September 2014 INSIGHTS. Infrastructure in 2014: Past, Present, & Future. Scott Card, Partner Zack Lesko, CTO Nick Wetegrove, Analyst

Transcription

1 September 2014 INSIGHTS Infrastructure in 2014: Past, Present, & Future Scott Card, Partner Zack Lesko, CTO Nick Wetegrove, Analyst

2 The Traditional Emergence Data of vs. Big Big Data Data Summary SIZE: LOCATION: TYPE: SCHEMA: RELATION: VELOCITY: SIZE: LOCATION: TYPE: SCHEMA: RELATION: VELOCITY: Traditional Data Gigabytes to Terabytes Centralized Structured Defined, stable data model Known set of basic relationships Big Data Petabytes to Exabytes Distributed Source: Wikibon, 451 Research, AGC Static, largely fixed datasets Unstructured / loose structure Flat, schema-less models Large number of complex relationships Rapidly growing / changing datasets Overview / Summary This report explores the fundamental drivers and key trends underlying Big Data, which is a rapidly emerging opportunity and a term which has been very broadly used to describe all aspects of next-generation data management and analytics. In this report we look at the key underlying infrastructure building blocks which form the foundation of Big Data, discuss the solutions and vendors who are leveraging these underlying platforms, and explore the key battlegrounds and opportunities for consolidation which are emerging as enterprises increasingly deploy and integrate Big Data solutions into their environments. The Genesis / Emergence of Big Data The exponential explosion in the amount of data humans are creating every moment is an oft-cited statistic. Indeed, the statistics are impressive Wikibon estimates that in 2014, humans will generate 5 billion gigabytes worth of data every 10 minutes, which equates to the same amount of data that was generated by humans from the beginning of time up until This enormous increase in the sheer volume and speed of data creation, as well as the variety of data sources and data types being created, has quickly put pressure on existing database solutions such as SQL-based relational database management systems (RDBMS), which were designed in the 1970s to deal with structured, siloed datasets. While RDBMS continue to be the dominant force in data management and have seen significant performance/efficiency improvements, in part due to reduced hardware costs and improved storage/memory capabilities, traditional RDBMS are not equipped to deal with the scale, velocity, diversity, and unstructured nature of data in emerging, highly distributed Web-scale environments. The excerpts below from The 451 Group and IDC describe the resulting opportunity for Big Data technologies and solutions: Big Data is a term applied to data sets that are large, complex or dynamic (or a combination thereof) and for which there is a requirement to capture, manage and process the data set in its entirety, such that it is not possible to process the data using traditional software tools and analytic techniques within tolerable time frames. 451 Group Big Data describes a new generation of technologies and architectures, designed to economically extract value from very large volumes of a wide variety of data, by enabling high-velocity capture, discovery, and/or analysis IDC The origins of the underlying technologies of the Big Data movement were driven by a need to create an alternative data management solution identified by some of the largest Internet companies. These companies have been on the front lines of the explosion in data creation, and in response, developed Page 2 of 15

3 internal data management solutions to effectively process and analyze complex, unstructured datasets spanning text, audio/video, and other types of data. As many of these cloudbased Web platforms needed to derive real-time and dynamic business intelligence and analytics distributed across hundreds of millions of users, they created a wide variety of platforms designed to overcome the resource constraints of RDBMS. Amazon s Dynamo, Yahoo s Hadoop, Google s MapReduce, LinkedIn s Voldemort, and Facebook s Cassandra & Hive are all today offered in various forms as standalone Big Data management tools. Most of these platforms have also been open-sourced and serve as the foundation for many of today s proprietary Big Data solutions. Over the past few years, developers have expanded the capabilities of these solutions by developing proprietary solutions which leverage and add value to the core open source platforms. Source: Wikibon 2014 Source: Wikibon 2014 In line with the explosion of Big Data volumes, the market for Big Data software, services, and hardware has seen significant growth. According to Wikibon estimates, sales in 2013 for Big Data solutions were $18.6 billion, representing a 58% increase over Wikibon estimates, that by the end of 2014, sales will reach $28.5 billion and will top $50 billion by the end of This represents a ~28% CAGR from 2013 to It is worth noting that services, from companies such as Cognilytics, represented the biggest component of sales at ~40%, followed by hardware and software at ~28% and ~22% respectively. This highlights the fact that much of software in the Big Data ecosystem is open source, and that enterprises still need significant expertise in effectively leveraging the core platforms and identifying effective use cases for emerging Big Data technology. As a result, professional services are expected to remain a significant component of Big Data revenues in the coming years as the technology and associated solutions continue to mature. Key Technology Platforms in Big Data Today, with the average company holding 100+ TB of data, enterprises face a somewhat daunting set of challenges as well as some compelling opportunities in looking to leverage this data and derive intelligence in order to drive more effective business processes. A number of technology platforms have emerged over the past several years to address the Big Data opportunity, each with distinct capabilities and associated use cases. While Hadoop and NoSQL solutions have garnered the most attention, various other solutions, including columnbased / massively parallel analytic databases, scale-out SQL (or newsql ) and graph databases have also gained traction and mindshare in the Big Data marketplace. Within this mix of platforms and despite heterogonous approaches to tackling Big Data, there are several common traits these platforms share. According to Wikibon, these include: Taking advantage of commodity hardware to enable scale-out, parallel processing techniques Source: Wikibon 2013 Page 3 of 15

