Book Image

Hadoop Blueprints

By : Anurag Shrivastava, Tanmay Deshpande
Book Image

Hadoop Blueprints

By: Anurag Shrivastava, Tanmay Deshpande

Overview of this book

If you have a basic understanding of Hadoop and want to put your knowledge to use to build fantastic Big Data solutions for business, then this book is for you. Build six real-life, end-to-end solutions using the tools in the Hadoop ecosystem, and take your knowledge of Hadoop to the next level. Start off by understanding various business problems which can be solved using Hadoop. You will also get acquainted with the common architectural patterns which are used to build Hadoop-based solutions. Build a 360-degree view of the customer by working with different types of data, and build an efficient fraud detection system for a financial institution. You will also develop a system in Hadoop to improve the effectiveness of marketing campaigns. Build a churn detection system for a telecom company, develop an Internet of Things (IoT) system to monitor the environment in a factory, and build a data lake – all making use of the concepts and techniques mentioned in this book. The book covers other technologies and frameworks like Apache Spark, Hive, Sqoop, and more, and how they can be used in conjunction with Hadoop. You will be able to try out the solutions explained in the book and use the knowledge gained to extend them further in your own problem space.
Table of Contents (14 chapters)
Hadoop Blueprints
About the Authors
About the Reviewers

Enterprise Hadoop

Large enterprises have traditionally stored data in data warehouse systems for reporting and analysis. These data warehouse systems store data in the order of hundreds of gigabytes, but they rarely match the scale of the storage and processing challenge Hadoop intended to take. Enterprises spend a considerable part of their budget in procuring and running ETL systems, data warehousing software and hardware required to run it. Commercial vendors of Hadoop see the opportunity to grab a share of the data warehousing spending, and increase their market share by catering to the storage and processing of big data.

Let's examine, in the next two sections, the factors which have led to the rise of Hadoop in enterprises.

Social media and mobile channels

Social media and mobile channels have emerged as the prime media through which to conduct business, and to market products and services. This trend is evident across all sectors of industry. For example, airlines use mobiles for bookings and check-ins and banks use social media such as Facebook to inform customers about their latest offerings, and to provide customer support. These channels create new kinds of customer interactions with business that happens several times per week and contain valuable information about customer behavior and preference in raw form. Analyzing this data, with the help of Hadoop, is an attractive proposition for businesses because of the lower cost of storage, and the ability to analyze data quickly.

Data storage cost reduction

Enterprise Data Warehouse Systems procured from the software vendors bring the software license costs of DBMS software, ETL tooling and schedulers with them. A resilient and high performing Enterprise data warehouse hardware setup for a Fortune 500 company could cost several million dollars. Also, 10% to 20% of procurement cost would be paid in the form of annual support services and the salary cost of operational support personnel.

Enterprise Hadoop vendors aim to derive their revenues by expecting that Hadoop can take over the storage and workload of an Enterprise Data Warehouse system in part or full, and thereby it will contribute to the reduction of the IT costs.

Open source Hadoop was not designed keeping the requirements of large enterprises in mind. Business enterprises need fine-grained security and ease of integration with other enterprise systems in Hadoop. Availability of training, and round the clock service and support, when Hadoop supports important business processes, is considered very important in enterprise adoption. Hadoop vendors emerged to fill the gaps in the Hadoop ecosystem and developed a business model to sell service and support to enterprises. They are also working on strengthening the Hadoop ecosystem to make it appealing for the enterprise market. With the help of contributions to open source Hadoop, or by developing proprietary products to enhance the appeal of their specific offering to the enterprise customers, Hadoop vendors are trying to make in roads in enterprise.

At the time of writing this book, several vendors were active in the Hadoop market as described in the next section.

Enterprise software vendors

Enterprise software vendors such as IBM, Teradata, Oracle and SAS have adopted Hadoop as the standard platform for big data processing. They are promoting Hadoop as a complimentary offering in their existing enterprise data warehouse solutions.

IBM Infosphere Big Insights product suite is one such example that packages open source Hadoop with proprietary products such as Infosphere Streams for streaming analytics, and IBM Big Sheets as a Microsoft Excel-like spreadsheet for ad-hoc analysis of data from a Hadoop cluster. IBM leverages its long experience in Enterprise Data Warehouse systems to provide the solutions for security and data lineage in Hadoop.

