate of 60%. Over the next four years, Wikibon forecasts this market to grow to nearly $1.7 billion, a compound annual growth rate (CAGR) of 40%. This forecast does not include related hardware revenue nor complimentary Hadoop-based tools such as data visualization, data transformation and data integration software. The current Hadoop market is significantly fragmented, with seven vendors generating 64% of total revenue. Comparing these seven vendors is made difficult due to their varying business models and product/services on offer. One vendor (Accenture), for example, derives all of its Hadoop-based revenue through professional services, while another (Amazon) does so exclusively through cloud-based software. Yet another (Hortonworks) offers a Hadoop distribution, but does not charge license or subscription fees for its use, but generates revenue through a support subscription offering along with professional services. With those caveats made clear, below is a vendor-by-vendor revenue breakdown of the top seven Hadoop vendors for the 3 / 12

4 calendar year 2014 (See Figure 2). Figure 2; Source: Wikibon 2014 Looking at market share, Cloudera, one of three Hadoop pure-play vendors, leads the pack at 13%. IBM, whose Hadoop revenue is largely driven by its Global Business Services unit, and Accenture both captured approximately 10% market share. Amazon Web Service followed close behind at 9% market share thanks to its cloud-based ELastic MapReduce offering. MapR and Hortonworks, the other two Hadoop pure-plays, both checked in with 7% share followed by Pivotal, the EMC-VMware spinoff, with 6%. The remaining 36% of the market is divided amongst various professional services firms, independent software vendors (ISVs) and value-added resellers (VARs). Hadoop Market Drivers The Hadoop market in 2014 is being driven by two main factors. The first is the increasing costs associated with processing and storing ever growing data volumes. Enterprises across verticals are being inundated with data from all directions, from both inside and outside corporate data centers. In addition to growth of traditional data sources, new sources of data associated with the Internet of Things and the Industrial Internet are leading to now incremental but exponential overall data growth in 4 / 12

5 many industries. Processing and storing ever-increasing data volumes with traditional enterprise data warehouses and related data integration technology, and their legacy pricing models, is taxing stagnant IT budgets. Data management professionals are forced to allocate more and more of their budgets to expanding expensive software licenses and maintenance contracts, a pricing model that was sustainable if not ideal when data growth was more modest. In the current environment, enterprise practitioners are increasingly coming to the conclusion that a new approach is needed. For many, that new approach involves Hadoop. Based on direct feedback from data management practitioners and the Wikibon community, it is clear that a small but growing percentage of enterprises are baselining their current spend on traditional data warehouse and related technology by moving data and workloads to Hadoop. More to the point, these practitioners are keeping only the most current, or hot, data in traditional EDWs and moving older data to Hadoop. Specifically, according to Wikibon s 2014 Big Data adoption survey, fully 61% of practitioners that have deployed Hadoop have shifted one or more workloads from a legacy data warehouse or mainframe to the open source Big Data platform. The most popular workload being shifted to Hadoop is large-scale data transformations, but business intelligence and reporting workloads are not far behind. Hadoop costs are just a fraction of traditional commercial data warehouse technology as it is based on open source hardware that runs on scale-out commodity hardware, resulting in significant costs savings for budget-taxed IT departments. The second Hadoop driver is the realization by many enterprises that in order to remain competitive in the current economic and business climate they must leverage all the data at their disposal to make smarter data-driven decisions. This is simply not possible with traditional data warehouse technology, which is designed to support structured, relational data. The majority of net-new data, however, is semi-structured and unstructured. This includes machine-generated data from mobile devices and industrial equipment, sensor data associated with the Internet of Things, and text-based data such as the content of social media posts, s, and documents. Hadoop, and the underlying Hadoop Distributed File System, is an environment ideal for storing and processing semistructured and unstructured data. There are also a number of related technologies and tooling at various stages of development to make it easier for developers to build applications that enable business users - not just advanced Data Scientists - to interact with Hadoop-based data in various ways. These include YARN, which enables developers to interact with Hadoop data in myriad new ways beyond MapReduce (including the increasingly popular in-memory Spark framework), and SQL-like databases such as Cloudera s Impala and the latest iteration of Hive (spearheaded by Hortonworks) that provide a hook into business intelligence tools for non-expert end-users. The first driver - cost savings - is largely IT-led, while the second driver - the ability to analyze all data for better decisionmaking - is largely business led. Hadoop Market Headwinds While Hadoop holds much promise, there are a number of barriers holding back the market in The first, and most obvious, is that Hadoop itself is still relatively immature. This despite the immense progress made by both commercial Hadoop vendors and the open source community in improving the usability of Hadoop and beefing up its enterprise-grade features in the areas of security, high availability, and backup and recovery. Before practitioners are willing to use Hadoop to support production-grade applications in which SLAs must be met, they need assurance that it meets the minimum level requirements in these areas. In Wikibon s 2014 adoption survey, concerns around enterprise-grade features were identified as the major roadblocks to expanding deployments to full-scale production. This is important to the market s growth from a vendor-revenue perspective because the majority of Hadoop software and subscription offerings are geared towards supporting production deployments. A related barrier to adoption is the lack of skilled Hadoop practitioners. This includes not just Data Scientists, who are tasked with building predictive models and algorithms, but developers, engineers and operations pros that are tasked with standing up Hadoop deployments and administering them as use cases and users grow over time. As Hadoop becomes easier to deploy and manage, this barrier will recede as less expertise will be required to support deployments. 5 / 12