4 ORIGIN: Hadoop vs. NoSQL Hadoop Framework for storage & processing of data developed in 2005 by a Yahoo employee; Based on Google s MapReduce solution to provide a means to map and reduce large-scale, distributed computation USE CASES: Historical, batch-style processing, including: Recommendations for e-commerce & travel sites LinkedIn s People You May Know Marketing analysis for loyalty card holders VENDORS: Cloudera, Hortonworks, MapR, Datameer, Platfora, Incumbent Vendors MODLUES / Hadoop Distributed File EXTENSIONS: Systems (HDFS), MapReduce, YARN (simultaneous data processing), Pig (programming language extension), Hive (data structure), HBase (sparse data/big tables), Spark (rapid analytics) Source: Wikibon, 451 Research, AGC Employing non-relational data storage capabilities to process unstructured and semi-structured data Applying advanced analytics and data visualization technology to Big Data to convey insights to end-users For purposes of this discussion, we will focus primarily on Hadoop and NoSQL platforms, which serve as the foundation technologies for numerous other Big Data solutions, as well as the solutions brought to market from relational database and other incumbent database/data management providers. Hadoop Hadoop was developed in 2005 by an employee at Yahoo who was inspired by Google s MapReduce solution, which itself was initially developed to help improve Web indexing. Hadoop is an open source data storage & processing solution that allows for predictive analytics to be readily performed on large, distributed, and unstructured data sets. Hadoop is able to break up huge amounts of data and distribute ( map ) the analysis and processing across multiple hardware nodes and then assemble the finished product from each node back into a single work ( reduce ). Hadoop thus provides the framework for conducting analytic jobs using data stored within the massively-scalable Hadoop distributed file system. It is able to deliver processed data to data scientists who can analyze, manipulate, and interpret the data using other tools built on top of the platform. Hadoop is most adept at enabling the historical analysis of batch data, as opposed to NoSQL platforms which specialize in the real-time processing of data, in some cases data which is returned by Hadoop/MapReduce jobs themselves. Hadoop s cluster-based architecture is scalable across petabytes (1 million gigabytes) or exabytes (1 billion gigabytes) of data, which allows enterprises to analyze the entirety of a data set, instead of just sample sets, which was previously the norm. This has allowed enterprises to uncover previously unknown and undiscoverable trends and insights on their data sets and correspondingly their customers. For instance, Hadoop has powered the rise of the recommendation engine on sites such as Amazon and LinkedIn, which can suggest relevant products or People You May Know. Hadoop also powers sentiment analysis for companies seeking to derive insights from the plethora of unstructured media such as Twitter feeds and Facebook posts related to particular brands. Apart from powerful business insights for developers and enterprises, Hadoop is also attractive due to the open-source nature of the software versus expensive, traditional RDBMS enterprise licenses offered by incumbent database vendors. Unlike the NoSQL market, which is comprised of numerous database platform vendors, Hadoop is more monolithic. That said, there are several startups, including the recipients of the 3 largest disclosed Big Data financings in the first half of 2014 Cloudera (valued at $4B in most recent financing round on $73M in 2013 revenues), Hortonworks (valued at $1B+ in most recent round on $55M in revenues), and MapR (raised $110M Page 4 of 15

