The Forrester Wave Big Data Fabric Q4 2016

The Forrester Wave™: Big Data Fabric, Q4 2016A Critical Platform For Enterprises To Succeed With Big Data Initiatives

by Noel YuhannaNovember 22, 2016

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Key takeawaysInformatica, IBm, And Talend Lead The PackForrester’s research uncovered a market in which informatica, iBM, oracle, and talend lead the pack. Denodo technologies, Global iDs, paxata, sAp, syncsort, and trifacta offer competitive options. Waterline Data lags behind.

EA Pros Look At Big Data Fabric Solutionsthis market is growing largely because EA pros see big data fabric as a strategic platform to support their next-generation applications and insights. When selecting a solution, enterprises should look for scale-out architecture, security, automation, and cost as the key factors.

Tooling And Services Can Be A DealmakerWhile all of the evaluated vendors offer compelling value and features, some offer a broader range of tooling and services that can accelerate deployments.

Why read this reportBig data initiatives are on the rise as organizations focus on rolling out actionable insights. Big data fabric offers enterprise architecture (EA) pros a platform that helps them discover, prepare, curate, orchestrate, and integrate data across sources by leveraging big data technologies in an automated manner. Forrester’s 26-criteria evaluation of 11 big data fabric solutions will help EA pros understand the available choices and recommend the best for their organization.

this report details our findings about how each vendor fulfills our criteria and where they stand in relation to each other to help EA.

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table of contents

The Big Data Fabric Market Is Immature But Will Grow Rapidly

Big Data Fabric Evaluation Overview

Evaluation criteria: current offering, strategy, And Market presence

Forrester’s Evaluation Assesses the capabilities of 11 Big Data Fabric Vendor offerings

Larger Providers Have An Edge With A Broader Range Of Functionality

Vendor Profiles

leaders

strong performers

contenders

Supplemental Material

Notes & resources

Forrester conducted product evaluations and interviews with 11 vendor companies: Denodo technologies, Global iDs, iBM, informatica, oracle, paxata, sAp, syncsort, talend, trifacta, and Waterline Data.

related research Documents

Big Data Fabric Drives innovation And Growth

the Forrester Wave™: Big Data NosQl, Q3 2016

techradar™: Big Data, Q1 2016

For ENtErprisE ArchitEcturE proFEssioNAls

The Forrester Wave™: Big Data Fabric, Q4 2016A Critical Platform For Enterprises To Succeed With Big Data Initiatives

by Noel Yuhannawith Gene leganza and shreyas Warrier

November 22, 2016

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The Forrester Wave™: Big Data Fabric, Q4 2016november 22, 2016

© 2016 Forrester research, inc. unauthorized copying or distributing is a violation of copyright law. [email protected] or +1 866-367-7378

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A Critical Platform For Enterprises To Succeed With Big Data Initiatives

the Big Data Fabric Market is immature But Will Grow rapidly

Big data is not an option — it has become a necessity for supporting next-generation insights. Enterprises of all types and sizes are embracing big data, but the gap between business expectations and the challenges of supporting big data technology (such as hadoop) has become the primary motivation to innovate with big data fabric. the collection of technologies enables enterprise architects to integrate, secure, and govern various data sources through automation, simplification, and self-services capabilities. it reduces complexity and hides heterogeneity by embodying an abstracted model of the data processing pipeline that reflects business requirements rather than the complexity of the underlying systems.

today, big data fabric is accelerating the delivery of insights by automating key processes for increased agility while giving business users more autonomy in the data preparation process. Enterprises use it to support many use cases, such as enabling 360-degree and multidimensional views of the customer, internet-of-things (iot) and real-time analytics, offloading data warehouses, fraud detection, integrated analytics, and risk analytics. Enterprises are using big data fabric primarily because it:

› Delivers new actionable insights with minimal effort. Big data fabric offers the ability to aggregate, transform, cleanse, and integrate data from multiple big data sources, which can then be presented in dashboards, reporting tools, and web applications. it leverages advanced technologies such as machine learning, Apache spark, hadoop, Kafka, storm, ranger, and others to deliver insights with zero to minimal coding.