6 The flipside, of course, is that Hadoop professional services are in high demand precisely because the open source Big Data framework is complex. Early Hadoop adopters across verticals are increasingly turning to professional services firms, both large multi-national consulting firms and small boutique shops, to aid them in fleshing out use cases, architecting systems, deploying the technology and building applications. Over 60% of Hadoop revenue in 2014 was from professional services, with less than 40% for Hadoop software and support subscriptions. It is not surprising, then, that IBM and Accenture round out the top three Hadoop vendors by revenue. Amazon Web Services, meanwhile, quitely grew its Hadoop business significantly in 2014 as more and more early adopters looked to the public cloud to begin experimenting with Big Data application development and related analytics. According to Wikibon s 2014 Big Data adoption survey, fully 58% of practitioners are leveraging the public cloud for at least some part of existing Big Data projects. As with all AWS offerings, Elastic MapReduce significantly lowers the barriers to adoption for Hadoop by eliminating the need on the part of practitioners to procure, deploy and tune hardware. Whether these Hadoop practitioners will continue leveraging AWS when deployments move to production status is an open question. Major Hadoop Market Developments The biggest supply-side question on the minds of vendors and investors today is some variation of: Is it possible to make money in the Hadoop market? More specifically, can anyone build a large and successful Hadoop software (not professional services) company? Wikibon believes the answer is yes, but it is far from a sure thing and clearly not going to be easy. The market got a first-hand look at just how difficult it is to build a Hadoop business in November 2014 when Hortonworks filed an S-1. The company, which was spun out of Yahoo in 2011, reported revenue figures that largely underwhelmed market expectations. Specifically, for the twelve months ending April 30, 2013, Hortonworks generated just under $11 million in revenue. For the nine months ended September 30, 2014, it generated $33 million in revenue. For full year 2014, Wikibon estimates Hortonworks revenue to come in at $45 million. While lower than many previous estimates, Hortonworks overall revenue figures are solid when considering the company is just three and a half years old, that it has only had a product on the market since 2012 and that it is executing a business model unique in the industry. That is, Hortonworks Hadoop distribution, called Hortonworks Data Platform, is made up entirely of open source components and is free for anyone to download and use. The company makes money through a three-level maintenance and support subscription as well as professional services, though Hortonworks executives have clearly stated they intend the latter to make up only a small fraction of their business in the long term. This is an important point because building a large, profitable Hadoop professional services company from scratch would be an extremely difficult undertaking. So what is more concerning about the Hortonworks S1 than overall revenue is the percentage of revenue coming from professional services and the negative margin associated with this revenue. For the nine months ended September 30, 2014, 43% of revenue was from professional services with a -35% operating margin. During that time the company generated $14.2 million in revenue from professional services at a cost of $19.1 million resulting in a net loss of nearly $5 million. These numbers highlight just how expensive it is to provide professional support services for complex Hadoop deployments and reinforce the notion that Hortonworks must transition more of its revenue to repeatable software and maintenance support subscriptions. The problem for Hortonworks, indeed for all Hadoop pure-play vendors, is that through 2014 only a fraction of Hadoop practitioners are paying for the software. According to findings from Wikibon s 2014 Big Data adoption survey, just 25% of Hadoop practitioners are paying customers of one of the commercial Hadoop distribution vendors. 75% are using Hadoop software without paying a nickel to do so. Clearly the Hadoop market is still in its early days, but Wikibon remains bullish on the long-term prospects of Hadoop vendors generally and Hortonworks specifically. Hortonworks strategy has always been to position itself to capitalize when Hadoop crosses the chasm from early adopters to mainstream adopters. Hortonworks is still positioned to do that and Wikibon expects revenue to ramp up considerably over the coming twelve to eighteen months as production deployments expand in the financial services, retail and telecommunication sectors. Another notable event of 2014 was the massive investment by Intel in Cloudera (See Figure 3). Specifically, Intel invested $740 million for an 18% equity stake in Cloudera, the first Hadoop distribution vendor to market in The investment 6 / 12

7 pushed total capital raised by the Palo Alto company over the $1 billion mark, an outrageous sum even in the current venture capital climate. As part of the investment, Intel announced it was abandoning its own Hadoop distribution and integrating its IP, which focused largely on chip-level security for Hadoop, into Cloudera s platform. Cloudera also took over Intel s Hadoop clients, most in Asia, and gained the benefit of Intel as a strategic reseller. Figure 3; Source: Wikibon 2014 The Intel-Cloudera deal wasn t the only notable venture capital news of In June, Google Capital announced a $110 million in MapR, bringing total capital raised by MapR to $174 million. In July, HP announced a $50 million equity investment in Hortonworks for a 5% stake in the company, bringing total capital raised by Hortonworks to nearly $250 million. This leaves all three Hadoop pure-play vendors well capitalized heading into Hadoop Market in 2015 and Beyond Wikibon believes the market for Hadoop software and support subscriptions is poised for significant growth over the next one to five years. As early adopters transition Hadoop from PoC to production deployments and eventually to full-scale platform status, they will increasingly turn to commercial vendors for help troubleshooting, managing, scaling and securing these platforms. Indeed, Hortonworks maintenance and support subscription, as well as Cloudera s enterprise data hub offering and MapR s various platform offerings are aimed at production-grade deployments. Wikibon forecasts the Hadoop market for software and support subscriptions as well as professional services to grow to $909 million in 2015 from $621 million in That represents year-over-year growth of 46% at a time when enterprise software overall is growing at low single digits. Looking ahead, the Hadoop market will cross the $1 billion mark in 2016 reaching nearly $1.25 billion, then topping $1.6 billion in NoSQL Market Forecast The market for NoSQL database software, support subscriptions, cloud services and related professional services reached $411 million in 2014 as measured by vendor revenue. This is up from $330 million 2013, a year-over-year growth rate of 25%. Over the next four years, Wikibon forecasts this market to grow to over $1.5 billion, a compound annual growth rate (CAGR) of 7 / 12

8 55%. This forecast does not include related hardware revenue nor complimentary NoSQL-related tools such as data integration software. The current NoSQ market is highly fragmented. As stated in the Executive Summary of this report, there are numerous flavors of NoSQL database, each with their own strengths and weaknesses. These NoSQL databases include key-value stores, document databases, column stores and graph databases. Most of these databases started life inside practitioner organizations, such as Facebook and even the National Security Agency, where they were developed to solve specific problems that traditional relational databases technology could not. The result is that no one NoSQL database has emerged as a general purpose database to support most use cases, hence the fragmented nature of the market. This is in contrast to Hadoop, which has emerged as the de facto Big Data storage and processing platform for both start-ups and enterprises. A number of start-ups have emerged that are commercializing the various NoSQL databases on the market. In most cases, a single vendor has come to dominate the particular NoSQL database in question. For example, DataStax is the dominant vendor commercializing Apache Cassandra, a NoSQL column store database originally developed at Facebook. MongoDB, formerly 10gen, is the main vendor behind the NoSQL database of the same name, which developed the database and open sourced it. Most of the NoSQL vendors adhere to the open source-plus business model: they make a basic version of their version of the database available for free and monetize and enterprise version often with associated proprietary management software and maintenance support. Most also offer professional services. From a competitive perspective, some NoSQL vendors compete with one another while others are more complementary. In terms of marketshare, MarkLogic, with its eponymous document database, leads the market with 25% share (See Figure 4). Amazon, with its cloud-based DynamoDB NoSQL database, follows close behind with 21% marketshare. MongoDB and DataStax take the third and fourth positions with 11% and 10% share respectively. Couchbase has 4% share, followed by Basho, Aerospike and Neo Technology, which each have 3% share. The remaining 18% of the market is divided up by various professional services firms, ISVs and VARs. 8 / 12

9 Figure 4; Source: Wikibon 2014 NoSQL Market Drivers The NoSQL market in 2014 is being driven by the desire of enterprise developers to build and support real-time, interactive data-driven applications that incorporate not just structured data but all manner of semi-structured and unstructured data as well. These applications take many forms, but most involve delivering data and information to end-users in the form of interactive web and mobile applications. NoSQL databases provide an ideal platform for these types of applications because they do not require developers to define the data schema or format in advance. Most NoSQL databases also run on commodity hardware, meaning they easily scale linearly by simply adding nodes to the cluster. Both these attributes are radically different than the approach taken by most proprietary, relational databases and give application developers significantly more flexibility as to the types and size of applications they can build and support. In addition to end-user applications, a number of NoSQL early adopters are also using the technology to support applications that intelligently automate business processes. The adtech use case is likely the most familiar to most consumers, but these real- 9 / 12

