• Create a virtual machine instance and select your Amazon Machine Image (AMI).
• Specify the instance size: micro, small, medium, large and so on.
• Establish the number of instances to launch based on the AMI and zone availability.
• Define the metadata tags to simplify the administration of the of EC2 instances.
• Select a key pairs to securely connect to an instance.

An EC2 instance has one public Domain Name System (DNS) and Internet Protocol (IP) address and one private DNS and IP. The public DNS can be used to connect securely to an instance via Secure Shell (SSH).

• Create a virtual machine instance and select a machine image for the instance.
• Select a zone in which the instance must be activated.
• Provide instance name, instance tags and metadata.

Every instance has a disk resource and depending on the instance the disk can be:

1. A scratch disk which is deleted when the machine instance terminates.
2. A persistent disk which persists beyond the life of an instance.

Network options allow to control instance traffic. By default, traffic between instances in the same network is enabled. You can enable other connections, using additional firewall rules.

• Create a virtual machine instance and select machine image and instance type.
• Provide user name and password or upload a certificate for securely connecting to the instance.

Any changes made to a virtual machine are persistently stored. New virtual machines can be created from previously stored machine images.

• Highly reliable, scalable, fast and fully redundant affordable storage.
• Data stored on S3 is organized in buckets. You must create a bucket before storing any data.
• Ability to upload any kind of file.
• Support for redundancy, encryption options and access permissions.

• The service uses Google’s proprietary network and datacenter technology. Google spent several years building proprietary infrastructure and technology to power Google’s sites, When you use the service, the same network goes to work for your data.
• The service replicates data to multiple data centers and serves end-user’s requests from the nearest data center that holds a copy of the data. You have a choice of regions (currently U.S. and Europe) to allow you to keep your data close to where it is most needed. Data is also replicated to different disaster zones to ensure high availability.
• When you upload an object and mark it as cacheable (by setting the standard HTTP Cache-Control header), The service automatically figures out how best to serve it using Google’s broad network infrastructure, including caching it closer to the end-user if possible.

• They are organized into containers.
• There are two kinds of blobs: block and page blobs.
• A block blob can be divided into some number of blocks.
• If failure occurs while transferring a blob, retransmission can resume with the most recent block.
• Page blobs are divided into some number of pages, which are designed for random access.

• Select the type of database to create such as MySQL, Oracle, or SQL Server.
• Define database size, allocated storage, database identifier.
• Define user name and password.

The database instance has an end point that the user can use to securely connect to the instance.

• A table is a collection of items.
• Each item is a collection of attributes.
• To store data you must create one or more tables and specify how much throughput must be provisioned for reading and writing operations.
• The service is fully managed and automatically distributes the data and the traffic over a number of servers to meet the throughput requirements.
• All the stored data is automatically replicated across multiple zones to allow for durability.

• Synchronous and asynchronous geographic replication.
• Ability to import and export databases.
• Selection of a region, database tier, billing plan and replication modality.
• Scheduling daily backups and restoring backups.

• ACID transactions and high availability of read and write operations.
• Data model consists of entities.
• Each entity has one or more properties.
• Properties (key-value pairs) can be of one supported data types.
• Each property has a kind and a key. The key uniquely identifies the entity, while the kind is used to categorize the entity for query purposes.

• The SQL database is a multi-tenant service.
• Each customer has a logical SQL server assigned.

• The tables are divided in a number of partitions.
• Each partition can be stored on a different machine.
• A partition holds a specified number of entities each containing several properties.
• Each property can be one of the several supported data types.
• Tables do not have a fixed schema, so different entities in table can have different properties.

• Support for programming languages such as Java, Python, Ruby and so on.
• Automatically allocate resources.
• Handle scaling without the need to run and maintain servers.

• Runtimes. App Engine provides runtime environments for Java, Python, PHP, Go programming languages.
• Sandbox. Applications run in a secure sandbox environment isolated from other applications. Limited access to the underlying OS is allowed.
• Web Frameworks. App Engine provides a simple Python web application framework called webapp2. It also supports frameworks written in Python that use WSGI, which include Django, CherryPy, Pylons, web.py and web2py.
• Datastore. A no-SQL data storage service is provided.
• Memcache. High performance in-memory key-value cache service is provided.
• Tasks. App Engine provides task queues to allow work in the background. It also provides a cron service for scheduling.