› Secures big data end-to-end. Big data fabric enables centralized data access and control, and it enforces a stricter level of data-at-rest and data-in-motion security measures than traditional approaches. it can remediate security risks with masking, auditing, and encryption across the fabric. today, large banks and insurance companies rely on big data fabric to ensure the protection of critical siloed data.

› Enables real-time integrated data across the business. Big data fabric enables data and metadata sharing between peers, employees, partners, and customers. it allows any application, process, dashboard, tool, or user to access any integrated data, regardless of where the data is physically or logically located and regardless of the data format. Big data fabric offers consistent, timely, and trusted data for internal and external users, creating a go-to place for integrated data like Google does for searches.

› Delivers a self-service data platform for business users. until recently, data platforms were mostly used by developers, architects, and data scientists, largely because of the platforms’ complexity and limited use cases. Big data fabric emphasizes self-service data preparation, curation, orchestration, and integration services that nontechnical personnel can leverage. it enables business users to blend, wrangle, and mash up their own data sets and share them among peers and other groups for improved decision making.




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Big Data Fabric Evaluation overview

to assess the state of the market and see how the vendors stack up against each other, Forrester evaluated the strengths and weaknesses of 11 top commercial big data fabric vendors: Denodo technologies, Global iDs, iBM, informatica, oracle, paxata, sAp, syncsort, talend, trifacta, and Waterline Data.

Evaluation Criteria: Current offering, Strategy, And market Presence

After examining past research, user requirements, and vendor interviews, we developed a comprehensive set of 26 evaluation criteria, which we grouped into three high-level buckets:

› Current offering. We evaluated each product’s application development, streaming, loading, data consistency, transactional support, data security, big data support, multimodel, deployment architecture, scalability, performance, in-memory, high availability, and other features and functionality to establish the capabilities of the vendor’s current offering. All products evaluated must have been publicly available by August 1, 2016.

› Strategy. We reviewed each vendor’s strategy to assess its ability to compete and grow in the commercial big data fabric market. Key criteria include Forrester’s level of confidence in the vendor’s ability to execute on its stated strategy as well as support for current and future customers. Forrester also reviewed each vendor’s product road map to assess how it will affect the vendor’s competitive position compared with the other vendors in this evaluation.

› market presence. to determine each vendor’s market presence, we evaluated overall big data fabric product revenue, install base, market awareness, partnerships, and reach.

Forrester’s Evaluation Assesses The Capabilities of 11 Big Data Fabric Vendor offerings

Each of the 11 vendors (Denodo technologies, Global iDs, iBM, informatica, oracle, paxata, sAp, syncsort, talend, trifacta, and Waterline Data) that Forrester included in this evaluation has (see Figure 1):

› A comprehensive big data fabric offering. the vendors included in this evaluation must provide big data fabric functions as defined in the Forrester report “Big Data Fabric Drives innovation And Growth,” published in March 2016.1 these include functions such as access, discovery, transformation, integration, security, governance, lineage, and orchestration of big data sources to support big data workloads and use cases. the solution must be able to process and curate large amounts of structured, semistructured, and unstructured data stored in big data platforms such as Apache hadoop, Mpp EDWs, NosQl, Apache spark, in-memory technologies, and other related commercial and open source platforms, including Apache projects.2 in addition, it must leverage big data technologies such as spark, hadoop, and in-memory as a compute and storage layer to assist the big data fabric with aggregation, transformation, and curation processing.




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› A standalone big data fabric solution. the vendors included in this evaluation provide a software solution that organizations can implement independent of hadoop distribution and the analytics/visualization tool. the solution should not be technologically tied or bundled to any particular application, product, or solution. the vendor must market the big data fabric as a standalone product or solution. the solution can run on cloud and/or on-premises platforms.

› Big data use cases. the solution must be able to support big data use cases such as customer churn, the iot, 360-degree views of customers and the business, advanced analytics, real-time analytics, and others.

› A referenceable install base. there should be 10 or more unique enterprise paying customers using the big data fabric product that span more than one major geographical region. Each vendor also provided at least two customer references who Forrester interviewed.