10 time, in-line analytic applications are increasingly finding their way into the financial services and industrial sectors. Many emerging applications designed to leverage sensor and machine-generated data created as part of the Internet of Things are also built on NoSQL foundations. These types of applications enable enterprises to operationalize the insights and predictive models that are uncovered and built with the help of Hadoop and other analytic environments. The two, Hadoop and NoSQL, or Deep Data Analytics and In-Line Analytics, are two sides of the same coin. In addition to the new applications NoSQL enables, and like Hadoop, NoSQL approaches are also significantly less costly than those offered by proprietary RDBMS offerings. This is due in part to the open source nature of most NoSQL database software as well as the inexpensive off-the-shelf hardware that most run on. As a result, a small but growing percentage of enterprise developers replacing the underlying RDBMS supporting existing applications with one or another NoSQL database. NoSQL Market Headwinds The NoSQL market faces a number of challenges to growth from a vendor revenue perspective. The first, and again like Hadoop, is that most NoSQL database technology is relatively immature, especially when compared to relational database technology that has been around for over thirty years. While NoSQL database technology provides significantly more flexibility in terms of data type, most lack the same level of enterprise-grade security, data consistency, resiliency and backup capabilities as their relational database counterparts. According to Wikibon s 2014 Big Data adoption survey, over 25% of Big Data early adopters have deployed or are evaluating NoSQL database technology. The biggest barriers to successful NoSQL projects, according to these practitioners, are concerns about maintaining application performance as both data volumes and concurrent users increase; concerns about data backup and recovery; and data security and privacy concerns. Data consistency is also an area of concern for NoSQL practitioners and is particularly important for transactional applications. Major NoSQL Market Developments 2014 was an eventful year for the NoSQL market. The various market participants continued to jostle for position, with several securing large funding rounds. The biggest recipient in 2014 was DataStax, who secured $106 million in Series E funding in September (See Figure 5). Earlier in the year, Couchbase raised $60 million, also in Series E funding. From a market consolidation standpoint, IBM acquired Cloudant, which competes with Couchbase in commercializing Apache Couchbase but specializes in cloud-based deployments of the NoSQL database. 10 / 12

11 Figure 5; Source: Wikibon 2014 The biggest news of the year was a change in leadership at one of the leading NoSQL vendors. Max Schireson stepped down in August as CEO of MongoDB. In a well-publicised blog post, Schireson cited his desire to spend more time with family as his reason for stepping aside from the best job I ever had. Venture capitalist and former BMC executive Dev Ittycheria was named to the top spot to replace Schireson. While Wikibon takes Schireson at his word regarding his departure from MongoDB, the reality is that the company - as well as many of its NoSQL competitors - are struggling to develop and implement sustainable business models. Commercializing open source software is challenging, as we ve seen in the Hadoop space, especially when its popularity is driven by an active and passionate community of committers and practitioners. This is certainly the case with MongoDB, which is extremely popular with developers. However, MongoDB has not capitalized that popularity into significant profits, as Schireson himself alluded to in his keynote address at MongoDB World in June, two months prior to his recognition. In short, NoSQL vendors like MongoDB need to strike a balance between increasing price per unit and establishing a realistic path to profitability while simultaneously maintaining the support of the open source community, without which there would be no NoSQL market in the first place. 11 / 12

12 Powered by TCPDF ( Hadoop-NoSQL Software and Services Market Forecast, NoSQL Market in 2015 and Beyond Over the long term, Wikibon believes the NoSQL market will enjoy significant growth, eventually outpacing the Hadoop market. This is due to the potential value that NoSQL databases can deliver via operationalizing Big Data insights. While Hadoop and related analytics technologies allow enterprise practitioners to discover important insights, NoSQL translates them into automated actions that, in many cases, directly impact the bottom-line. In 2015, Wikibon forecasts the NoSQL market for software and support subscriptions as well as professional services to grow to $643 million from $411 million in That represents year-over-year growth of 56%. The NoSQL market will top $1.5 billion in 2017, leaving it just slightly smaller than the Hadoop market. Wikibon expects the NoSQL market size to exceed the Hadoop market by Conclusion The Hadoop and NoSQL markets are in the midst of significant growth and tremendous innovation. Enterprise practitioners are frustrated with existing approaches to both data warehousing and RDBMS and are actively looking for more cost-effective, scalable, flexible and agile approaches to data management. The ultimate driver of these markets, however, is not cost savings but the need on the part of all enterprises to make better use of their data assets to remain competitive in the fast moving business environment. Enterprise practitioners that have yet to do so should begin evaluating Big Data use cases and developing realistic plans to adopt Big Data technology to support executing against these use cases. LEGAL SiliconANGLE Media, Inc. All rights reserved. This document and its contents is restricted for the private use of Wikibon Premium Members. External use without written permission is forbidden. 12 / 12

Wikibon Big Data Analytics Adoption Survey, 2014-2015 Frequency Analysis

Wikibon Big Data Analytics Adoption Survey, 2014-2015 Frequency Analysis Wikibon.com - http://wikibon.com Wikibon Big Data Analytics Adoption Survey, 2014-2015 Frequency Analysis by Jeff Kelly - 1 July 2014 http://wikibon.com/wikibon-big-data-analytics-adoption-survey-2014-2015-frequency-analysis/

More information

Big Data Market Size and Vendor Revenues

Big Data Market Size and Vendor Revenues Analysis from The Wikibon Project February 2012 Big Data Market Size and Vendor Revenues Jeff Kelly, David Vellante, David Floyer A Wikibon Reprint The Big Data market is on the verge of a rapid growth

More information

Big Data Vendor Revenue and Market Forecast, 2011-2026

Big Data Vendor Revenue and Market Forecast, 2011-2026 Wikibon.com - http://wikibon.com Big Data Vendor Revenue and Market Forecast, 2011-2026 by Jeff Kelly - 31 March 2015 http://wikibon.com/big-data-vendor-revenue-and-market-forecast-2011-2026/ 1 / 13 Co-Authored

More information

How To Handle Big Data With A Data Scientist

How To Handle Big Data With A Data Scientist III Big Data Technologies Today, new technologies make it possible to realize value from Big Data. Big data technologies can replace highly customized, expensive legacy systems with a standard solution

More information

Big Data Vendor Revenue and Market Forecast 2012-2017

Big Data Vendor Revenue and Market Forecast 2012-2017 Big Data Vendor and Market Forecast 2012-2017 Contributing authors: Jeff Kelly, David Floyer, Dave Vellante, Stu Miniman Original publication date: February 19, 2013 The hype surrounding Big Data, which

More information

INTRODUCTION TO CASSANDRA

INTRODUCTION TO CASSANDRA INTRODUCTION TO CASSANDRA This ebook provides a high level overview of Cassandra and describes some of its key strengths and applications. WHAT IS CASSANDRA? Apache Cassandra is a high performance, open

More information

Create and Drive Big Data Success Don t Get Left Behind

Create and Drive Big Data Success Don t Get Left Behind Create and Drive Big Data Success Don t Get Left Behind The performance boost from MapR not only means we have lower hardware requirements, but also enables us to deliver faster analytics for our users.

