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Home > Data > Data Processing > Securing Hadoop
Securing Hadoop
Securing Hadoop

Securing Hadoop: Implement robust end-to-end security for your Hadoop ecosystem

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Securing Hadoop

Chapter 1. Hadoop Security Overview

Like any development project, the ones in Hadoop start with proof of concept (POC). Especially because the technology is new and continuously evolving, the focus always begins with figuring out what it can offer and how to leverage it to solve different business problems, be it consumer analysis, breaking news processing, and so on. Being an open source framework, it has its own nuances and requires a learning curve. As these POCs mature and move to pilot and then to production phase, a new infrastructure has to be set up. Then questions arise around maintaining the newly setup infrastructure, including questions on data security and the overall ecosystem's security. Few of the questions that the infrastructure administrators and security paranoids would ask are:

How secure is a Hadoop ecosystem? How secure is the data residing in Hadoop? How would different teams including business analysts, data scientists, developers, and others in the enterprise access the Hadoop ecosystem in a secure manner? How to enforce existing Enterprise Security Models in this new infrastructure? Are there any best practices for securing such an infrastructure?

This chapter will begin the journey to answer these questions and provide an overview of the typical challenges faced in securing Hadoop-based Big Data ecosystem. We will look at the key security considerations and then present the security reference architecture that can be used for securing Hadoop.

The following topics will be covered in this chapter:

  • Why do we need to secure a Hadoop-based ecosystem?

  • The challenges in securing such an infrastructure

  • Important security considerations for a Hadoop ecosystem

  • The reference architecture for securing a Hadoop ecosystem

Why do we need to secure Hadoop?

Enterprise data consists of crucial information related to sales, customer interactions, human resources, and so on, and is locked securely within systems such as ERP, CRM, and general ledger systems. In the last decade, enterprise data security has matured significantly as organizations learned their lessons from various data security incidents that caused them losses in billions. As the services industry has grown and matured, most of the systems are outsourced to vendors who deal with crucial client information most of the time. As a result, security and privacy standards such as HIPAA, HITECH, PCI, SOX, ISO, and COBIT have evolved . This requires service providers to comply with these regulatory standards to fully safeguard their client's data assets. This has resulted in a very protective data security enforcement within enterprises including service providers as well as the clients. There is absolutely no tolerance to data security violations. Over the last eight years of its development, Hadoop has now reached a mature state where enterprises have started adopting it for their Big Data processing needs. The prime use case is to gain strategic and operational advantages from their humongous data sets. However, to do any analysis on top of these datasets, we need to bring them to the Hadoop ecosystem for processing. So the immediate question that arises with respect to data security is, how secure is the data storage inside the Hadoop ecosystem?

The question is not just about securing the source data which is moved from the enterprise systems to the Hadoop ecosystem. Once these datasets land into the Hadoop ecosystems, analysts and data scientists perform large-scale analytics and machine-learning-based processing to derive business insights. These business insights are of great importance to the enterprise. Any such insights in the hands of the competitor or any unauthorized personnel could be disastrous to the business. It is these business insights that are highly sensitive and must be fully secured.

Any data security incident will cause business users to lose their trust in the ecosystem. Unless the business teams have confidence in the Hadoop ecosystem, they won't take the risk to invest in Big Data. Hence, the success and failure of Big Data-related projects really depends upon how secure our data ecosystem is going to be.

Challenges for securing the Hadoop ecosystem

Big Data not only brings challenges for storing, processing, and analysis but also for managing and securing these large data assets. Hadoop was not built with security to begin with. As enterprises started adopting Hadoop, the Kerberos-based security model evolved within Hadoop. But given the distributed nature of the ecosystem and wide range of applications that are built on top of Hadoop, securing Hadoop from an enterprise context is a big challenge.