• Shared and standard options.
• In the shared option, a web site runs on virtual machines that can contain multiples sites by multiple users.
• In the standard option, a web site runs on virtual machines that belong to a single user.
• Applications written in ASP.NET, PHP, Node.js and Python are supported.
• Multiple copies of an application can run on different virtual machines and requests are automatically load-balanced among them.

• They allow for asynchronous processing.
• Provide overflow buffers to handle temporary volume spikes or to handle the mismatch between message generation and consumption.

Messages can be queued and read from queues simultaneously. The message latency can go from a couple of days to a couple of weeks.

Windows Azure Queues

The figure above illustrates a simple but common scenario for cloud applications. There are a set of web servers hosting the frontend logic of handling web requests. There are a set of backend processing servers implementing the business logic of the application. The web server frontend nodes communicate with the backend processing nodes via a set of queues. Persistent state of the application can be stored in Windows Azure Blob storage and Windows Azure Table storage.

Amazon Elastic MapReduce (Amazon EMR) is a web service that makes it easy to quickly and cost-effectively process vast amounts of data.

Amazon EMR uses Hadoop, an open source framework, to distribute your data and processing across a resizable cluster of Amazon EC2 instances. Amazon EMR is used in a variety of applications, including log analysis, web indexing, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics. Customers launch millions of Amazon EMR clusters every year.

Google MapReduce service is an open-source library that is built on top of App Engine services, including Datastore and Task Queues. You must download the MapReduce library and include it with your application. The library provides:

• A programming model for large-scale distributed data processing
• Automatic parallelization and distribution within your existing codebase
• Access to Google-scale data storage
• I/O scheduling
• Fault-tolerance, handling of exceptions
• User tunable settings to optimize for speed/cost
• Tools for monitoring status

There are no usage charges associated with the MapReduce library. As with any App Engine application, you will be charged for any App Engine resources that the library or your MapReduce code consumes (beyond the free quotas) while running your job. These can include instance hours, Datastore and Google Cloud Storage usage, network, and other storage.

Querying massive datasets can be time consuming and expensive without the right hardware and infrastructure. Google BigQuery solves this problem by enabling super-fast, SQL-like queries against append-only tables, using the processing power of Google’s infrastructure. Simply move your data into BigQuery and let us handle the hard work. You can control access to both the project and your data based on your business needs, such as giving others the ability to view or query your data.

You can access BigQuery by using a browser tool or a command-line tool, or by making calls to the BigQuery REST API using a variety of client libraries such asJava, PHP or Python. There are also a variety of third-party tools that you can use to interact with BigQuery, such as visualizing the data or loading the data.

Get started now with creating an app, running a web query or using the command-line tool, or read on for more information about BigQuery fundamentals and how you can work with the product.

HDInsight is a Hadoop-based service from Microsoft that brings a 100 percent Apache Hadoop solution to the cloud. A modern, cloud-based data platform that manages data of any type, whether structured or unstructured, and of any size, HDInsight makes it possible for you to gain the full value of big data.

With HDInsight you can seamlessly process data of all types through Microsoft’s modern data platform, which provides simplicity, ease of management, and an open Enterprise-ready Hadoop service all running in the cloud. You can analyze your Hadoop data with PowerPivot, Power View and other Microsoft BI tools, thanks to integration with Microsoft data platform.

Amazon CloudFormation gives developers and systems administrators an easy way to create and manage a collection of related AWS resources, provisioning and updating them in an orderly and predictable fashion.

You can use AWS CloudFormation’s sample templates or create your own templates to describe the AWS resources, and any associated dependencies or runtime parameters, required to run your application. You don’t need to figure out the order for provisioning AWS services or the subtleties of making those dependencies work. CloudFormation takes care of this for you. After the AWS resources are deployed, you can modify and update them in a controlled and predictable way, in effect applying version control to your AWS infrastructure the same way you do with your software.

You can deploy and update a template and its associated collection of resources (called a stack) by using the AWS Management Console, AWS Command Line Interface, or APIs. CloudFormation is available at no additional charge, and you pay only for the AWS resources needed to run your applications.

AWS Identity and Access Management (IAM) enables you to securely control access to AWS services and resources for your users. Using IAM, you can create and manage AWS users and groups and use permissions to allow and deny their access to AWS resources.

To get started using IAM, click on the Create New Account; or if you have already registered with AWS, sign into the AWS Management Console and get started with these IAM Best Practices.

IAM is free so start using it today!