More information

How Transactional Analytics is Changing the Future of Business A look at the options, use cases, and anti-patterns

How Transactional Analytics is Changing the Future of Business A look at the options, use cases, and anti-patterns How Transactional Analytics is Changing the Future of Business A look at the options, use cases, and anti-patterns Table of Contents Abstract... 3 Introduction... 3 Definition... 3 The Expanding Digitization

More information

HADOOP SOLUTION USING EMC ISILON AND CLOUDERA ENTERPRISE Efficient, Flexible In-Place Hadoop Analytics

HADOOP SOLUTION USING EMC ISILON AND CLOUDERA ENTERPRISE Efficient, Flexible In-Place Hadoop Analytics HADOOP SOLUTION USING EMC ISILON AND CLOUDERA ENTERPRISE Efficient, Flexible In-Place Hadoop Analytics ESSENTIALS EMC ISILON Use the industry's first and only scale-out NAS solution with native Hadoop

More information

Big Data reconsidered Separating Hype from Reality for Hosting and Cloud Providers

Big Data reconsidered Separating Hype from Reality for Hosting and Cloud Providers Big Data reconsidered Separating Hype from Reality for Hosting and Cloud Providers Matthew Aslett Research Director, Data Management and Analytics, 451 Research Matthew Aslett Research Director, Data Management

More information

Big Data Adoption Progress Across Industries

Big Data Adoption Progress Across Industries Wikibon.com - http://wikibon.com Big Data Adoption Progress Across Industries by Ralph Finos - 29 October 2015 http://wikibon.com/big-data-adoption-progress-across-industries/ 1 / 20 Premise The rate of

More information

Hadoop for Enterprises:

Hadoop for Enterprises: Hadoop for Enterprises: Overcoming the Major Challenges Introduction to Big Data Big Data are information assets that are high volume, velocity, and variety. Big Data demands cost-effective, innovative

More information

Tap into Hadoop and Other No SQL Sources

Tap into Hadoop and Other No SQL Sources Tap into Hadoop and Other No SQL Sources Presented by: Trishla Maru What is Big Data really? The Three Vs of Big Data According to Gartner Volume Volume Orders of magnitude bigger than conventional data

More information

TOP 8 TRENDS FOR 2016 BIG DATA

TOP 8 TRENDS FOR 2016 BIG DATA The year 2015 was an important one in the world of big data. What used to be hype became the norm as more businesses realized that data, in all forms and sizes, is critical to making the best possible

More information

The Modern Online Application for the Internet Economy: 5 Key Requirements that Ensure Success

The Modern Online Application for the Internet Economy: 5 Key Requirements that Ensure Success The Modern Online Application for the Internet Economy: 5 Key Requirements that Ensure Success 1 Table of Contents Abstract... 3 Introduction... 3 Requirement #1 Smarter Customer Interactions... 4 Requirement

More information

Ubuntu and Hadoop: the perfect match

Ubuntu and Hadoop: the perfect match WHITE PAPER Ubuntu and Hadoop: the perfect match February 2012 Copyright Canonical 2012 www.canonical.com Executive introduction In many fields of IT, there are always stand-out technologies. This is definitely

More information

Delivering Real-World Total Cost of Ownership and Operational Benefits

Delivering Real-World Total Cost of Ownership and Operational Benefits Delivering Real-World Total Cost of Ownership and Operational Benefits Treasure Data - Delivering Real-World Total Cost of Ownership and Operational Benefits 1 Background Big Data is traditionally thought

More information

Big Data and Telecom Analytics Market: Business Case, Market Analysis & Forecasts 2014-2019

Big Data and Telecom Analytics Market: Business Case, Market Analysis & Forecasts 2014-2019 MARKET RESEARCH STORE Big Data and Telecom Analytics Market: Business Case, Market Analysis & Forecasts 2014-2019 Market Research Store included latest deep and professional market research report on Big

More information

Wikibon Big Data Analytics Survey: Barriers to Adoption by Role

Wikibon Big Data Analytics Survey: Barriers to Adoption by Role Wikibon.com - http://wikibon.com Wikibon Big Data Analytics Survey: Barriers to Adoption by Role by George Gilbert - 12 August 2015 http://wikibon.com/wikibon-big-data-analytics-survey-barriers-to-adoption-by-role/

More information

Apache Hadoop Patterns of Use

Apache Hadoop Patterns of Use Community Driven Apache Hadoop Apache Hadoop Patterns of Use April 2013 2013 Hortonworks Inc. http://www.hortonworks.com Big Data: Apache Hadoop Use Distilled There certainly is no shortage of hype when

More information

Wikibon Big Data Analytics Survey: Barriers to Adoption by Deployment Status

Wikibon Big Data Analytics Survey: Barriers to Adoption by Deployment Status Wikibon.com - http://wikibon.com Wikibon Big Data Analytics Survey: Barriers to Adoption by Deployment Status by Ralph Finos - 12 August 2015 http://wikibon.com/barriers-to-big-data-analytics-and-deployment-status/

More information

How To Understand The Business Case For Big Data

How To Understand The Business Case For Big Data Brochure More information from http://www.researchandmarkets.com/reports/2643647/ Big Data and Telecom Analytics Market: Business Case, Market Analysis & Forecasts 2014-2019 Description: Big Data refers

More information

Mind Commerce. http://www.marketresearch.com/mind Commerce Publishing v3122/ Publisher Sample

Mind Commerce. http://www.marketresearch.com/mind Commerce Publishing v3122/ Publisher Sample Mind Commerce http://www.marketresearch.com/mind Commerce Publishing v3122/ Publisher Sample Phone: 800.298.5699 (US) or +1.240.747.3093 or +1.240.747.3093 (Int'l) Hours: Monday - Thursday: 5:30am - 6:30pm

More information

Analytics March 2015 White paper. Why NoSQL? Your database options in the new non-relational world

Analytics March 2015 White paper. Why NoSQL? Your database options in the new non-relational world Analytics March 2015 White paper Why NoSQL? Your database options in the new non-relational world 2 Why NoSQL? Contents 2 New types of apps are generating new types of data 2 A brief history of NoSQL 3

More information

VIEWPOINT. High Performance Analytics. Industry Context and Trends

VIEWPOINT. High Performance Analytics. Industry Context and Trends VIEWPOINT High Performance Analytics Industry Context and Trends In the digital age of social media and connected devices, enterprises have a plethora of data that they can mine, to discover hidden correlations