› A publicly available product. the participating vendors must have actively marketed a big data fabric product as of August 1, 2016.

› Customer interest. Forrester included only those vendors that customers mentioned during Forrester inquiry calls during the past 12 months related to big data fabric topics.

› Client inquiries and/or technologies that put the vendor on Forrester’s radar. Forrester clients often discuss the vendors and products through inquiries and interviews; alternatively, the vendor may, in Forrester’s judgment, warrant inclusion or exclusion in this evaluation because of technology trends and market presence.




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FIGUrE 1 Evaluated Vendors: product information

Vendor

Denodo Technologies

Global IDs

IBM

Informatica

Oracle

Paxata

SAP

Syncsort

Talend

Trifacta

Waterline Data

Product evaluated

Denodo Platform

Data Ecosystem Management Suite

IBM InfoSphere Information Server Enterprise Edition

Informatica Platform

GoldenGate for Big DataOracle Data Integrator for Big DataOracle Big Data Preparation Cloud ServiceOracle Big Data Discovery Cloud ServiceOracle Stream AnalyticsOracle Enterprise Metadata ManagementBig Data Cloud ServiceBig Data SQL Cloud Service

Adaptive Information Platform

SAP Hana VoraSAP HanaSAP BWSAP IQSAP EIM (multiple products)

DMX-h and Ironstream

Talend Data Fabric

Trifacta Wrangler Enterprise Fall ’16 release

Waterline Data Catalog

Product versionevaluated

6.0

9

11.5

10.1

12.2.0.1.112.2.1.2.0

12.2.1.112.2.1.1

1.3SP 12

716

o9 and 1.4

6.2

3




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FIGUrE 1 Evaluated Vendors: product information (cont.)

Forrester included providers who met the following inclusion criteria:

• A comprehensive big data fabric offering. The vendors included in this evaluation must provide bigdata fabric functions as defined in the “Big Data Fabric” Forrester report published in March 2016. These include functions such as access, discovery, transformation, integration, security, governance, lineage, and orchestration of big data sources to support big data workloads and use cases. It must be able to process and curate large amounts of structured, semi-structured, and unstructured data stored in big data platforms such as Apache Hadoop, MPP EDW, NoSQL, Apache Spark, in-memory technologies and other related commercial and open source including Apache projects. In addition, it must leverage big data technologies such as Apache Spark, Apache Hadoop, and in-memory as compute and storage layer to assist big data fabric with aggregation, transformation, and curation processing.

• A standalone big data fabric solution. The vendors included in this evaluation provide a software solution that organizations can implement independent of Hadoop distribution and the analytics/visualization tool. The solution should not be technologically tied or bundled to any particular application, product or solution. The vendor must market the big data fabric or big data fabric like a standalone product or solution. The solution can run on cloud and/or on-premises platforms.

• Big data use cases. The solution must be able to support big data use cases such as customer churn,IoT, 360-degree view of customer and business, advanced analytics, real-time analytics and others.

• A referenceable install base. There should be 10 or more unique enterprise paying customers usingthe big data fabric product that span more than one major geographical region. Each vendor must provide at least two customer references that will be interviewed by Forrester.

• A publicly available product. The participating vendors must have actively marketed their big datafabric product as of August 1, 2016.

• Customer interest. Forrester plans to include only vendors that have been mentioned by customersduring Forrester inquiry calls during the past 12 months related to big data fabric topics.

• Client inquiries and/or technologies that put the vendor on Forrester’s radar. Forrester clients oftendiscuss the vendors and products through inquiries and interviews; alternatively, the vendor may, in Forrester’s judgment, warrant inclusion or exclusion in this evaluation because of technology trends and market presence.

Forrester reserves the rights to include or exclude any vendor.