A typical Big Data ecosystem has multiple stakeholders who interact with the system. For example, expert users (business analysts and data scientists) within the organization would interact with the ecosystem using business intelligence (BI) and analytical tools, and would need deep data access to the data to perform various analysis. A finance department business analyst should not be able to see the data from the HR department and so on. BI tools need a wide range of system-level access to the Hadoop ecosystem depending on the protocol and data that they use for communicating with the ecosystem.

One of the biggest challenges for Big Data projects within enterprises today is about securely integrating the external data sources (social blogs, websites, existing ERP and CRM systems, and so on). This external connectivity needs to be established so that the extracted data from these external sources is available in the Hadoop ecosystem.

Hadoop ecosystem tools such as Sqoop and Flume were not built with full enterprise grade security. Cloudera, MapR, and few others have made significant contributions towards enabling these ecosystem components to be enterprise grade, resulting in Sqoop 2, Flume-ng, and Hive Server 2. Apart from these, there are multiple security-focused projects within the Hadoop ecosystem such as Cloudera Sentry (http://www.cloudera.com/content/cloudera/en/products/cdh/sentry.html), Hortonworks Knox Gateway (http://hortonworks.com/hadoop/knox-gateway/), and Intel's Project Rhino (https://github.com/intel-hadoop/project-rhino/). These projects are making significant progress to make Apache Hadoop provide enterprise grade security. A detailed understanding of each of these ecosystem components is needed to deploy them in production.

Another area of concern within enterprises is the need the existing enterprise Identity and Access Management (IDAM) systems with the Hadoop ecosystem. With such integration, enterprises can extend the Identity and Access Management to the Hadoop ecosystem. However, these integrations bring in multiple challenges as Hadoop inherently has not been built with such enterprise integrations in mind.

Apart from ecosystem integration, there is often a need to have sensitive information within the Big Data ecosystem, to derive patterns and inferences from these datasets. As we move these datasets to the Big Data ecosystem we need to mask/encrypt this sensitive information. Traditional data masking and encryption tools don't scale well for large scale Big Data masking and encryption. We need to identify new means for encryption of large scale datasets.

Usually, as the adoption of Big Data increases, enterprises quickly move to a multicluster/multiversion scenario, where there are multiple versions of the Hadoop ecosystem operating in an enterprise. Also, sensitive data that was earlier banned from the Big Data platform slowly makes its way in. This brings in additional challenges on how we address security in such a complex environment, as a small lapse in security could result in huge financial loss for the organization.

Key security considerations

As discussed previously, to meet the enterprise data security needs for a Big Data ecosystem, a complex and holistic approach is needed to secure the entire ecosystem. Some of the key security considerations while securing Hadoop-based Big Data ecosystem are:

  • Authentication: There is a need to provide a single point for authentication that is aligned and integrated with existing enterprise identity and access management system.

  • Authorization: We need to enforce a role-based authorization with fine-grained access control for providing access to sensitive data.

  • Access control: There is a need to control who can do what on a dataset, and who can use how much of the processing capacity available in the cluster.

  • Data masking and encryption: We need to deploy proper encryption and masking techniques on data to ensure secure access to sensitive data for authorized personnel.

  • Network perimeter security: We need to deploy perimeter security for the overall Hadoop ecosystem that controls how the data can move in and move out of the ecosystem to other infrastructures. Design and implement the network topology to provide proper isolation of the Big Data ecosystem from the rest of the enterprise. Provide proper network-level security by configuring the appropriate firewall rules to prevent unauthorized traffic.

  • System security: There is a need to provide system-level security by hardening the OS and the applications that are installed as part of the ecosystem. Address all the known vulnerability of OS and applications.

  • Infrastructure security: We need to enforce strict infrastructure and physical access security in the data center.

  • Audits and event monitoring: A proper audit trial is required for any changes to the data ecosystem and provide audit reports for various activities (data access and data processing) that occur within the ecosystem.