More information

HDP Enabling the Modern Data Architecture

HDP Enabling the Modern Data Architecture HDP Enabling the Modern Data Architecture Herb Cunitz President, Hortonworks Page 1 Hortonworks enables adoption of Apache Hadoop through HDP (Hortonworks Data Platform) Founded in 2011 Original 24 architects,

More information

The Future of Data Management

The Future of Data Management The Future of Data Management with Hadoop and the Enterprise Data Hub Amr Awadallah (@awadallah) Cofounder and CTO Cloudera Snapshot Founded 2008, by former employees of Employees Today ~ 800 World Class

More information

The Inside Scoop on Hadoop

The Inside Scoop on Hadoop The Inside Scoop on Hadoop Orion Gebremedhin National Solutions Director BI & Big Data, Neudesic LLC. VTSP Microsoft Corp. Orion.Gebremedhin@Neudesic.COM B-orgebr@Microsoft.com @OrionGM The Inside Scoop

More information

Forecast of Big Data Trends. Assoc. Prof. Dr. Thanachart Numnonda Executive Director IMC Institute 3 September 2014

Forecast of Big Data Trends. Assoc. Prof. Dr. Thanachart Numnonda Executive Director IMC Institute 3 September 2014 Forecast of Big Data Trends Assoc. Prof. Dr. Thanachart Numnonda Executive Director IMC Institute 3 September 2014 Big Data transforms Business 2 Data created every minute Source http://mashable.com/2012/06/22/data-created-every-minute/

More information

CA Technologies Big Data Infrastructure Management Unified Management and Visibility of Big Data

CA Technologies Big Data Infrastructure Management Unified Management and Visibility of Big Data Research Report CA Technologies Big Data Infrastructure Management Executive Summary CA Technologies recently exhibited new technology innovations, marking its entry into the Big Data marketplace with

More information

Big Data Success Step 1: Get the Technology Right

Big Data Success Step 1: Get the Technology Right Big Data Success Step 1: Get the Technology Right TOM MATIJEVIC Director, Business Development ANDY MCNALIS Director, Data Management & Integration MetaScale is a subsidiary of Sears Holdings Corporation

More information

The Big Data Manifesto

The Big Data Manifesto Wikibon.com - http://wikibon.com The Big Data Manifesto by Jeff Kelly - 12 January 2012 http://wikibon.com/big-data-manifesto/ 1 / 11 A Big Data Manifesto from the Wikibon Community Providing effective

More information

Big Data Technologies Compared June 2014

Big Data Technologies Compared June 2014 Big Data Technologies Compared June 2014 Agenda What is Big Data Big Data Technology Comparison Summary Other Big Data Technologies Questions 2 What is Big Data by Example The SKA Telescope is a new development

More information

Big Data Defined Introducing DataStack 3.0

Big Data Defined Introducing DataStack 3.0 Big Data Big Data Defined Introducing DataStack 3.0 Inside: Executive Summary... 1 Introduction... 2 Emergence of DataStack 3.0... 3 DataStack 1.0 to 2.0... 4 DataStack 2.0 Refined for Large Data & Analytics...

More information

How Big Is Big Data Adoption? Survey Results. Survey Results... 4. Big Data Company Strategy... 6

How Big Is Big Data Adoption? Survey Results. Survey Results... 4. Big Data Company Strategy... 6 Survey Results Table of Contents Survey Results... 4 Big Data Company Strategy... 6 Big Data Business Drivers and Benefits Received... 8 Big Data Integration... 10 Big Data Implementation Challenges...

More information

TABLE OF CONTENTS 1 Chapter 1: Introduction 2 Chapter 2: Big Data Technology & Business Case 3 Chapter 3: Key Investment Sectors for Big Data

TABLE OF CONTENTS 1 Chapter 1: Introduction 2 Chapter 2: Big Data Technology & Business Case 3 Chapter 3: Key Investment Sectors for Big Data TABLE OF CONTENTS 1 Chapter 1: Introduction 1.1 Executive Summary 1.2 Topics Covered 1.3 Key Findings 1.4 Target Audience 1.5 Companies Mentioned 2 Chapter 2: Big Data Technology & Business Case 2.1 Defining

More information

Build Your Competitive Edge in Big Data with Cisco. Rick Speyer Senior Global Marketing Manager Big Data Cisco Systems 6/25/2015

Build Your Competitive Edge in Big Data with Cisco. Rick Speyer Senior Global Marketing Manager Big Data Cisco Systems 6/25/2015 Build Your Competitive Edge in Big Data with Cisco Rick Speyer Senior Global Marketing Manager Big Data Cisco Systems 6/25/2015 Big Data Trends Increasingly Everything will be Connected to Everything Massive

More information

Datenverwaltung im Wandel - Building an Enterprise Data Hub with

Datenverwaltung im Wandel - Building an Enterprise Data Hub with Datenverwaltung im Wandel - Building an Enterprise Data Hub with Cloudera Bernard Doering Regional Director, Central EMEA, Cloudera Cloudera Your Hadoop Experts Founded 2008, by former employees of Employees

More information

Why NoSQL? Your database options in the new non- relational world. 2015 IBM Cloudant 1

Why NoSQL? Your database options in the new non- relational world. 2015 IBM Cloudant 1 Why NoSQL? Your database options in the new non- relational world 2015 IBM Cloudant 1 Table of Contents New types of apps are generating new types of data... 3 A brief history on NoSQL... 3 NoSQL s roots

More information

HDP Hadoop From concept to deployment.

HDP Hadoop From concept to deployment. HDP Hadoop From concept to deployment. Ankur Gupta Senior Solutions Engineer Rackspace: Page 41 27 th Jan 2015 Where are you in your Hadoop Journey? A. Researching our options B. Currently evaluating some

More information

Architecting for Big Data Analytics and Beyond: A New Framework for Business Intelligence and Data Warehousing

Architecting for Big Data Analytics and Beyond: A New Framework for Business Intelligence and Data Warehousing Architecting for Big Data Analytics and Beyond: A New Framework for Business Intelligence and Data Warehousing Wayne W. Eckerson Director of Research, TechTarget Founder, BI Leadership Forum Business Analytics

More information

Increase Agility and Reduce Costs with a Logical Data Warehouse. February 2014

Increase Agility and Reduce Costs with a Logical Data Warehouse. February 2014 Increase Agility and Reduce Costs with a Logical Data Warehouse February 2014 Table of Contents Summary... 3 Data Virtualization & the Logical Data Warehouse... 4 What is a Logical Data Warehouse?... 4