Vendor inclusion criteria

larger providers have An Edge With A Broader range of Functionality

Forrester’s evaluation of big data fabric vendors uncovered a market with four leaders, six strong performers, and one contender (see Figure 2):




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› Informatica, IBm, oracle, and Talend are Leaders. these vendors offer more comprehensive, scalable platforms with broader use-case support. Each has a sweet spot enabling it to compete vigorously in the market. they have had strong offerings in the traditional data integration space and have been quick to expand their platform to leverage big data technologies. EA pros often shortlist informatica for its integration capabilities, but over the past two years it has extended its platform to support a broader big data fabric that appeals to many enterprises. iBM’s strong data and information management offering, including its broad range of database, hadoop, and integration services, helps deliver the big data fabric. oracle offers a scalable fabric software and appliance. it continues to expand its existing data platform to support big data use cases, leveraging its high-performance hadoop loader, open source integration, and big data appliance. talend offers a big data fabric that delivers high scale and performance and supports various big data use cases.

› Denodo, Global IDs, Paxata, SAP, Syncsort, and Trifacta are Strong Performers. strong performers can still be a strong choice, especially if price/performance, broader big-data-as-a-service, integration-as-a-service, and big data appliances are important. Denodo’s mature data virtualization technology broadens its coverage to support big data fabric use cases. Global iDs leverages its core expertise in data discovery, governance, metadata, and data quality to support various use cases. paxata’s platform has been expanding. it is built on Apache spark and optimized to run in hadoop, leveraging distributing computing and machine learning. sAp’s hana Vora supports big data initiatives by combining in-memory, spark, hadoop, and integration services in a unique platform. syncsort’s solution supports new big data use cases by leveraging technologies to collect, integrate, sort, and distribute data. trifacta’s data prep software continues to expand to support big data fabric, leveraging machine learning, sophisticated transformations, discovery, and enrichment.

› Waterline Data is a Contender. Waterline provides a niche solution focused on the enterprise data catalog space, but it is not a complete data fabric solution. customers often use Waterline Data with other vendor solutions, such as data prep software to support big data fabric deployments.




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FIGUrE 2 Forrester Wave™: Big Data Fabric, Q4 ’16

Challengers Contenders LeadersStrong

Performers

StrategyWeak Strong

Currentoffering

Weak

Strong

Go to Forrester.comto download the Forrester Wave tool for more detailed product evaluations, feature comparisons, and customizable rankings.

Market presence

Full vendor participation

Denodo Technologies

Global IDs

IBM

Informatica

Oracle

Paxata

SAP

Syncsort

Talend

Trifacta

Waterline Data




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FIGUrE 2 Forrester Wave™: Big Data Fabric, Q4 ’16 (cont.)

All scores are based on a scale of 0 (weak) to 5 (strong).