Reference architecture for Big Data security

Implementing all the preceding security considerations for the enterprise data security becomes very vital to building a trusted Big Data ecosystem within the enterprise. The following figure shows as a typical Big Data ecosystem and how various ecosystem components and stakeholders interact with each other. Implementing the security controls in each of these interactions requires elaborate planning and careful execution.

The reference architecture depicted in the following diagram summarizes the key security pillars that needs to be considered for securing a Big Data ecosystem. In the next chapters, we will explore how to leverage the Hadoop security model and the various existing enterprise tools to secure the Big Data ecosystem.

In Chapter 4, Securing the Hadoop Ecosystem, we will look at the implementation details to secure the OS and applications that are deployed along with Hadoop in the ecosystem. In Chapter 5, Integrating Hadoop with Enterprise Security Systems, we look at the corporate network perimeter security requirement and how to secure the cluster and look at how authorization defined within the enterprise identity management system can be integrated with the Hadoop ecosystem. In Chapter 6, Securing Sensitive Data in Hadoop, we look at the encryption implementation for securing sensitive data in Hadoop. In Chapter 7, Security Event and Audit Logging in Hadoop, we look at security incidents and event monitoring along with the security policies required to address the audit and reporting requirements.

Summary

In this chapter, we understood the overall security challenges for securing Hadoop-based Big Data ecosystem deployments. We looked at the two different types (source and insights) of data that is stored in the Hadoop ecosystem and how important it is to secure these datasets to retain business confidence. We detailed out the key security considerations for securing Hadoop, and presented the overall security reference architecture that can be used as a guiding light for the overall security design of a Big Data ecosystem. In the rest of the book, we will use this reference architecture as a guide to implement the Hadoop-based secured Big Data ecosystem.

In the next chapter, we will look in depth at the Kerberos security model and how this is deployed in a secured Hadoop cluster. We will look at the Hadoop security model in detail and understand the key design considerations based on the current Hadoop security implementation.

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Key benefits

  • Master the key concepts behind Hadoop security as well as how to secure a Hadoop-based Big Data ecosystem
  • Understand and deploy authentication, authorization, and data encryption in a Hadoop-based Big Data platform
  • Administer the auditing and security event monitoring system

Description

Security of Big Data is one of the biggest concerns for enterprises today. How do we protect the sensitive information in a Hadoop ecosystem? How can we integrate Hadoop security with existing enterprise security systems? What are the challenges in securing Hadoop and its ecosystem? These are the questions which need to be answered in order to ensure effective management of Big Data. Hadoop, along with Kerberos, provides security features which enable Big Data management and which keep data secure. This book is a practitioner's guide for securing a Hadoop-based Big Data platform. This book provides you with a step-by-step approach to implementing end-to-end security along with a solid foundation of knowledge of the Hadoop and Kerberos security models. This practical, hands-on guide looks at the security challenges involved in securing sensitive data in a Hadoop-based Big Data platform and also covers the Security Reference Architecture for securing Big Data. It will take you through the internals of the Hadoop and Kerberos security models and will provide detailed implementation steps for securing Hadoop. You will also learn how the internals of the Hadoop security model are implemented, how to integrate Enterprise Security Systems with Hadoop security, and how you can manage and control user access to a Hadoop ecosystem seamlessly. You will also get acquainted with implementing audit logging and security incident monitoring within a Big Data platform.

Who is this book for?

This book is great for Hadoop practitioners (solution architects, Hadoop administrators, developers, and Hadoop project managers) who are looking to get a good grounding in what Kerberos is all about and who wish to learn how to implement end-to-end Hadoop security within an enterprise setup. It's assumed that you will have some basic understanding of Hadoop as well as be familiar with some basic security concepts.