More information

Wikibon Big Data Analytics Survey: Enterprises Report Enthusiasm Amid Complexity

Wikibon Big Data Analytics Survey: Enterprises Report Enthusiasm Amid Complexity Wikibon.com - http://wikibon.com Wikibon Big Data Analytics Survey: Enterprises Report Enthusiasm Amid Complexity by George Gilbert - 12 October 2015 http://wikibon.com/wikibon-big-data-analytics-survey-fall-2015/

More information

Global Big Data Enabled Market 2015-2019

Global Big Data Enabled Market 2015-2019 Brochure More information from http://www.researchandmarkets.com/reports/3498655/ Global Big Data Enabled Market 2015-2019 Description: Global Big Data Enabled Market 2015-2019 Covering: Market forecast

More information

Big Data: Beyond the Hype

Big Data: Beyond the Hype Big Data: Beyond the Hype Why Big Data Matters to You WHITE PAPER Big Data: Beyond the Hype Why Big Data Matters to You By DataStax Corporation October 2011 Table of Contents Introduction...4 Big Data

More information

Building Your Big Data Team

Building Your Big Data Team Building Your Big Data Team With all the buzz around Big Data, many companies have decided they need some sort of Big Data initiative in place to stay current with modern data management requirements.

More information

Microsoft Big Data. Solution Brief

Microsoft Big Data. Solution Brief Microsoft Big Data Solution Brief Contents Introduction... 2 The Microsoft Big Data Solution... 3 Key Benefits... 3 Immersive Insight, Wherever You Are... 3 Connecting with the World s Data... 3 Any Data,

More information

TECHNOLOGY TRANSFER PRESENTS MIKE FERGUSON BIG DATA MULTI-PLATFORM JUNE 25-27, 2014 RESIDENZA DI RIPETTA - VIA DI RIPETTA, 231 ROME (ITALY)

TECHNOLOGY TRANSFER PRESENTS MIKE FERGUSON BIG DATA MULTI-PLATFORM JUNE 25-27, 2014 RESIDENZA DI RIPETTA - VIA DI RIPETTA, 231 ROME (ITALY) TECHNOLOGY TRANSFER PRESENTS MIKE FERGUSON BIG DATA MULTI-PLATFORM ANALYTICS JUNE 25-27, 2014 RESIDENZA DI RIPETTA - VIA DI RIPETTA, 231 ROME (ITALY) info@technologytransfer.it www.technologytransfer.it

More information

Big Data must become a first class citizen in the enterprise

Big Data must become a first class citizen in the enterprise Big Data must become a first class citizen in the enterprise An Ovum white paper for Cloudera Publication Date: 14 January 2014 Author: Tony Baer SUMMARY Catalyst Ovum view Big Data analytics have caught

More information

Driving Growth in Insurance With a Big Data Architecture

Driving Growth in Insurance With a Big Data Architecture Driving Growth in Insurance With a Big Data Architecture The SAS and Cloudera Advantage Version: 103 Table of Contents Overview 3 Current Data Challenges for Insurers 3 Unlocking the Power of Big Data

More information

Big Data: Are You Ready? Kevin Lancaster

Big Data: Are You Ready? Kevin Lancaster Big Data: Are You Ready? Kevin Lancaster Director, Engineered Systems Oracle Europe, Middle East & Africa 1 A Data Explosion... Traditional Data Sources Billing engines Custom developed New, Non-Traditional

More information

Global Big Data Market: Trends & Opportunities (2014-2019) June 2015

Global Big Data Market: Trends & Opportunities (2014-2019) June 2015 Global Big Data Market: Trends & Opportunities (2014-2019) June 2015 Scope of the Report The report titled Global Big Data Market: Trends and Opportunities (2014-2019), analyzes the potential opportunities

More information

WHITEPAPER. A Technical Perspective on the Talena Data Availability Management Solution

WHITEPAPER. A Technical Perspective on the Talena Data Availability Management Solution WHITEPAPER A Technical Perspective on the Talena Data Availability Management Solution BIG DATA TECHNOLOGY LANDSCAPE Over the past decade, the emergence of social media, mobile, and cloud technologies

More information

PDF PREVIEW EMERGING TECHNOLOGIES. Applying Technologies for Social Media Data Analysis

PDF PREVIEW EMERGING TECHNOLOGIES. Applying Technologies for Social Media Data Analysis VOLUME 34 BEST PRACTICES IN BUSINESS INTELLIGENCE AND DATA WAREHOUSING FROM LEADING SOLUTION PROVIDERS AND EXPERTS PDF PREVIEW IN EMERGING TECHNOLOGIES POWERFUL CASE STUDIES AND LESSONS LEARNED FOCUSING

More information

Enabling Big Data with Cloud. Go faster Reduce risk Scale as you grow Avoid mistakes

Enabling Big Data with Cloud. Go faster Reduce risk Scale as you grow Avoid mistakes Enabling Big Data with Cloud Go faster Reduce risk Scale as you grow Avoid mistakes Dr. Phil Shelley Why Cloud and Big Data? Complexity Speed Cost Skills Support Technology Analytics 2.0 Industry Trends

More information

Annex: Concept Note. Big Data for Policy, Development and Official Statistics New York, 22 February 2013

Annex: Concept Note. Big Data for Policy, Development and Official Statistics New York, 22 February 2013 Annex: Concept Note Friday Seminar on Emerging Issues Big Data for Policy, Development and Official Statistics New York, 22 February 2013 How is Big Data different from just very large databases? 1 Traditionally,

More information

Ubuntu: helping drive business insight from Big Data

Ubuntu: helping drive business insight from Big Data WHITE PAPER Ubuntu: helping drive business insight from Big Data February 2012 Copyright Canonical 2012 www.canonical.com Executive introduction For years, web giants such as Facebook, Google and ebay

More information

ATA DRIVEN GLOBAL VISION CLOUD PLATFORM STRATEG N POWERFUL RELEVANT PERFORMANCE SOLUTION CLO IRTUAL BIG DATA SOLUTION ROI FLEXIBLE DATA DRIVEN V

ATA DRIVEN GLOBAL VISION CLOUD PLATFORM STRATEG N POWERFUL RELEVANT PERFORMANCE SOLUTION CLO IRTUAL BIG DATA SOLUTION ROI FLEXIBLE DATA DRIVEN V ATA DRIVEN GLOBAL VISION CLOUD PLATFORM STRATEG N POWERFUL RELEVANT PERFORMANCE SOLUTION CLO IRTUAL BIG DATA SOLUTION ROI FLEXIBLE DATA DRIVEN V WHITE PAPER Build the Future of Big Data Today By Hitachi

More information

Why Big Data in the Cloud?