Global

IDs

IBM

Infor

mat

ica

Oracle

Paxat

a

SAPSyn

csor

t

Talen

d

2.38

2.00

2.00

2.00

2.60

2.80

2.50

3.00

3.00

3.00

3.00

3.00

1.65

1.00

2.00

2.00

2.00

Denod

o Te

chno

logies

3.54

3.00

4.00

4.00

4.20

3.00

3.00

3.30

3.00

3.00

4.00

3.00

2.50

2.00

3.00

3.00

2.00

weight

ing

Forre

ster’s

50%

10%

15%

15%

20%

20%

20%

50%

35%

30%

30%

5%

0%

35%

30%

20%

15%

4.08

3.00

3.00

3.00

5.00

4.40

5.00

4.05

4.00

4.00

4.00

5.00

4.00

4.00

4.00

4.00

4.00

4.53

4.00

4.00

5.00

5.00

4.40

4.50

4.05

4.00

4.00

4.00

5.00

4.45

4.00

5.00

4.00

5.00

3.57

3.00

4.00

3.00

4.60

3.00

3.50

3.70

4.00

3.00

4.00

4.00

3.65

4.00

4.00

3.00

3.00

4.00

3.00

4.00

4.00

4.60

4.40

3.50

3.00

3.00

3.00

3.00

3.00

2.40

2.00

2.00

4.00

2.00

2.93

3.00

3.00

4.00

3.00

2.40

2.50

3.60

3.00

4.00

4.00

3.00

3.00

3.00

3.00

3.00

3.00

3.19

4.00

3.00

2.00

3.80

2.40

4.00

3.00

3.00

3.00

3.00

3.00

2.70

2.00

3.00

4.00

2.00

4.13

4.00

4.00

3.00

5.00

4.40

4.00

3.65

4.00

4.00

3.00

3.00

3.65

3.00

5.00

4.00

2.00

Trifa

cta

Wat

erlin

e

Data

3.72

2.00

4.00

4.00

4.20

4.40

3.00

3.00

3.00

3.00

3.00

3.00

2.85

2.00

3.00

4.00

3.00

2.15

1.00

0.00

2.50

3.00

3.60

1.75

2.60

2.00

3.00

3.00

2.00

1.65

1.00

2.00

2.00

2.00

Current offering

Data ingestion

Data orchestration

Data discovery

Data management

Fabric data access

Fabric management

Strategy

Ability to execute

Road map

Vision

Professional services

Market presence

Product revenue

Customer base

Market awareness

Partner ecosystem

Vendor profiles

Whether they are a leader, strong performer, or contender, every big data fabric vendor in this Forrester Wave offers a credible solution to support new and emerging use cases. this evaluation of the big data fabric market is intended to be a starting point only. We encourage clients to view the detailed product evaluations and adapt the criteria weightings to fit their individual needs through




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the Forrester Wave Excel-based vendor comparison tool. clients can also schedule an inquiry to have a conversation about the market and specific vendor products to discuss specific business and technology requirements.

Leaders

› IBm differentiates with its broad information management capabilities. iBM is known for its strong data and information management offering, and now the company is extending it to support big data fabric deployments. unlike other big data fabric vendors, iBM provides its own hadoop distribution, yet it also provides connectors to support connectivity to hadoop and spark ecosystems. iBM’s key strengths lie in high-end scalability, support for complex data issues, end-to-end big data governance, integrated metadata, and granular security and privacy controls. in addition, several reference customers mentioned that iBM Global Business services helped them implement a big data fabric quicker through customized models, access patterns, and integration with existing analytical tooling. iBM is a good fit for enterprises that have complex legacy data, have multiple data lakes, require tight security controls, and want to leverage a hybrid platform.

› Informatica provides a big data fabric with all the trimmings. With more than 7,000 firms using informatica for their information management initiatives, its technology is proven and mature. informatica’s strength lies in increasing developer productivity via its intuitive visual and metadata-driven development environment, which developers can leverage for big data sources and prebuilt parsers, transformers, and connectors that help parse, integrate, cleanse, mask, and match data natively on hadoop. it also supports the reuse of workflow pipelines to support other infrastructures. informatica provides an enterprise information catalog, which catalogs data assets across the enterprise using an inferred understanding of the data as well as crowdsourced input from business analysts, stewards, and architects. Enterprises use informatica’s big data fabric solutions to deliver enterprise data lakes for real-time analytics, iot, integrated analytics, and real-time operational intelligence like fraud detection and proactive customer engagement.

› Talend offers a compelling, flexibly priced big data fabric solution. the talend big data fabric combines several technologies to deliver a common set of easy-to-use tools for real-time, batch, or dynamic integration running in on-premises, cloud, or hybrid environments. talend platform for Big Data simplifies the process of working with hadoop and spark distributions, requiring no coding to perform various activities. in the Eclipse-based talend user interface, you can drag, drop, and configure graphical components representing hadoop-related data transformation and data quality operations and natively connect to applications, databases, NosQl, and the iot. talend automatically generates the corresponding native spark or Mapreduce code for transforming data using the hadoop cluster. however, data preparation, discovery, and self-service are still emerging functionality compared with leading big data fabric vendors.




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› oracle offers a viable and scalable big data fabric solution. oracle’s GoldenGate replication solution provides real-time capabilities, integrating with oracle Data integrator tools to deliver a unified development experience. it also supports real-time big data integration to dynamically push data into the hDFs, hBase, hive, Flume, storm, and Kafka big data frameworks. oracle Big Data sQl provides data federation with hadoop; oracle Big Data connectors deliver a high-performance hadoop to oracle Database loader and enables optimized analysis using oracle’s distribution of open source r directly on hadoop data. oracle’s key strengths lie in its security and governance capabilities, highly scalable data movement and transformations, and tight integration with oracle Big Data Appliance. its customers use big data fabric to support various use cases, including real-time analytics across disparate data sources (such as data lakes), customer intelligence, iot applications, and other big data applications and insights.