5 ORIGIN: NoSQL Early 2000s to deliver unstructured data to users & for analysis USE CASES: Real-time, web-based transactions, including: Facebook chat Online shopping carts Social gaming user info PLATFORM MongoDB Documentoriented Hadoop vs. NoSQL (Cont d) Riak Key-value store Couchbase Server [Apache CouchDB & Membase] Real-time operational database Apache Cassandra Key-value store Aerospike In-memory keyvalue store Apache Accumulo Key-value store Proprietary MongoDB, MongoLabs, MongoHQ, ObjectRocket (Rackspace) Basho Couchbase DataStax Aerospike Sqrrl Source: Wikibon, 451 Research, AGC PRIMARY VENDORS MarkLogic, FoundationDB in debt and equity in June 2014 at an undisclosed valuation) as well as incumbent IT platform vendors such as Microsoft, IBM, and Intel who have focused on delivering solutions which allow for enterprise-class Hadoop deployments. In addition, while Hadoop s batch-style processing does not support realtime data analysis, several solutions have been developed to integrate Hadoop with NoSQL s more real-time processing capabilities, including HBase, an open-source NoSQL database that can be used for quick Hadoop lookups. Furthermore, extensions such as Spark, an engine for largescale data processing and rapid analytics, have been built ontop of Hadoop utilizing in-memory capabilities for real-time processing. NoSQL NoSQL has emerged over the last several years as a database platform aimed at delivering unstructured data to users and data analysis applications. While Hadoop is adept at enabling predictive analytics to be performed on batches of data, NoSQL databases have emerged to power real-time, often web-based transactions and content delivery across unstructured data environments where traditional RDBMS cannot keep up. For NoSQL databases, however, performance has historically come at the cost of SQL tenets such as ACID (atomicity, consistency, isolation, durability) due to the distributed nature of NoSQL clusters that focus more on speed and scaling out than consistency. NoSQL can still offer a highdegree of availability in some cases, due to the redundant nature of distributed clusters where consistency will be eventually reached, but there is increased risk of data loss in some instances. As such, NoSQL platforms have found use cases among web platform providers such as Facebook, for instance for storing millions to billions of user chats, and Amazon, for remembering shopping cart items before checkout. A number of vendors are working to overcome consistency deficiencies and provide a more mature NoSQL toolset. As has been the case with Hadoop, there are several NoSQL database platform vendors that have gained significant traction including MarkLogic, MongoDB, DataStax, Couchbase, Basho, Aerospike, and Sqrrl. Reactions of Traditional Database Providers While emerging technologies like Hadoop and NoSQL have garnered significant hype and mindshare, the incumbent database / data management vendors are closely watching the Big Data opportunity and are launching solutions which leverage their existing product portfolios. Further, it should be noted that traditional SQL databases are expected to play an important role in Big Data solutions going forward, and are expected to account for the majority of database revenue in Big Data versus Hadoop/NoSQL (though Hadoop/NoSQL growth rates are expected to be significantly higher based on Wikibon data). For incumbent vendors such as IBM, EMC/Pivotal and HP, solutions targeted at Big Data typically represent extensions of their initial first forays into data warehousing / analytical Page 5 of 15

6 Source: Wikibon Source: Wikibon database solutions, which were products obtained via acquisition before Big Data as we know it today emerged in the marketplace. Specifically, these solutions are capable of processing terabytes or petabytes of structured data across multiple, parallel nodes using column-based architectures (a more effective way of processing certain ad hoc or aggregate queries than traditional row-based methods), data compression, and in-memory processing. In the second half of 2010 and early 2011, IBM, EMC, and HP all acquired data warehousing / analytic database solutions - Netezza (IBM), Greenplum (EMC), and Vertica (HP), which as a group garnered a median EV / LTM revenue multiple of over 12x, and have continued to evolve as key components of their parent companies Big Data strategies. For Netezza, an appliance-based data warehousing solution, IBM has rolled the company s analytics capabilities into its PureData product family and focused on integrating Netezza s appliance technology into IBM s own servers. Recently, through its Pivotal business unit, EMC s Greenplum data warehouse has been integrated with Pivotal s Hadoop distribution as a more robust analytics solution or a total data warehouse. For HP Vertica s data warehousing and analytics solution, the ability for SQL-based Hadoop queries and extended BI front-end support has extended Vertica s potential market reach. Given how costly an acquisition of an emerging Hadoop vendor would be, and the fact that the underlying technology platforms have been open sourced, the incumbents have largely focused on integration and extensibility across technologies, particularly with regards to extending Hadoop into a more real time, query-able solution. In addition to solutions and services for integrating Hadoop and NoSQL into a broader Big Data solution, several vendors such as Clustrix, NuoDB and VoltDB have introduced scaleout SQL or NewSQL solutions for Big Data. These solutions are positioned as more scalable and cloud-ready forms of traditional SQL-based RDBMS. These NewSQL parallel processing solutions support SQL queries, which makes it particularly appealing for organizations who already have armies of analysts trained in this relatively simple query language. In addition, companies such as Delphix, ScaleArc, and ScaleBase have bolstered the viability of traditional database solutions with traffic management, sharding, and optimization solutions for both relational and distributed databases. In addition, companies such as Altibase and GridGain have leveraged the cost and operational efficiencies of RAM-based, in-memory databases along with the convenience of SQL queries, to provide comparable performance to NoSQL and other big data platforms without relying solely on disk storage systems. These solutions will arguably allow SQL to continue to reside in certain highly distributed and cloud environments and offer many of the benefits that emerging Hadoop and NoSQL platforms have brought to the marketplace. Emerging Battlegrounds and Outlook for Big Data For enterprises and end-users, the market for Big Data solutions appears fragmented, with incumbent vendors and Page 6 of 15