Why Big Data in the Cloud? Have 40 Why Big Data in the Cloud? Colin White, BI Research January 2014 Sponsored by Treasure Data TABLE OF CONTENTS Introduction The Importance of Big Data The Role of Cloud Computing Using Big Data

More information

TOTAL DATA WAREHOUSING: 2013-2018

TOTAL DATA WAREHOUSING: 2013-2018 TOTAL DATA WAREHOUSING: 2013-2018 Analytic Database and Hadoop Market Sizing and Forecasts This report examines the marketplace for Total Data Warehousing including competing players, revenue generation

More information

Customized Report- Big Data

Customized Report- Big Data GINeVRA Digital Research Hub Customized Report- Big Data 1 2014. All Rights Reserved. Agenda Context Challenges and opportunities Solutions Market Case studies Recommendations 2 2014. All Rights Reserved.

More information

Big Data and Data Science: Behind the Buzz Words

Big Data and Data Science: Behind the Buzz Words Big Data and Data Science: Behind the Buzz Words Peggy Brinkmann, FCAS, MAAA Actuary Milliman, Inc. April 1, 2014 Contents Big data: from hype to value Deconstructing data science Managing big data Analyzing

More information

White Paper: Big Data and the hype around IoT

White Paper: Big Data and the hype around IoT 1 White Paper: Big Data and the hype around IoT Author: Alton Harewood 21 Aug 2014 (first published on LinkedIn) If I knew today what I will know tomorrow, how would my life change? For some time the idea

More information

Big Data and Hadoop for the Executive A Reference Guide

Big Data and Hadoop for the Executive A Reference Guide Big Data and Hadoop for the Executive A Reference Guide Overview The amount of information being collected by companies today is incredible. Wal- Mart has 460 terabytes of data, which, according to the

More information

Synergic Partners: Spanish big-data pioneer

Synergic Partners: Spanish big-data pioneer Synergic Partners: Spanish big-data pioneer Analyst: Katy Ring 20 Mar, 2015 Synergic Partners offers a services portfolio around data engineering, big data and data science. The company focuses on business

More information

BIG DATA & DATA SCIENCE

BIG DATA & DATA SCIENCE BIG DATA & DATA SCIENCE ACADEMY PROGRAMS IN-COMPANY TRAINING PORTFOLIO 2 TRAINING PORTFOLIO 2016 Synergic Academy Solutions BIG DATA FOR LEADING BUSINESS Big data promises a significant shift in the way

More information

Big Data Database Revenue and Market Forecast, 2012-2017

Big Data Database Revenue and Market Forecast, 2012-2017 Wikibon.com - http://wikibon.com Big Data Database Revenue and Market Forecast, 2012-2017 by David Floyer - 13 February 2013 http://wikibon.com/big-data-database-revenue-and-market-forecast-2012-2017/

More information

Native Connectivity to Big Data Sources in MSTR 10

Native Connectivity to Big Data Sources in MSTR 10 Native Connectivity to Big Data Sources in MSTR 10 Bring All Relevant Data to Decision Makers Support for More Big Data Sources Optimized Access to Your Entire Big Data Ecosystem as If It Were a Single

More information

Big Data for the Rest of Us Technical White Paper

Big Data for the Rest of Us Technical White Paper Big Data for the Rest of Us Technical White Paper Treasure Data - Big Data for the Rest of Us 1 Introduction The importance of data warehousing and analytics has increased as companies seek to gain competitive

More information

BIG DATA & ANALYTICS. Transforming the business and driving revenue through big data and analytics

BIG DATA & ANALYTICS. Transforming the business and driving revenue through big data and analytics BIG DATA & ANALYTICS Transforming the business and driving revenue through big data and analytics Collection, storage and extraction of business value from data generated from a variety of sources are

More information

Optimizing Data Centers for Big Infrastructure Applications

Optimizing Data Centers for Big Infrastructure Applications white paper Optimizing Data Centers for Big Infrastructure Applications Contents Whether you need to analyze large data sets or deploy a cloud, building big infrastructure is a big job. This paper discusses

More information

Next-Generation Cloud Analytics with Amazon Redshift

Next-Generation Cloud Analytics with Amazon Redshift Next-Generation Cloud Analytics with Amazon Redshift What s inside Introduction Why Amazon Redshift is Great for Analytics Cloud Data Warehousing Strategies for Relational Databases Analyzing Fast, Transactional

More information

WHITE PAPER. Four Key Pillars To A Big Data Management Solution

WHITE PAPER. Four Key Pillars To A Big Data Management Solution WHITE PAPER Four Key Pillars To A Big Data Management Solution EXECUTIVE SUMMARY... 4 1. Big Data: a Big Term... 4 EVOLVING BIG DATA USE CASES... 7 Recommendation Engines... 7 Marketing Campaign Analysis...

More information

COULD VS. SHOULD: BALANCING BIG DATA AND ANALYTICS TECHNOLOGY WITH PRACTICAL OUTCOMES

COULD VS. SHOULD: BALANCING BIG DATA AND ANALYTICS TECHNOLOGY WITH PRACTICAL OUTCOMES COULD VS. SHOULD: BALANCING BIG DATA AND ANALYTICS TECHNOLOGY The business world is abuzz with the potential of data. In fact, most businesses have so much data that it is difficult for them to process

More information

Proact whitepaper on Big Data

Proact whitepaper on Big Data Proact whitepaper on Big Data Summary Big Data is not a definite term. Even if it sounds like just another buzz word, it manifests some interesting opportunities for organisations with the skill, resources

More information

How the emergence of OpenFlow and SDN will change the networking landscape

How the emergence of OpenFlow and SDN will change the networking landscape How the emergence of OpenFlow and SDN will change the networking landscape Software-defined networking (SDN) powered by the OpenFlow protocol has the potential to be an important and necessary game-changer

More information

THE JOURNEY TO A DATA LAKE

THE JOURNEY TO A DATA LAKE THE JOURNEY TO A DATA LAKE 1 THE JOURNEY TO A DATA LAKE 85% OF DATA GROWTH BY 2020 WILL COME FROM NEW TYPES OF DATA ACCORDING TO IDC, AS MUCH AS 85% OF DATA GROWTH BY 2020 WILL COME FROM NEW TYPES OF DATA,

More information

Quick Reference Selling Guide for Intel Lustre Solutions Overview

Quick Reference Selling Guide for Intel Lustre Solutions Overview Overview The 30 Second Pitch Intel Solutions for Lustre* solutions Deliver sustained storage performance needed that accelerate breakthrough innovations and deliver smarter, data-driven decisions for enterprise

More information

Mike Maxey. Senior Director Product Marketing Greenplum A Division of EMC. Copyright 2011 EMC Corporation. All rights reserved.