SAS Visual Analytics is another example in which SAS packages Hadoop as the data store for their line of analytics and visualization products. SAP positions its in-memory analytics system, SAP HANA, as the storage for high-value, often used data such as customer master data, and Hadoop as a system to store information for archiving and retrieval of weblogs, and other unstructured and unprocessed data, because storing such data in-memory on the system would be expensive, and not of much direct value.

Pure Play Hadoop vendors

Pure Play Hadoop vendors have emerged in the past six years. Vendors such as Cloudera, MapR, and Hortonworks fall in this category. These vendors are also very active contributors to open source Hadoop and its ecosystem of other tools. Despite falling into the same category, these vendors are trying to carve out their own niche in Hadoop business.

These vendors do not have a long record of accomplishment in developing and supporting enterprise software where large vendors such as IBM, SAS or SAP enjoy superiority. The familiarity of Enterprise Software vendors with complex integration and compliance challenges in large enterprises bestows on them an edge over Pure Play Hadoop vendors in the lucrative market where Pure Play vendors are relatively inexperienced.

Pure Play Hadoop vendors have a different revenue and growth model. Hortonworks, which is a spinoff company from Yahoo, focuses upon providing services on the Hadoop framework to enterprise, but also to Enterprise Software Vendors such as Microsoft, who have bundled Hadoop in their offering. Hortonworks has repackaged Apache Hadoop and related tools in a product called Hortonworks Data Platform.

Pure Play Hadoop vendor Cloudera is No. 2 in the market in terms of revenue. Cloudera has developed proprietary tools for Hadoop monitoring and data encryption. They earn a fee for licensing these products and providing support for their Hadoop distribution. They have more than 200 paying customers as of Q1 2014, some of who have deployments as large as 1,000 nodes supporting more than a petabyte of data. (Olavsrud, 2014)

MapR is another Pure Play Hadoop player. MapR lacks the aggressive marketing and presence that Hortonworks and Cloudera have. They started early on enhancing the enterprise features of Hadoop when Hadoop implementations were in their infancy in enterprises. MapR has introduced performance improvements in HBase and support for the network filesystem in Hadoop.

Pure Play Hadoop vendors may not be as dominant in enterprises as they would like to be, but they are still the driving force behind Hadoop innovations and making Hadoop a popular data platform by contributing to training courses, conferences, literature, and webinars.

Cloud Hadoop vendors

Amazon was the first company to offer Hadoop as a cloud service with Amazon EMR (Elastic MapReduce). Amazon is very successful with the EC2 service for in-cloud computing and S3 for in-cloud storage. EMR leverages the existing services of Amazon and offers to pay for actually using the model. In addition, Amazon also has Amazon Kinesis as a streaming platform and Amazon RedShift as a data warehousing platform on a cloud, which are the part of the Amazon big data roadmap.

The hosted Hadoop provided by Amazon EMR allows you to instantly provision Hadoop with the right capacity for different workloads. You can access Amazon EMR by using the AWS Management Console, Command Line Tools, SDKS, or the EMR API, which should be familiar to those who are already using the other Amazon cloud services.

Microsoft HDInsight is a Hadoop implementation on the Microsoft Azure cloud. In terms of service offering, like Amazon it leverages existing Azure services and other Microsoft applications. BI Tools such as Microsoft Excel, SQL Server Analysis Services, and SQL Server Reporting Services integrate with HDInsight. HDInsight uses the Hortonworks Data Platform (HDP) for Hadoop distribution.

These cloud-based Hadoop solutions require little setup and management effort. We can upscale or downscale the capacity based upon our workload. The relatively lower cost of initial setup make this offering very attractive for startups and small enterprises who would like to analyze big data but lack the financial resources to set up their own dedicated Hadoop infrastructure.

Despite the benefits of cloud-based Hadoop in terms of lower setup and management costs, the laws of various legal jurisdictions restrict the kind of data that can be stored in the cloud. The laws of the land also severely restrict the kind of analytics permitted on data sets if they involve customers' personal data, healthcare records or financial history. This restriction does not affect the choice of cloud-based Hadoop vendors alone, but all other Hadoop vendors too. However, storing data on a cloud outside the data center of the enterprise and in different legal jurisdictions makes compliance with privacy laws the foremost concern. To make cloud-based Hadoop successful in enterprise, t vendors need to address the compliance-related concerns in various legal jurisdictions.