Cloud architecture, the systems architecture of the software systems involved in the delivery of cloud computing, typically involves multiple cloud components communicating with each other over a loose coupling mechanism such as a messaging queue. When talking about a cloud computing system, it’s helpful to divide it into two sections:

1. The Front End or the Intercloud:
The front end includes the client’s computer (or computer network) and the application required to access the cloud computing system. Not all cloud computing systems have the same user interface. Services like Web-based e-mail programs leverage existing Web browsers like Internet Explorer or Firefox. Other systems have unique applications that provide network access to clients.

Cloud Computing Architecture

2. The Back End or The Cloud Engineering :
On the back end of the system are the various computers, servers and data storage systems that create the “cloud” of computing services. In theory, a cloud computing system could include practically any computer program you can imagine, from data processing to video games. Usually, each application will have its own dedicated server.

[N.B: Cloud engineering is the application of engineering disciplines to cloud computing. It brings a systematic approach to the high level concerns of commercialisation, standardisation, and governance in conceiving, developing, operating and maintaining cloud computing systems. It is a multidisciplinary method encompassing contributions from diverse areas such as systems, software, web, performance, information, security, platform, risk, and quality engineering.]

If a cloud computing company has a lot of clients, there’s likely to be a high demand for a lot of storage space. Some companies require hundreds of digital storage devices. Cloud computing systems need at least twice the number of storage devices it requires to keep all its clients’ information stored. That’s because these devices, like all computers, occasionally break down. A cloud computing system must make a copy of all its clients’ information and store it on other devices. The copies enable the central server to access backup machines to retrieve data that otherwise would be unreachable. Making copies of data as a backup is called Redundancy.

The architecture of cloud is evolving rapidly. Hopefully in the upcoming future of computing we can say “we build our home in the cloud”. There are also many issues such as privacy, data maintenance, etc, but still there are loads of advantages too. We will discuss it in the later blogs. Stay tuned for more !!!

The term “cloud” is used as a metaphor for the Internet, based on the cloud drawing used in the past to represent the telephone network, and later to depict the Internet in computer network diagrams as an abstraction of the underlying infrastructure it represents.Cloud computing is a natural evolution of the widespread adoption of virtualisation, service-oriented architecture, autonomic, and utility computing. Details are abstracted from end-users, who no longer have need for expertise in, or control over, the technology infrastructure “in the cloud” that supports them.

Let’s say you’re an executive at a large corporation. Your particular responsibilities include making sure that all of your employees have the right hardware and software they need to do their jobs. Buying computers for everyone isn’t enough — you also have to purchase software or software licenses to give employees the tools they require. Whenever you have a new hire, you have to buy more software or make sure your current software license allows another user. It’s so stressful that you find it difficult to go to sleep on your huge pile of money every night. And this is where the concept of Cloud computing come into play.
Now all you need to do is just to load only one application instead of installing a suite of software for each computer. That application would allow workers to log into a Web-based service which hosts all the programs the user would need for his or her job. Remote machines owned by another company would run everything from e-mail to word processing to complex data analysis programs. This is what CLOUD COMPUTING is all about.

In a cloud computing system, there’s a significant workload shift. Local computers no longer have to do all the heavy lifting when it comes to running applications. The network of computers that make up the cloud handles them instead. Hardware and software demands on the user’s side decrease. The only thing the user’s computer needs to be able to run is the cloud computing system’s interface software, which can be as simple as a Web browser, and the cloud’s network takes care of the rest.

If you guys are still wondering, just take another simple example of Gmail accounts that is provided by Google. Instead of running an e-mail program on your computer, you log in to a Web e-mail account remotely. The software and storage for your account doesn’t exist on your computer — it’s on the service’s computer cloud.

Cloud computing is all the rage. “It’s become the phrase du jour,” says Gartner senior analyst Ben Pring.

Hence my small overview about cloud computing is over. I guess soon i will be able to provide you with more details about this new phenomenon.

• 2-Tier Architecture

In 2-tier architecture, the MicroStrategy Desktop itself queries against the Data warehouse and the Metadata without the Intermediate tier of the Intelligence server.

• 3-Tier Architecture

The 3-Tier architecture comprises an Intelligence server between MicroStrategy Desktop and the data Warehouse and the Metadata.

• 4-Tier Architecture

The 4-tier architecture is same as 3-tier except it has an additional component of MicroStrategy Web.

Features:

Dynamic SQL Generation: MicroStrategy Intelligence Server stores information about the database tables in metadata. MicroStrategy Intelligence Server uses this metadata to generate optimized SQL for the database. Because the metadata is schema independent, these reports, queries and analyses are generated from your current physical schema without any modifications.

Advanced Caching: MicroStrategy Intelligence Server caches all user requests. Not only are reports cached, but the individual report pages requested by users are also cached. As a result, no redundant processing occurs on the MicroStrategy Intelligence Server or on the database.

Built-in Software-level Clustering and Failover: MicroStrategy Intelligence Server lets you cluster many different individual servers together without any additional software or hardware components. Built-in failover support ensures that if a server experiences a hardware failure, the remaining MicroStrategy Intelligence Servers will pick up failed jobs.

Integrated Aggregations, OLAP, Financial and Statistical Analysis: MicroStrategy Intelligence Server provides simple analysis such as basic performance indicators, as well as more sophisticated analyses such as market basket, churn, retention and deciling analyses. Other analyses include hypothesis testing, regressions, extrapolations and bond calculations.