7 Source: 451 Research, Capital IQ Source: 451 Research, Capital IQ Source: 451 Research, Capital IQ Source: 451 Research, Capital IQ emerging companies jockeying for position. Based on our discussions with key market participants, a core movement and focus area will be large enterprise adoption, and integration of emerging Hadoop and NoSQL environments into existing data management / data warehousing infrastructure. As a result, we have seen more cohesive Big Data solutions emerge, as incumbents, emerging vendors (and in many cases combined solutions among multiple vendors) look to introduce fully integrated solutions which add value relative to what a raw open source platform or a legacy solution could do in isolation. Below are several key areas of innovation that may represent key battlegrounds in the Big Data arena as we approach 2015: Integration / Federation Across Platforms / Systems Both incumbent vendors and emerging startups have launched solutions to integrate structured databases (e.g. SQL-based RDBMS), unstructured databases (e.g. NoSQL), and Hadoop s analytics capabilities into a single, query-able Big Data offering. For instance, Oracle has recently announced the launch of Oracle Big Data SQL, which provides this functionality for enterprises seeking to analyze data across structured and unstructured datasets. Among emerging NoSQL database vendors, Hadoop integration has also gained traction as vendors try to simplify and integrate Big Data offerings. For example, NoSQL vendor MongoDB recently announced a partnership with Hadoop vendor Cloudera. In addition, independently held vendors such as Cirro are introducing solutions which can integrate multiple data warehouses across various platforms into a combined offering for more efficient and effective data analytics. Analytic Pipelines / Search Platforms While organizations are rapidly deploying Hadoop clusters and NoSQL databases, those emerging platforms in isolation do not necessarily provide a full solution which enables the insight and intelligence which enterprises are looking for from their data sets. Emerging solutions in this area include search algorithms from vendors such as Lucidworks who can enable more rapid identification and exploration of data in Hadoop clusters, or solutions from vendors such as Datameer and Platfora who provide a key layer of data cleansing and preparation before analytics are performed on the underlying data store. Platform distribution vendors such as Cloudera, Hortonworks and MongoDB are also looking to aggressively add data management functionality in order to differentiate their core platforms and move further up the stack into analytics to provide a full solution to the customer. Security and Policy Management One of the key hurdles the largest enterprises have identified with emerging Big Data solutions is a lack of security and governance. Hadoop clusters and/or NoSQL databases have often been deployed in isolation without being managed as part of an organization s broader security & policy management framework. As Big Data solutions continue to mature, vendors such as Zettaset (which offers policy management for Hadoop) and security / compliance features offered by the platform vendors Page 7 of 15

