Cloud providers come in all shapes and sizes and offer many different products for big data. Some are household names while others are recently emerging. Some of the cloud providers that offer IaaS services that can be used for big data include Amazon.com, AT&T, GoGrid, Joyent, Rackspace, IBM, and Verizon/Terremark.
Amazon's Public Elastic Compute Cloud for big data
Currently, one of the most high-profile IaaS service providers is Amazon web Services with its Elastic Compute Cloud (Amazon EC2). Amazon didn't start out with a vision to build a big infrastructure services business.
Instead, the company built a massive infrastructure to support its own retail business and discovered that its resources were underused. Instead of allowing this asset to sit idle, it decided to leverage this resource while adding to the bottom line. Amazon's EC2 service was launched in 2006 and continues to evolve.
Amazon EC2 offers scalability under the user's control, with the user paying for resources by the hour. The use of the term elastic in the naming of Amazon's EC2 is significant. Here, elasticity refers to the capability that the EC2 users have to increase or decrease the infrastructure resources assigned to meet their needs.
Amazon also offers other big data services to customers of its Amazon web Services portfolio. These include the following:
• Amazon Elastic MapReduce: Targeted for processing huge volumes of data. Elastic MapReduce utilizes a hosted Hadoop framework running on EC2 and Amazon Simple Storage Service (Amazon S3). Users can now run HBase.
• Amazon DynamoDB: A fully managed not only SQL (NoSQL) database service. DynamoDB is a fault tolerant, highly available data storage service offering self-provisioning, transparent scalability, and simple administration. It is implemented on SSDs (solid state disks) for greater reliability and high performance.
• Amazon Simple Storage Service (S3): A web-scale service designed to store any amount of data. The strength of its design center is performance and scalability, so it is not as feature laden as other data stores. Data is stored in "buckets" and you can select one or more global regions for physical storage to address latency or regulatory needs.
• Amazon High Performance Computing: Tuned for specialized tasks, this service provides low-latency tuned high performance computing clusters. Most often used by scientists and academics, HPC is entering the mainstream because of the offering of Amazon and other HPC providers. Amazon HPC clusters are purpose built for specific workloads and can be reconfigured easily for new tasks.
• Amazon RedShift: Available in limited preview, RedShift is a petabyte-scale data warehousing service built on a scalable MPP architecture. Managed by Amazon, it offers a secure, reliable alternative to in-house data warehouses and is compatible with several popular business intelligence tools.
Google big data services
Google, the Internet search giant, also offers a number of cloud services targeted for big data. These include the following:
• Google Compute Engine: A cloud-based capability for virtual machine computing, Google Compute Engine offers a secure, flexible computing environment from energy efficient data centers. Google also offers workload management solutions from several technology partners who have optimized their products for Google Compute Engine.
• Google Big Query: Allows you to run SQL-like queries at a high speed against large data sets of potentially billions of rows. Although it is good for querying data, data cannot be modified after it is in it. Consider Google Big Query a sort of Online Analytical Processing (OLAP) system for big data. It is good for ad hoc reporting or exploratory analysis.
• Google Prediction API: A cloud-based, machine learning tool for vast amounts of data, Prediction is capable of identifying patterns in data and then remembering them. It can learn more about a pattern each time it is used. The patterns can be analyzed for a variety of purposes, including fraud detection, churn analysis, and customer sentiment.
Microsoft Azure for big data
Based on Windows and SQL abstractions, Microsoft has productized a set of development tools, virtual machine support, management and media services, and mobile device services in a PaaS offering. For customers with deep expertise in .Net, SQLServer, and Windows, the adoption of the Azure-based PaaS is straightforward.
To address the emerging requirements to integrate big data into Windows Azure solutions, Microsoft has also added Windows Azure HDInsight. Built on Hortonworks Data Platform (HDP), which according to Microsoft, offers 100 percent compatibility with Apache Hadoop, HDInsight supports connection with Microsoft Excel and other business intelligence (BI) tools. In addition to Azure HDInsight can also be deployed on Windows Server.
OpenStack for big data
Initiated by Rackspace and NASA, OpenStack is implementing an open-cloud platform aimed at either public or private clouds. While the organization is tightly managed by Rackspace, it moved to a separate OpenStack foundation. Although companies can leverage OpenStack to create proprietary implementations, the OpenStack designation requires conformance to a standard implementation of services.
OpenStack's goal is to provide a massively scaled, multitenant cloud specification that can run on any hardware. OpenStack is building a large ecosystem of partners interested in adopting its cloud platform, including Dell, HP, Intel, Cisco, Red Hat, and IBM, along with at least 100 others that are using OpenStack as the foundation for their cloud offerings.
In essence, OpenStack is an open source IaaS initiative built on Ubuntu, an operating system based on the Debian Linux distribution. It can also run on Red Hat's version of Linux.
OpenStack offers a range of services, including compute, object storage, catalog and repository, dashboarding, identity, and networking. In terms of big data, Rackspace and Hortonworks (a provider of an open source data management platform based on Apache Hadoop) announced that Rackspace will release an OpenStack public cloud-based Hadoop service, which will be validated and supported by Hortonworks and will enable customers to quickly create a big data environment.
About the Book Author
Judith Hurwitz is an expert in cloud computing, information management, and business strategy. Alan Nugent has extensive experience in cloud-based big data solutions. Dr. Fern Halper specializes in big data and analytics. Marcia Kaufman specializes in cloud infrastructure, information management, and analytics.
Find the right big data solution for your business or organization
Big data management is one of the major challenges facing business, industry, and not-for-profit organizations. Data sets such as customer transactions for a mega-retailer, weather patterns monitored by meteorologists, or social network activity can quickly outpace the capacity of traditional data management tools. If you need to develop or manage big data solutions, you'll appreciate how these four experts define, explain, and guide you through this new and often confusing concept. You'll learn what it is, why it matters, and how to choose and implement solutions that work.
• Effectively managing big data is an issue of growing importance to businesses,
not-for-profit organizations, government, and IT professionals
• Authors are experts in information management, big data, and a variety of solutions
• Explains big data in detail and discusses how to select and implement a solution, security concerns to consider, data storage and presentation issues, analytics, and much more
• Provides essential information in a no-nonsense, easy-to-understand style that is empowering
Big Data For Dummies cuts through the confusion and helps you take charge of big data solutions for your organization.