The Architecture has the following components:

• Source System (OLTP):

Source systems are typically databases or mainframes that store transaction processing data. As such, they are an Online Transaction Processing System (OLTP). Transaction Processing involves simple recording of transactions like sales, inventory, withdrawals, deposits and so forth.

• Data Warehouse (OLAP):

A well designed and robust data warehouse lies at the heart of the business intelligence system and enables its users to leverage the competitive advantage that business intelligence provides. A data warehouse is an example of Online Analytical Processing System (OLAP).

Analytical Processing involves manipulating transactional records to calculate sales trends, growth patterns, percent to total contributions, trend reporting, profit analysis etc.

• ETL Processes:

The extraction, transformation and loading (ETL) process contains information that facilitates the transfer of the data from the source systems to the data warehouse. We have discussed about this in details in my previous post.

• Metadata Database:

The metadata database contains information that facilitates the retrieval of data from the data warehouse when using MicroStrategy applications. It stores MicroStrategy object definitions and information about the data warehouse in proprietary format and maps MicroStrategy objects to the data warehouse structures and content.

• MicroStrategy Application:

The MicroStrategy applications allow you to interact with the business intelligence system. They allow you to logically organize data hierarchically to quickly and easily create, calculate, and analyze complex data relationships. They also provide the ability to look at the data from different perspective.

A variety of grid and graph formats are available for superior report presentation. You can even build documents, which enable you to combine multiple reports with text and graphics.

As per we previously discussed, we need ETL tools (e.g. Informatica) to build a Data Warehouse. The ETL (Extract-Transform-Load) extract the data from OLTP and different other data sources, transform the data in the staging area according to the business need and finally load the data to Data Warehouse. Now one common question would be that how will you segregate between a database and a data warehouse. And a compact answer can be like this – “Data warehouse is also a database. When a database stores historical data (data from the same system, taken at different time period), then it becomes a Data Warehouse.”

So we have historical data in the data warehouse. Now what is the use of these data? These data can be used for analysis of business and for that they need to be represented in different format according to the business need. Suppose a business owner wants to have the trend of his last 10 year revenue – represented in Bar graph. Now you can fetch the data from database using SQL for the last 10 year. But can you represent the same data graphically? Here comes the reporting tools and MicroStrategy is a powerful leader of those.

The purpose of reporting tools is to fetch the data from the data warehouse and to represent those data according to business requirements. MicroStrategy has huge number of powerful features to support this. Hopefully we will come to know about those in the upcoming posts. The following snapshot is of MicroStrategy Desktop window.

Fig1: Snapshot of MicroStrategy Desktop

Fig2: Differenct Types of Project in MicroStrategy

As we can see from the above snapshot, we have 2 types of projects in MicroStrategy – 3 Tier and 2 Tier project. We will discuss about this in detail in the next post. As well as I will try to give some information about the architecture of MicroStrategy.

The Extract-Transform-Load (ETL) system is the foundation of the data warehouse. A properly designed ETL system extracts the data from source systems, performs transformations and cleansing and delivers the data in a presentation ready format after which the data will be loaded to the ware house. The following figure is the schematic description of the ETL process.

The 4 steps of ETL process are explained below:

Extracting: In this phase, data from different types of source systems are fetched into the staging area. The source systems can be mainframe, production sources or any other OLTP sources. Source files can also be different. Data can be stored in relational tables as well as flat files (e.g. Notepad files). The first job of ETL is to fetch data from these different sources.

Cleansing: In most cases level of data quality in an OLTP source system is different from what is required in a data warehouse. To achieve those data quality, cleansing has to be applied. Data cleansing consists of many discrete steps, including checking for valid values, ensuring consistency across values, removing duplicates and checking whether complex business rules and procedures have been applied.

Conforming: Data conformation is required whenever 2 or more data sources are merged in the data warehouse. Separate data sources cannot be queried together unless some or all the textual labels in these sources have been made identical and unless similar numerical measures have been rationalized.

Delivering: The main motto of this step is to make the data ready for querying. It includes physical structuring the data into a set of simple, symmetric schemas known as dimensional models, or star schemas (we will discuss star schema and dimensional model later). These schemas are a necessary basis for building an OLAP system.

A Database is that which is storing only the most current data. In a more technical term, it is known as OLTP source. OLTP stands for Online Transactional Processing. But whenever we use Oracle/DB2/Teradata etc. to store historical data, it becomes a Data Warehouse. By the term Historical data, we mean “Snapshots” of data. That is data of same process taken at different instant of time. Technically its known as OLAP (Online Analytical Processing) system.  A simple example can be given at this context. Lets consider a bank. A database of the bank will store the latest transactional data only. But each day the bank have these types of data. So they need to archive these. So they are storing it in a daily basis, i.e. they are taking snapshots of data and consequently the are creating a data warehouse.

So am I able to draw the line clearly between a database and a data warehouse? Maybe I am ( thinking positively ). Now another question comes into the picture. So why do one have to maintain a data warehouse. Because maintaining a data warehouse is a costly job. Day by day the size of the warehouse will grow. The answer is Business. One have to maintain the historical data to analyse the trend of his business and making decisions based on those data. This decisions can be a big factor in case of the present and upcoming performance of his business.

The job of a data warehouse specialist can be in many phase. He/she has to extract the data from different OLTP source systems, apply the modifications or cleansing according to the requirements and then loading this data to the data warehouse. And in another phase he have to fetch the data from the data warehouse, analyse it and develop the report. The end users can see those reports and based on those he/she can make the business decisions.

Ok… lets end it here. I will discuss more about this in depth from the next post. Take care..see you soon friends.