8 Recent M&A Transactions Teradata / Hadapt (7/14; Undisclosed transaction details) Teradata s acquisition of Hadapt, a provider of Hadoop and adaptive analytic SQL unification solutions for enterprises, highlights continued momentum for Big Data platform integration. Cloudera / Gazzang (6/14; Undisclosed transaction details) Cloudera s purchase of Gazzang, a cloud-based PaaS data encryption solution for securing Big Data, signifies a stronger push for unified security as Hadoop deployments spread across the enterprise. Hortonworks / XA Secure (5/14; Undisclosed transaction details) The acquisition of XA Secure, a Hadoop security & policy management solution, is in line with Cloudera s Gazzang acquisition. Specifically, the early lack of focus on security and fragmented nature of security solutions for Hadoop has pushed the largest vendors towards an integrated security solution. IBM / Cloudant (2/14; $150M at 21.4x LTM revenues) Cloudant represents an acquisition within the DaaS space as IBM and Big Data vendors in general push towards the cloud as the next logical step in the Big Data product lifecycle. Recent Financings Couchbase (6/14; $60M) The investment round in the NoSQL company was led by WestSummit Capital, Accel Growth Fund, and existing investors. Brings total funding to $115M at a post-money valuation south of $1B. MapR (6/14; $80M) Latest round for Hadoop vendor at an undisclosed valuation brings total funding to date to $174M. $80M in equity led by Google Capital, QUALCOMM Ventures, and existing investors plus $30M in debt from Silicon Valley Bank. Hortonworks (3/14; $150M) Series D round for Hadoop vendor saw participation from investment manager BlackRock, hedge fund Passport Capital, and strategic investor HP among others. Latest round values company at over $1B and brings total funding to $150M. Cloudera (3/14; $900M) Round for Hadoop vendor included $740M investment from Intel Capital, as well as funds from T. Rowe Price and Google Ventures. Series F funding and investment from Intel values company at $4.1B. MongoDB (10/13; $150M) NYC-based NoSQL vendor raised funds from T. Rowe Price, Fidelity, EMC, Salesforce and others. Total funding to date of $231M and most recent round valued Company at a post-money valuation of $1.2B. Source: 451 Research, Capital IQ, AGC Note: Financings represent largest disclosed financings between 7/1/13 and 7/1/14 themselves may continue to gain traction. Vendors from the security and compliance community are also highly aware of this opportunity, and in many cases have launched solutions specifically targeted at securing Big Data environments. Cloud / DBaaS Given that many of the applications Big Data is fueling reside in the cloud, and the bursty / dynamic nature of data and data analytics workloads, Cloud and Big Data were meant for each other in many ways. Many Big Data workloads will reside in the Cloud, and incumbents, emerging vendors and Cloud service providers have all launched cloud-based Big Data and/or Database-as-a-Service (DaaS) solutions. Vendors such as Cloudant (acquired by IBM), MongoLabs, GenieDB and CumuLogic offer various forms of databases as hosted or on-premise solutions which provide a dynamic level of service delivery on top of the underlying database platform. Other emerging vendors such as Qubole are offering a full analytics environment delivered through Amazon Web Services, providing the underlying data store as well as certain tools / platforms for performing analytics. As enterprise adoption of both Cloud & Big Data continues to proliferate, cloud-based delivery models and solutions for Big Data are positioned for significant market penetration. Transaction Activity in the Big Data Sector The Big Data sector has seen a flurry of acquisitions in 2014 after an impressive year in Despite the wave of consolidation over the past 18 months, we have not yet seen anything that compares to the blockbuster acquisitions of Aster Data Systems, Vertica Systems, Netezza, and Greenplum in 2010 and 2011, which as a group had a median EV / LTM revenue multiple of over 12x. Having said that, M&A volume has picked up dramatically as enterprise traction for Big Data builds, causing leading incumbents and some of the emerging platform vendors to try to fill in their offerings with complementary capabilities. Financings have also been very active with large amounts of capital being raised at highly compelling ($1B+ in some cases) valuations, positioning many of those companies as potential acquirers rather than potential targets. In closing, Big Data remains an early and complicated opportunity in the eyes of many enterprises. While nearly all enterprises understand that they need a Big Data strategy, these technologies are still raw, not user friendly, and sometimes inaccessible. Further, deploying Big Data solutions often requires significant expertise and training within organizations. Nonetheless, a move towards integration and cooperation leaves promise that total Big Data solutions will continue to develop. While it is still uncertain who the winners will be in this space, it is important to remember that this is not a zero sum game. Despite broader uncertainty among enterprises regarding Big Data use-cases, the possibility that the most critical insights that might be gleaned from Big Data are, as of yet, unknown will continue to drive explosive growth and innovation in the sector. Page 8 of 15

