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Securing Big Data Infrastructure: An Evolving Market Ecosystem

Securing Big Data Infrastructure: An Evolving Market Ecosystem

The Big Data industry is experiencing profound changes across the entire stack including infrastructure, security, analytics, and application layer. The data management industry is shifting from host based architecture to cloud based data centric architecture and operational model, thereby creating enormous challenges on infrastructure security.

This creates an evolving market ecosystem for Big Data technology and services, infrastructure, and security solutions, software solutions, and support services. Open source solutions such as Hadoop, Cassandra, and MongoDB play a key role in the ecosystem in terms of security features. However, full infrastructure security involves initiatives of many players including general security solution providers.

This research evaluates the components of Big Data infrastructure and security framework along with security challenges related with Hadoop, Cassandra, and MongoDB. This research also analyzes the Big Data infrastructure security market ecosystem, components associated with strategic framework for Big Data protection, and vendor solution landscape.

The report also includes market projection for enterprise spending on Big Data technology and services encompassing three verticals. Forecasting includes market projections by industry verticals and geographic region. All purchases of Mind Commerce reports includes time with an expert analyst who will help you link key findings in the report to the business issues you're addressing. This needs to be used within three months of purchasing the report.

Key Findings:

Certain industries will be drivers of Big Data Security
Big Data infrastructure will require strategic governance and framework for security
Big Data infrastructure and security solution market spending alone will reach $23.7 billion by 2021
The evolving Big Data Security ecosystem consists of infrastructure, applications, APIs, and data itself
Big Data Security should be a key part of strategy within the Big Data Lifecycle Management (BDLM) model

Target Audience:

Big Data vendors
Big Data security providers
Telecom equipment providers
Global infrastructure suppliers
Communication service providers
Cloud services and datacenter companies
Big Data consulting, and advisory companies

General Methodology

Mind Commerce Publishing's research methodology encompasses input from a wide variety of sources.

We rely heavily upon our Subject Matter Experts (SME) in terms of their market knowledge, unique perspective, and vision. We utilize SME industry contacts as well as previous customers and participants in our market surveys and interactive interviews.

In addition, we rely upon our extensive internal database, which contains modeling, qualitative analysis, and quantitative data. We review secondary sources and compare to our primary sources to update previous findings (for prior version reports) and/or compile baseline information for technology and market modeling.

We share preliminary models with industry contacts (select previous clients, experts, and thought leaders) to verify the veracity of initial modeling. Prior to final report production (analysis, findings, and conclusions), we engage in an internal review with internal SMEs as well as cross-expertise, senior staff members to challenge results.

We believe that forecasts should be prepared as part of an integrated process which involves both quantitative as well as qualitative factors. We follow the following 3-step process for forecasting.

Forecasting Methodology

Step 1 - Forecasts Input: The inputs for the present and historical revenues are derived from industry players. Financial and other quantitative data for individual sub-market categories are derived from original research and tested with interviews with major industry constituents.

Step 2 - Forecasting of Future Years: Mind Commerce extends forecasts based on a variety of factors including demand drivers as well as supply side data. Key success factors and assumptions are considered.

Step 3 - Validation of Data: The final step is to validate projections, which is accomplished in consultation with both internal and external industry experts, including both topic and regional experts. Adjustments are made to the forecasts based on factors identified throughout this process.


1.0 Introduction
1.1 The Rise Of Big Data
1.2 The Big Data Stack
1.2.1 Physical Infrastructure
1.2.2 Data Collection And Integration
1.2.3 Data Security And Storage
1.2.4 Data Analytics
1.2.5 Data Consumption
1.3 Hadoop Distribution
1.3.1 Hadoop Distributed File System
1.4 Big Data Ecosystem
1.4.1 Infrastructure
1.4.2 Analytics
1.4.3 Applications
1.5 Big Data And It Enabled Services
1.6 Big Data Infrastructure
1.6.1 Infrastructure For Data Management
1.6.2 Infrastructure For Analytics
1.6.3 Infrastructure For Cloud Based Service
1.7 Security Challenge And Weakness
1.7.1 Infrastructure Security And Integrity
1.7.2 Data Security And Privacy
1.7.3 Data Management
1.7.4 Identity And Access Management
1.7.5 Network Security
1.8 Hadoop Weakness
2.0 Big Three In Big Data Security: Hadoop, Cassandra, And Mongo
2.1 Hadoop
2.1.1 Authentication
2.1.2 Service Level Authorization
2.1.3 Authentication For Http Web Consoles
2.1.4 Data Confidentiality
2.1.5 Configuration
2.1.6 Kerberos
2.1.7 Hortonworks Data Platform (Hdp)
2.1.8 Cloudera Hadoop Distribution (Cdh)
2.2 Cassandra
2.2.1 Authentication
2.2.2 Role Based Security
2.2.3 Object Permission Management
2.2.4 Database Security And Logging
2.3 Mongodb
3.0 Big Data Protection Strategies
3.1 Points To Consider In Developing Data Protection Strategy
3.2 Components To Develop Big Data Protection Strategy
4.0 Big Data Infrastructure Security Market Ecosystem
4.1 Securing Data At Rest
4.2 Securing Apis
4.3 Securing Applications
4.4 Securing Data For Analysis
4.5 Securing User Privileges
4.6 Securing Enterprise Information
4.7 Securing Data Collection And Integration
4.8 Tools To Evaluate Big Data Infrastructure Security
4.9 Security Vendor Landscape
5.0 Strategic Framework For Big Data Protection
5.1 Components For Strategic Framework
5.1.1 Federated Access And Delivery Infrastructure (Fadi)
5.1.2 Access Control
5.1.3 Xacml Policies
5.1.4 Nosql Databases
5.1.5 Encryption
5.1.6 Bootstrapping Protocol
5.2 Big Data Security Framework
5.2.1 Data Management
5.2.2 Identity And Access Management
5.2.3 Data Protection And Privacy
5.2.4 Network Security
5.2.5 Infrastructure Security And Integrity
5.3 Big Data Security Framework For Infrastructure
5.3.1 Logging / Audit
5.3.2 Secure Linux
5.3.3 Data Monitoring
5.3.4 User Monitoring
6.0 Big Data Technology And Service Market 2016 - 2021
6.1 Global Big Data Technology And Service Market Spending
6.1.1 Big Data Infrastructure And Security Solution
6.1.2 Big Data Software Solution
6.1.3 Big Data Support Services
6.2 Global Big Data Technology And Service Spending By
Industry Vertical
6.3 Big Data Technology And Service Spending By Region
7.0 Summary And Recommendations
Figures
Figure 1: Big Data across Industries and Use Cases
Figure 2: Big Data Stack
Figure 3: Complete Big Data Ecosystem
Figure 4: Big Data Infrastructure and Components
Figure 5: Big Data Lifecycle Management (BDLM)
Figure 6: Big Data Components for Analytics Infrastructure
Figure 7: Scientific Workflow into Cloud Based Infrastructure
Figure 8: Hortonworks Data Platform (HDP) Architecture
Figure 9: Cloudera Hadoop Distribution (CDH) Architecture
Figure 10: Federated Access and Delivery Infrastructure (FADI)
Figure 11: Big Data Security Framework and Components
Tables
Table 1: Global Big Data Spending on Technology and Services 2016 - 2021
Table 2: Global Big Data Technology and Service by Industry 2016 - 2021
Table 3: Big Data Technology and Service by Region 2016 - 2021

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