What you will learn

  • Understand the challenges of securing Hadoop and Big Data and master the reference architecture for Big Data security
  • Demystify Kerberos and the Hadoop security model
  • Learn the steps to secure a Hadoop platform with Kerberos
  • Integrate Enterprise Security Systems with Hadoop security and build an integrated security model
  • Get detailed insights into securing sensitive data in a Hadoop Big Data platform
  • Implement audit logging and a security event monitoring system for your Big Data platform
  • Discover the various industry tools and vendors that can be used to build a secured Hadoop platform
  • Recognize how the various Hadoop components interact with each other and what protocols and security they implement
  • Design a secure Hadoop infrastructure and implement the various security controls within the enterprise
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Publication date, Length, Edition, Language, ISBN-13
Publication date : Nov 22, 2013
Length: 116 pages
Edition : 1st
Language : English
ISBN-13 : 9781783285259
Vendor :
Apache
Category :
Data
Languages :
Java
Concepts :
Data Processing
Tools :
Hadoop

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Edition : 1st
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ISBN-13 : 9781783285259
Vendor :
Apache
Category :
Data
Languages :
Java
Concepts :
Data Processing
Tools :
Hadoop

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Table of Contents

7 Chapters
Hadoop Security Overview Chevron down icon Chevron up icon
Hadoop Security Overview
Why do we need to secure Hadoop?
Challenges for securing the Hadoop ecosystem
Key security considerations
Summary
Hadoop Security Design Chevron down icon Chevron up icon
Hadoop Security Design
What is Kerberos?
The Hadoop default security model without Kerberos
Hadoop Kerberos security implementation
Summary
Setting Up a Secured Hadoop Cluster Chevron down icon Chevron up icon
Setting Up a Secured Hadoop Cluster
Prerequisites
Setting up Kerberos
Configuring Hadoop with Kerberos authentication
Configuring users for Hadoop
Automation of a secured Hadoop deployment
Summary
Securing the Hadoop Ecosystem Chevron down icon Chevron up icon
Securing the Hadoop Ecosystem
Configuring Kerberos for Hadoop ecosystem components
Best practices for securing the Hadoop ecosystem components
Summary
Integrating Hadoop with Enterprise Security Systems Chevron down icon Chevron up icon
Integrating Hadoop with Enterprise Security Systems
Integrating Enterprise Identity Management systems
Accessing a secured Hadoop cluster from an enterprise network
Summary
Securing Sensitive Data in Hadoop Chevron down icon Chevron up icon
Securing Sensitive Data in Hadoop
Securing sensitive data in Hadoop
Summary
Security Event and Audit Logging in Hadoop Chevron down icon Chevron up icon
Security Event and Audit Logging in Hadoop
Security Incident and Event Monitoring in a Hadoop Cluster
Setting up audit logging in a secured Hadoop cluster
Summary

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Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.5
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Jeff Feb 13, 2014
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Great presentation of information on how to secure Hadoop. Covers all relevant areas of Hadoop security in great detail while offering solid best practices.
Amazon Verified review Amazon
MiaMadea Mar 05, 2014
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
Before reading the book, i had a minimal understanding of Hadoop. let alone knowing how to secure it. The book was comprehensive regarding security configurations, yet was straightforward to understand if you have a basic knowledge of Hadoop. I now refer to the book occasionally when reviewing Hadoop implementations.The reader should be aware that different vendors offering Hadoop (Cloudera, IBM, etc) made have their own extensions of Hadoop and thus the security features available may differ from what is described in the book.
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About the author

Profile icon Sudheesh Narayan
Sudheesh Narayan
Sudheesh Narayanan is a Technology Strategist and Big Data Practitioner with expertise in technology consulting and implementing Big Data solutions. With over 15 years of IT experience in Information Management, Business Intelligence, Big Data & Analytics, and Cloud & J2EE application development, he provided his expertise in architecting, designing, and developing Big Data products, Cloud management platforms, and highly scalable platform services. His expertise in Big Data includes Hadoop and its ecosystem components, NoSQL databases (MongoDB, Cassandra, and HBase), Text Analytics (GATE and OpenNLP), Machine Learning (Mahout, Weka, and R), and Complex Event Processing. Sudheesh is currently working with Genpact as the Assistant Vice President and Chief Architect – Big Data, with focus on driving innovation and building Intellectual Property assets, frameworks, and solutions. Prior to Genpact, he was the co-inventor and Chief Architect of the Infosys BigDataEdge product.
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The orders shipped to the countries that are listed under EU27 will not bear custom charges. They are paid by Packt as part of the order.