9 Big Data Infrastructure Landscape Source: 451 Research, Capital IQ, Wikibon, Company Websites, AGC Page 9 of 15

10 Precedent M&A Transactions Date Announced Target Name Acquirer Name Enterprise Value ($M) EV / LTM Revenue LTM Revenue Target Business Description 1. 06/30/14 OhmData WANdisco International $2 ND ND Provides active data replication and source-code management software for Apache Subversion, Git, Non-Stop Hadoop, etc /17/14 Radoop RapidMiner ND ND ND 3. 06/03/14 Gazzang Cloudera ND ND ND 4. 05/15/14 XA Secure Hortonworks ND ND ND 5. 04/28/14 Jaspersoft TIBCO Software x $ /16/14 Karmasphere FICO ND ND ND 7. 02/24/14 Cloudant IBM x /22/14 Scout Analytics ServiceSource x /08/14 InsightsOne Apigee ND ND ND /09/13 StormDB TransLattice ND ND ND /07/13 Confio Software SolarWinds x /06/13 Infochimps CSC 25 ND ND Provides Hadoop-based data management SaaS to enable big data applications and predictive data analytics for businesses globally. Provides cloud-based data encryption software for securing big data and key management software for securing digital assets. Provides Hadoop security administration and enforcement software, which covers policy management, encryption, compliance capabilities, etc. Provides open-source business intelligence (BI) reporting and analytics software and SaaS for businesses globally. Provides businesses with query construction and collaboration software for use in Hadoop data analytics environments. Provides NoSQL-based non-relational database-as-a-service (DBaaS) software for businesses. Provides predictive customer analytics SaaS for businesses that's meant to optimize sales and renewals. Provides on-premise & cloud-based software for predicting customer complaints & identifying sales leads to provide targeted advertisements. Provides relational open source PostgreSQL database software, enabling clusters of disparate servers in a single datacenter to run a single database. Provides Microsoft SQL Server, Oracle, IBM DB2, VMware virtualized databases and SAP Sybase database performance management software. Provides Hadoop-based data management PaaS for developers and businesses globally to develop and deploy big data applications /17/13 Akiban Technologies FoundationDB ND ND ND Provides businesses with open-source, SQL relational database software for use with SaaS-based applications /20/13 Composite Software Cisco Systems x 40 Provides a range of data virtualization, integration and analytics software and services for businesses /17/13 Panopticon Software Datawatch x 5 Provides real-time data visualization BI analytics and reporting software /11/13 StreamBase Systems TIBCO Software x 20 Provides business intelligence (BI) analytics and reporting software for use in analyzing streaming data in real-time for businesses /25/13 ParAccel (1) Actian x 20 Provides big data warehousing and analytics-based database management software for companies, including online retailers and financial institutions /27/13 ObjectRocket Rackspace ND ND ND Provides MongoDB database application hosting services for businesses /01/13 Star Analytics IBM ND ND ND /18/13 GridIron Systems Violin Memory ND ND ND /21/12 Versant Actian x ND /23/12 Kitenga Quest Software ND ND ND /24/12 AlchemyDB CitrusLeaf ND ND ND Provides data integration software for use with Oracle database, ERP and business intelligence (BI) software. Provides storage area network (SAN) data caching and application performance acceleration systems for businesses globally. Provides object-oriented database management software for the telecommunications and financial services sectors globally. Provides enterprise text, audio, video and image data mining, search and related analytics software for use in Hadoop environments for businesses. Provides database software that combines SQL and NoSQL databases for businesses /02/11 Aster Data Systems Teradata x ND Provides data warehousing and analytics software and systems for businesses globally /14/11 Vertica Systems Hewlett-Packard x 25 Provides data warehousing and analytics software and software as a service (SaaS) for businesses /20/10 Netezza IBM 1, x 223 Develops and provides data warehouse and analytics software solutions /06/10 Greenplum EMC x 29 (1) Based on estimates MEDIAN: $ x $20 Note: M&A activity from 1/1/10 7/1/14 Source: Capital IQ, 451 Research Develops database software for business intelligence and data warehousing applications Page 10 of 15