Strong Performers

› Denodo Technologies extends its platform to support big data fabric. unlike other large software vendors in this evaluation, Denodo is a pure-play data virtualization vendor now extending the platform to support big data initiatives. today, several enterprises are leveraging Denodo to support big data fabric deployments — such as virtual big data marts, big data analytics, real-time analytics, and iot data processing — in various vertical industries. Denodo’s key strength is delivering a unified and centralized data services fabric with security and real-time integration across multiple traditional and big data sources, including hadoop, NosQl, cloud, and software-as-a-service (saas). customers like its easy-to-use, simple yet sophisticated data modeling capabilities, search, and support for various big data sources.

› Global IDs offers a viable big data fabric solution for all enterprises. Global iDs has been providing data management solutions to retailers, financial services, telcos, pharmaceuticals, and healthcare companies for more than 15 years. it addresses the data ecosystem problem by leveraging its core expertise in data discovery, governance, profiling, lineage, and quality. Enterprises can deploy the product in on-premises, cloud, and hybrid environments, and it is optimized for performance on the hadoop ecosystem. Business analysts can contribute business terms and metadata within the product and focus on technology-management-business collaboration. Global iDs provides extensive metadata functionality in its products to support end-to-end big data fabric deployments. Enterprises with complex big data platforms that need powerful metadata management and lineage should look at Global iDs.

› Paxata offers easy-to-use big data fabric focusing on self-service. paxata’s information platform provides an interactive, analyst-centric data preparation solution that is powered by a unified set of technologies designed to support data integration, quality, governance, collaboration, and enrichment. Machine learning algorithms help business analysts easily understand, categorize, integrate, and connect data more quickly. the platform is built on Apache spark and optimized to run in the hadoop environment, leveraging distributed computing, machine learning, and visual workspace. paxata focuses on delivering an easy-to-use solution that eliminates the need for




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coding, scripting, and sampling. Enterprises are using paxata to support ad hoc, operational, predictive, and real-time analytics. however, customers report that paxata’s integration with a few traditional and legacy data sources is not optimized.

› SAP Hana Vora extends the SAP platform to support big data fabric. sAp offers a comprehensive data management framework to support data access, data movement, data quality, transformation, and integration. And with sAp hana Vora, it extends the platform to support big data initiatives, including those for hadoop, spark, NosQl, and in-memory computing fabrics. sAp hana Vora couples tightly with Apache spark to expose Vora data and processing to spark. Enterprises can deploy machine learning algorithms in hana directly or to spark. in addition, organizations can distribute data preparation operations such as sorting, joining, and aggregation across hana and spark clusters. Enterprises use sAp’s big data fabric to support various use cases, including a 360-degree view of the customer, fraud detection, iot, and real-time insights.

› Syncsort offers a scalable big data fabric solution. syncsort provides a big data fabric solution that focuses on simplifying the process of collecting, integrating, sorting, and distributing enterprise data to deliver actionable insights, while requiring fewer resources. syncsort’s top use cases for big data fabric include leveraging data from mainframes and other traditional systems in hadoop, while ensuring data lineage, security, and efficiency. syncsort allows enterprises to deploy a full-featured Etl environment on premise and on AWs Ec2, Amazon Elastic Mapreduce, and Google cloud platform, with forthcoming support for Microsoft Azure. Data transformations are defined in a visual, wizard style Gui, and the same jobs can be executed natively in Mapreduce, spark, or stand-alone servers, without any changes. Although DMX-h does not ship with built-in machine learning capabilities, they can be included as task extensions and custom functions as part of the data flows. syncsort is still expanding its self-service capabilities.