Mike Maxey. Senior Director Product Marketing Greenplum A Division of EMC. Copyright 2011 EMC Corporation. All rights reserved. Mike Maxey Senior Director Product Marketing Greenplum A Division of EMC 1 Greenplum Becomes the Foundation of EMC s Big Data Analytics (July 2010) E M C A C Q U I R E S G R E E N P L U M For three years,

More information

I D C V E N D O R S P O T L I G H T. S t o r a g e Ar c h i t e c t u r e t o Better Manage B i g D a t a C hallenges

I D C V E N D O R S P O T L I G H T. S t o r a g e Ar c h i t e c t u r e t o Better Manage B i g D a t a C hallenges I D C V E N D O R S P O T L I G H T S t o r a g e Ar c h i t e c t u r e t o Better Manage B i g D a t a C hallenges September 2012 Adapted from Worldwide File-Based Storage 2011 2015 Forecast: Foundation

More information

Protecting Big Data Data Protection Solutions for the Business Data Lake

Protecting Big Data Data Protection Solutions for the Business Data Lake White Paper Protecting Big Data Data Protection Solutions for the Business Data Lake Abstract Big Data use cases are maturing and customers are using Big Data to improve top and bottom line revenues. With

More information

The Future of Data Management with Hadoop and the Enterprise Data Hub

The Future of Data Management with Hadoop and the Enterprise Data Hub The Future of Data Management with Hadoop and the Enterprise Data Hub Amr Awadallah Cofounder & CTO, Cloudera, Inc. Twitter: @awadallah 1 2 Cloudera Snapshot Founded 2008, by former employees of Employees

More information

A TECHNICAL WHITE PAPER ATTUNITY VISIBILITY

A TECHNICAL WHITE PAPER ATTUNITY VISIBILITY A TECHNICAL WHITE PAPER ATTUNITY VISIBILITY Analytics for Enterprise Data Warehouse Management and Optimization Executive Summary Successful enterprise data management is an important initiative for growing

More information

ROME, 17-10-2013 BIG DATA ANALYTICS

ROME, 17-10-2013 BIG DATA ANALYTICS ROME, 17-10-2013 BIG DATA ANALYTICS BIG DATA FOUNDATIONS Big Data is #1 on the 2012 and the 2013 list of most ambiguous terms - Global language monitor 2 BIG DATA FOUNDATIONS Big Data refers to data sets

More information

Hadoop and Data Warehouse Friends, Enemies or Profiteers? What about Real Time?

Hadoop and Data Warehouse Friends, Enemies or Profiteers? What about Real Time? Hadoop and Data Warehouse Friends, Enemies or Profiteers? What about Real Time? Kai Wähner kwaehner@tibco.com @KaiWaehner www.kai-waehner.de Disclaimer! These opinions are my own and do not necessarily

More information

Big Data: Beyond the Hype. Why Big Data Matters to You. White Paper

Big Data: Beyond the Hype. Why Big Data Matters to You. White Paper Big Data: Beyond the Hype Why Big Data Matters to You White Paper BY DATASTAX CORPORATION October 2013 Table of Contents Abstract 3 Introduction 3 Big Data and You 5 Big Data Is More Prevalent Than You

More information

Introduction to Multi-Data Center Operations with Apache Cassandra and DataStax Enterprise

Introduction to Multi-Data Center Operations with Apache Cassandra and DataStax Enterprise Introduction to Multi-Data Center Operations with Apache Cassandra and DataStax Enterprise White Paper BY DATASTAX CORPORATION October 2013 1 Table of Contents Abstract 3 Introduction 3 The Growth in Multiple

More information

Staying agile with Big Data

Staying agile with Big Data An Ovum white paper for Red Hat Publication Date: 09 Sep 2014 Tony Baer Summary Catalyst Like any major technology project, organizations implementing Big Data projects face challenges with aligning business

More information

Apache Hadoop: The Big Data Refinery

Apache Hadoop: The Big Data Refinery Architecting the Future of Big Data Whitepaper Apache Hadoop: The Big Data Refinery Introduction Big data has become an extremely popular term, due to the well-documented explosion in the amount of data

More information

The Big Data Market: Business Case, Market Analysis & Forecasts 2015-2020

The Big Data Market: Business Case, Market Analysis & Forecasts 2015-2020 Brochure More information from http://www.researchandmarkets.com/reports/2983902/ The Big Data Market: Business Case, Market Analysis & Forecasts 2015-2020 Description: Big Data refers to a massive volume

More information

Cloud Computing on a Smarter Planet. Smarter Computing

Cloud Computing on a Smarter Planet. Smarter Computing Cloud Computing on a Smarter Planet Smarter Computing 2 Cloud Computing on a Smarter Planet As our planet gets smarter more instrumented, interconnected and intelligent the underlying infrastructure needs

More information

Analytics framework: creating the data-centric organisation to optimise business performance

Analytics framework: creating the data-centric organisation to optimise business performance Research Report Analytics framework: creating the data-centric organisation to optimise business performance October 2013 Justin van der Lande 2 Contents [1] Slide no. 5. Executive summary 6. Executive

More information

Elastic Private Clouds

Elastic Private Clouds White Paper Elastic Private Clouds Agile, Efficient and Under Your Control 1 Introduction Most businesses want to spend less time and money building and managing IT infrastructure to focus resources on

More information

Solution White Paper Connect Hadoop to the Enterprise

Solution White Paper Connect Hadoop to the Enterprise Solution White Paper Connect Hadoop to the Enterprise Streamline workflow automation with BMC Control-M Application Integrator Table of Contents 1 EXECUTIVE SUMMARY 2 INTRODUCTION THE UNDERLYING CONCEPT

More information

So What s the Big Deal?

So What s the Big Deal? So What s the Big Deal? Presentation Agenda Introduction What is Big Data? So What is the Big Deal? Big Data Technologies Identifying Big Data Opportunities Conducting a Big Data Proof of Concept Big Data

More information

BEYOND BI: Big Data Analytic Use Cases

BEYOND BI: Big Data Analytic Use Cases BEYOND BI: Big Data Analytic Use Cases Big Data Analytics Use Cases This white paper discusses the types and characteristics of big data analytics use cases, how they differ from traditional business intelligence

More information

WHITE PAPER. Data Migration and Access in a Cloud Computing Environment INTELLIGENT BUSINESS STRATEGIES

WHITE PAPER. Data Migration and Access in a Cloud Computing Environment INTELLIGENT BUSINESS STRATEGIES INTELLIGENT BUSINESS STRATEGIES WHITE PAPER Data Migration and Access in a Cloud Computing Environment By Mike Ferguson Intelligent Business Strategies March 2014 Prepared for: Table of Contents Introduction...

More information

SAP HANA FAQ. A dozen answers to the top questions IT pros typically have about SAP HANA

SAP HANA FAQ. A dozen answers to the top questions IT pros typically have about SAP HANA ? SAP HANA FAQ A dozen answers to the top questions IT pros typically have about SAP HANA??? Overview If there s one thing that CEOs, CFOs, CMOs and CIOs agree on, it s the importance of collecting data.

More information

Real-Time Data Analytics and Visualization

Real-Time Data Analytics and Visualization Real-Time Data Analytics and Visualization Making the leap to BI on Hadoop Predictive Analytics & Business Insights 2015 February 9, 2015 David P. Mariani CEO, AtScale, Inc. THE TRUTH ABOUT DATA We think

More information