11 Private Placements Announced Date Target Buyer/Investors Size ($M) 6/26/2014 Couchbase 6/25/2014 MapR Technologies Accel Partners; Adams Street Partners; DoCoMo Capital; Ignition Partners; North Bridge Venture Partners; WestSummit Capital Google Capital; Lightspeed Venture Partners; Mayfield Fund; New Enterprise Associates; QUALCOMM Ventures; Redpoint Ventures 6/18/2014 NuoDB ND ND $ /30/2014 Concurrent Bain Capital Ventures; Rembrandt Venture Partners; True Ventures 10 5/19/2014 Context Relevant Bloomberg Beta; Formation8 Partners; Madrona Venture Group; Vulcan Capital 25 4/25/2014 Clustrix ND 10 3/31/2014 ClearStory Data Andreessen Horow itz; DAG Ventures; Google Ventures; Khosla Ventures; Kleiner Perkins Caufield & Byers 21 3/27/2014 Cirro ND 1 3/25/2014 Hortonw orks 3/19/2014 Platfora Benchmark Capital; BlackRock; Dragoneer Investment Group; Hew lett-packard Company; Index Ventures; Passport Capital; Tenaya Capital; Yahoo! Allegis Capital; Andreessen Horow itz; Battery Ventures; Cisco Systems; Citi Ventures; In-Q-Tel; Sutter Hill Ventures; Tenaya Capital /18/2014 Cloudera Google Ventures; Intel Capital; MSD Capital; T. Row e Price 900 2/27/2014 VoltDB Kepha Partners; Sigma Partners 8 2/11/2014 Platfora Citi Ventures ND 2/7/2014 NuoDB 11/15/2013 Datameer Dassault Systemes; Hummer Winblad Venture Partners; Longw orth Venture Partners; Morgenthaler Citi Venture Capital International; Kleiner Perkins Caufield & Byers; Next World Capital; Redpoint Ventures; Softw are AG; Workday /7/2013 Cirro Frost Data Capital; GE Ventures; Miramar Venture Partners; Toba Capital 8 10/2/2013 MongoDB EMC; Fidelity Investments; Intel Capital; New Enterprise Associates; Red Hat; Salesforce.com; Sequoia Capital; T. Row e Price 150 8/30/2013 Appistry ND 11 8/28/2013 Couchbase 8/2/2013 Clustrix Accel Partners; Adams Street Partners; Ignition Partners; Mayfield Fund; North Bridge Venture Partners ATA Ventures; Canary Foundation, The, Endow ment Arm; HighBAR Partners; Sequoia Capital; U.S. Venture Partners /23/2013 Treasure Data Sierra Ventures 5 7/17/2013 DataStax CrossLink Capital; Draper Fisher Jurvetson; Lightspeed Venture Partners; MeriTech Capital Partners; Next World Capital; Scale Venture Partners 45 7/10/2013 Treasure Data ND 8 7/8/2013 Context Relevant Bloomberg Beta L.P.; Madrona Venture Group; Vulcan Capital 7 6/27/2013 Ayasdi Citi Ventures; FLOODGATE; GE Ventures; Institutional Venture Partners; Khosla Ventures 31 6/25/2013 Hortonw orks Benchmark Capital; Dragoneer Investment Group; Index Ventures; Tenaya Capital; Yahoo! 50 Note: Private placement activity from 1/1/12 7/1/14 Source: Capital IQ, 451 Research Page 11 of 15

12 Private Placements (Cont d) Announced Date Target Buyer/Investors Size ($M) 5/24/2013 Guavus ND 2 5/8/2013 Predixion Softw are 4/30/2013 Skytree Accenture plc; DFJ Frontier; Frost Data Capital; GE Ventures; Miramar Venture Partners; Palomar Ventures Javelin Venture Partners; Osage Partners; U.S. Venture Partners; United Parcel Service; UPS Strategic Enterprise Fund /23/2013 Qubole Charles River Ventures; Lightspeed Venture Partners 7 4/22/2013 Clustrix ATA Ventures; Sequoia Capital; U.S. Venture Partners 17 4/22/2013 TransLattice ND 10 3/22/2013 MarkLogic Corporation Northgate Capital Group; Sequoia Capital; Tenaya Capital 26 3/20/2013 Concurrent Rembrandt Venture Partners; True Ventures 4 3/4/2013 MapR Technologies Lightspeed Venture Partners; Mayfield Fund; New Enterprise Associates; Redpoint Ventures 35 2/14/2013 HStreaming Atlas Venture 1 2/14/2013 Clustrix ND 3 2/6/2013 Cloudant Avalon Ventures; Devonshire Investors; In-Q-Tel; Rackspace Hosting; Samsung Venture Investment Corporation; Toba Capital 12 1/10/2013 ScaleArc Accel Partners; Nexus Venture Partners; Trinity Ventures 12 12/31/2012 Treasure Data ND 3 12/19/2012 Clustrix Hercules Technology Grow th Capital 1 12/18/2012 Revolution Analytics ND 8 12/5/2012 ClearStory Data Andreessen Horow itz; Google Ventures; Kleiner Perkins Caufield & Byers 9 12/3/2012 Cloudera Accel Partners; Greylock Partners; Ignition Partners; In-Q-Tel; MeriTech Capital Partners 65 11/30/2012 Guavus Artiman Managment; GS Direct; Intel Capital; Investor Grow th Capital; QuestMark Partners; Sofinnova Ventures; TransLink Capital 39 11/30/2012 Karmasphere Hummer Winblad Venture Partners; U.S. Venture Partners 4 11/27/2012 Zettaset Brocade Communications Systems; Draper Fisher Jurvetson; EPIC Ventures; HighBAR Partners 10 11/14/ gen (MongoDB) Intel Capital; Red Hat ND 11/9/2012 Tokutek ND 2 10/31/2012 Hadapt Atlas Venture 7 10/31/2012 Platfora Andreessen Horow itz; Battery Ventures; Sutter Hill Ventures 20 10/24/2012 Pentaho Corporation Benchmark Capital; DAG Ventures; Index Ventures; New Enterprise Associates 23 10/17/2012 ScaleBase Ascent Venture Partners; Bain Capital Ventures; Cedar Fund 11 Note: Private placement activity from 1/1/12 7/1/14 Source: Capital IQ, 451 Research Page 12 of 15

13 Private Placements (Cont d) Announced Date Target Buyer/Investors Size ($M) 10/10/2012 Skytree Osage Partners 1 10/2/2012 Cloudant In-Q-Tel ND 9/28/2012 DataStax CrossLink Capital; Lightspeed Venture Partners; MeriTech Capital Partners 25 9/21/2012 RainStor Doughty Hanson & Co. Technology Ventures; Rogers Ventures Partners; Storm Ventures; Dow Chemical 12 9/17/ gen (MongoDB) In-Q-Tel ND 9/12/2012 Datameer Kleiner Perkins Caufield & Byers; Redpoint Ventures 6 8/3/2012 Trackvia Access Venture Partners; Allen & Company; Draper Fisher Jurvetson; Fairhaven Capital Partners; Flyw heel Ventures; Longw orth Venture Partners 7 7/17/2012 Basho Technologies IDC Frontier 6 6/27/2012 Guavus ND 15 6/27/2012 Clustrix ATA Ventures; Sequoia Capital; U.S. Venture Partners 10 6/22/2012 Ayasdi Defense Advanced Research Projects Agency; FLOODGATE; Khosla Ventures 10 6/6/2012 Delphix Corp. Battery Ventures; Greylock Partners; JAFCO Ventures; Lightspeed Venture Partners; Summit Partners LLP 25 5/23/ gen (MongoDB) Flybridge Capital Partners; New Enterprise Associates; Sequoia Capital; Union Square Ventures 42 5/21/2012 Datahero Foundry Group; Neu Venture Capital 5 5/10/2012 Appistry ND 7 4/30/2012 Context Relevant Madrona Venture Group 3 4/24/2012 Ayasdi ND 1 4/18/2012 FluidInfo ND 0 4/5/2012 NuoDB Hummer Winblad Venture Partners; Longw orth Venture Partners; Morgenthaler 10 3/19/2012 ClearStory Data Andreessen Horow itz; Google Ventures; Khosla Ventures ND 2/24/2012 RainStor ND 1 2/23/2012 Skytree Javelin Venture Partners 2 MEDIAN: $10 Note: Private placement activity from 1/1/12 7/1/14 Source: Capital IQ, 451 Research Page 13 of 15

14 Scott Card Partner Investment Banking IT Infrastructure Scott is a Partner in the Investment Banking Group and founding team member at AGC Partners, focused on Enterprise Infrastructure sectors including Data Center, IT Security, and Networking In his ~20 years as an investment banker, Scott has completed more than 50 mergers and acquisitions and debt / equity financing transactions Prior to joining AGC Partners, Scott was part of Deutsche Bank Alex Brown s Technology Investment Banking Group in Boston Previously, Scott was an Associate in Global Mergers & Acquisitions at SBC Warburg and an Analyst in the Financial Institutions Group at Merrill Lynch & Co. in New York Scott holds a B.S. in Electrical Engineering from Cornell University and an M.B.A. from the Amos Tuck School at Dartmouth College AGC s IT Infrastructure Expertise AGC Today Culture of teamwork, creativity, passion, tenacity, community and integrity A reputation for closing deals large and small at premium valuations History of completing 25 to 30 transactions per year volume brings market knowledge, experience and deep worldwide technology contacts 50 person global team with offices in Boston, Silicon Valley, New York City, Minneapolis and London Strong technology sector expertise lead by 16 AGC Partners with average banking experience of 20 years security, cloud infrastructure, SaaS, digital media, consumer / enterprise application and more 451 Group ranks AGC as the leading technology boutique 10 years in business and 40 consecutive quarters of profitability Page 14 of 15

15 Note: This document is intended to serve as information only, and to suggest that further analysis and consideration may be warranted. Unless otherwise indicated, AGC does not believe that the information contained herein is sufficient to serve as the basis of an investment decision. There can be no assurance that these statements, estimates or forecasts will be attained and actual results may be materially different. Only those representations or warranties which are made in a definitive purchase agreement will have any legal effect. To learn more about the company/companies that is/are the subject of this commentary, contact one of persons named herein who can give you additional information. Page 15 of 15