› Trifacta’s solution makes self-service big data fabric easy to deploy. trifacta’s self-service data preparation software enables enterprises to easily explore, transform, and join together raw and diverse data sources into clean and structured outputs for a variety of analytic purposes. trifacta leverages machine learning algorithms to automate and simplify the interaction with data, making data wrangling a self-service process for analysts and business users. the vendor supports batch and on-demand natively and continuous ingestion through integrations with partners streamsets and Google Dataflow. it has extensive metadata management directly within the application and through integrations with partners such as cloudera Navigator, Apache Atlas, Waterline Data, and Alation. trifacta visually tracks and presents the lineage of data transformation steps for specific data sets and across multi-data-set-wrangling workflows. however, enterprises are reporting that trifacta lacks high-end scalable big data fabric deployments.




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Contenders

› Waterline Data focuses on delivering a Smart Data Catalog for big data environments. Waterline Data accelerates data discovery, governance, and time-to-value through its smart Data catalog, which automates the cataloging of all data lake assets. it empowers business analysts and data scientists to find, understand, and provision trusted data to extract insights and create accurate business decisions without coding and manual exploration. in addition to automated discovery, it also enables business analyst communities to crowdsource tagging and annotations and allows data stewards to curate the data catalog using an agile approach. Waterline ensures that the catalog is up to date by detecting changes and automatically cataloging new and updated data assets including curated business metadata and data lineage. While Waterline supports on-premises and cloud, hybrid is currently planned in a future release.

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supplemental Material

online resource

the online version of Figure 2 is an Excel-based vendor comparison tool that provides detailed product evaluations and customizable rankings.

Data Sources Used In This Forrester Wave

Forrester used a combination of 32 data sources to assess the strengths and weaknesses of each solution:

› Vendor surveys. Forrester surveyed vendors on their capabilities as they relate to the evaluation criteria. once we analyzed the completed vendor surveys, we conducted vendor calls where necessary to gather details of vendor qualifications.

› Product briefings and demos. We asked vendors to conduct briefings and demonstrations of their product’s functionality. We used findings from these product briefings and demos to validate details of each vendor’s product capabilities.

› Customer reference calls. to validate product and vendor qualifications, Forrester also conducted reference calls or conducted surveys with at least one of each vendor’s current customers.

The Forrester Wave methodology

We conduct primary research to develop a list of vendors that meet our criteria to be evaluated in this market. From that initial pool of vendors, we then narrow our final list. We choose these vendors based on: 1) product fit; 2) customer success; and 3) Forrester client demand. We eliminate vendors that have limited customer references and products that don’t fit the scope of our evaluation.

After examining past research, user need assessments, and vendor and expert interviews, we develop the initial evaluation criteria. to evaluate the vendors and their products against our set of criteria, we gather details of product qualifications through a combination of lab evaluations, questionnaires, demos, and/or discussions with client references. We send evaluations to the vendors for their review, and we adjust the evaluations to provide the most accurate view of vendor offerings and strategies.

We set default weightings to reflect our analysis of the needs of large user companies — and/or other scenarios as outlined in the Forrester Wave document — and then score the vendors based on a clearly defined scale. these default weightings are intended only as a starting point, and we encourage readers to adapt the weightings to fit their individual needs through the Excel-based tool. the final scores generate the graphical depiction of the market based on current offering, strategy, and market presence. Forrester intends to update vendor evaluations regularly as product capabilities and vendor strategies evolve. For more information on the methodology that every Forrester Wave follows, go to http://www.forrester.com/marketing/policies/forrester-wave-methodology.html.




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Endnotes1 increasing data volume is creating new challenges in integration, security, curation, administration, and governance.

Business users want real-time trusted data to make accurate business decisions, while technology management wants to simplify administration and lower costs. closing the big data platform gap is the goal of the emerging collection of technologies that Forrester calls big data fabric. Enterprise architects should look at big data fabric to accelerate their big data initiatives, monetize big data sources, and respond more quickly to business needs and competitive threats. see the Forrester report “Big Data Fabric Drives innovation And Growth.”

2 Mpp: massively parallel processing; EDW: enterprise data warehouse.


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The Forrester Wave Big Data Fabric Q4 2016

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