List of EU27 countries: www.gov.uk/eu-eea:

A custom duty or localized taxes may be applicable on the shipment and would be charged by the recipient country outside of the EU27 which should be paid by the customer and these duties are not included in the shipping charges been charged on the order.

How do I know my custom duty charges? Chevron down icon Chevron up icon

The amount of duty payable varies greatly depending on the imported goods, the country of origin and several other factors like the total invoice amount or dimensions like weight, and other such criteria applicable in your country.

For example:

  • If you live in Mexico, and the declared value of your ordered items is over $ 50, for you to receive a package, you will have to pay additional import tax of 19% which will be $ 9.50 to the courier service.
  • Whereas if you live in Turkey, and the declared value of your ordered items is over € 22, for you to receive a package, you will have to pay additional import tax of 18% which will be € 3.96 to the courier service.
How can I cancel my order? Chevron down icon Chevron up icon

Cancellation Policy for Published Printed Books:

You can cancel any order within 1 hour of placing the order. Simply contact customercare@packt.com with your order details or payment transaction id. If your order has already started the shipment process, we will do our best to stop it. However, if it is already on the way to you then when you receive it, you can contact us at customercare@packt.com using the returns and refund process.

Please understand that Packt Publishing cannot provide refunds or cancel any order except for the cases described in our Return Policy (i.e. Packt Publishing agrees to replace your printed book because it arrives damaged or material defect in book), Packt Publishing will not accept returns.

What is your returns and refunds policy? Chevron down icon Chevron up icon

Return Policy:

We want you to be happy with your purchase from Packtpub.com. We will not hassle you with returning print books to us. If the print book you receive from us is incorrect, damaged, doesn't work or is unacceptably late, please contact Customer Relations Team on customercare@packt.com with the order number and issue details as explained below:

  1. If you ordered (eBook, Video or Print Book) incorrectly or accidentally, please contact Customer Relations Team on customercare@packt.com within one hour of placing the order and we will replace/refund you the item cost.
  2. Sadly, if your eBook or Video file is faulty or a fault occurs during the eBook or Video being made available to you, i.e. during download then you should contact Customer Relations Team within 14 days of purchase on customercare@packt.com who will be able to resolve this issue for you.
  3. You will have a choice of replacement or refund of the problem items.(damaged, defective or incorrect)
  4. Once Customer Care Team confirms that you will be refunded, you should receive the refund within 10 to 12 working days.
  5. If you are only requesting a refund of one book from a multiple order, then we will refund you the appropriate single item.
  6. Where the items were shipped under a free shipping offer, there will be no shipping costs to refund.

On the off chance your printed book arrives damaged, with book material defect, contact our Customer Relation Team on customercare@packt.com within 14 days of receipt of the book with appropriate evidence of damage and we will work with you to secure a replacement copy, if necessary. Please note that each printed book you order from us is individually made by Packt's professional book-printing partner which is on a print-on-demand basis.

What tax is charged? Chevron down icon Chevron up icon

Currently, no tax is charged on the purchase of any print book (subject to change based on the laws and regulations). A localized VAT fee is charged only to our European and UK customers on eBooks, Video and subscriptions that they buy. GST is charged to Indian customers for eBooks and video purchases.

What payment methods can I use? Chevron down icon Chevron up icon

You can pay with the following card types:

  1. Visa Debit
  2. Visa Credit
  3. MasterCard
  4. PayPal
What is the delivery time and cost of print books? Chevron down icon Chevron up icon

Shipping Details

USA:

'

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela