How Operational Data Store Works

The operating system (OS) is the first thing loaded onto the computer -- without the operating system, our computer is useless. An Operational Data Store - ODS is an integrated database used for being an interim area for a data warehouse.

We have several database type systems:- transactional- operational, data warehouses etc.

If you think about how our brains work the three databases above would equate to the following:
- transactional = doing something and thinking about it;
- operational = using your short term memory to remember what you did a few days ago;
- data warehouse = using your long term memory to remember what you did 10 years ago.

It is common to use the different databases for various storage uses. The information keeps go through a life cycle, just like we do. And various databases or data structures are used for example to house the banks information at the appropriate life cycle.

Here's an example
Let's say that at the bank there is a team that takes orders for the Bankers' deals. This team needs a database solution for storing all the incoming Banking deals. On a transactional level, this team needs to store the- deal description- deal time- deal due date- deal amount Usually lots of data. This team will also connect to other database tables that store things like:
- customer (in terms of companies)
- person ( in terms of a companies employees)
- meetings (set up at customers for the investment bankers to meet with the employees)
- bank employees (need to store which bankers are going to meet with what customers and are assigned to which deals).
Many of the above tables are now transactional, they are updated everyday. The updates can be inserts of new records, updates of records, and even simply reading the records for viewing on a computer screen. Now there's a cost associated with building out data centers in terms of database software, server software, load balancing, failovers, disaster recovery and the way the data is used will dictate how a data architect will solution for the hardware, physical and logical architecture of the data solution. At a certain point, the team helping the Bankers, some of those deals go from the really fresh world of first inputting and make some expected updates to the bankers meetings and deals.

Then a data migration takes place, where rows of data are pulled from the transactional database and moved to the operational data store. In the staging area of the operational data store the normalization of the data will change. There's all sorts of steps andrules here. Basically, the architect will not change the data structure too much, but some, because of the type of reports that will be pulled from the operational data store, are more exhaustive than those from the transactional database.
There's a lot of theory aboutatomicity, data scrubbing, normalization, but it all basically relates to getting all the data in, setting it up in tables with relationships and likely building out a much of the logic that will drive reports via triggers, stored procedures and actual programs.
Some of the above is about dividing up the work between the application servers, the database servers, and tuning the application so that when the Bankers team needs to pull up a report from the last month about all of the meetings the bankers had, the team gets the report quickly.
Eventually, the data from the ODS goes into a data warehouse where even more normalization goes on and data marts are built off of the warehouse so that historical reports, trends analysis can be performed on the banks business trends.

These type of warehouses generate business intelligence reports by allowing for graphical reports to be drilled into, deeper and deeper so that researchers at the bank can understand the bank, customers, world economies, etc. An important thing to remember is that there are more transactional databases than ODS. And there are more ODS than data warehouses. The further one gets from the transactional database, you typically look for different hardware, software and even storage facilities.

ETL architecture

To describe ETL architecture we have to learn first of all about short history of information specially in companies were certain data created in some certain way is needed to succeed.
At the beginning was a real mess with data which was tried to implement into the people’s minds and everybody around let’s say “big bosses” was expecting from those poor people to use that information in correct way, to know everything about information which were circulating within the company. It was real misunderstanding from side of those people who should be run companies in certain way which would allowed to maintain good atmosphere. among everybody within the firm.
Fortunately some really smart people in form of academic scientists, managing directors mainly on business ground made some struggle and created very very good tools which caused revolution in the area of managing of information.
Because it was plenty of ideas and plenty good tools so every firm has its own choice and it can choose from the verity of tools which are not replaceable in today’s world.

Let’s write something about one of them as I mentioned before. ETL architecture which allows to extract, transform and load data into certain way which allows to use that data in normal mode for example to sell things from warehouse of certain company by people employed there which they have not knowledge about specific and all data which are in that firm because they do not need to know. It is enough for them just this particular we can say computer program created for that purpose which allows them to work smoothly and reach goals set up by management.

To describe and give the certain definition to ETL architecture we can say that it is the core process of data integration and it is mainly connected and linked with warehouse data. ETL is able to extract data from the source let’s say from the top to the new format which is needed for particular business rules in the company and then set up as a target structure for that firm.
We see here that ETL architecture is the tool which is so much needed in today’s world of companies where certain good planning and managing of information by these tools not necessary ETL architecture because we have plenty more of them can be really the best way to the success.

We can divde this particular process of transforming the way that data works for us.
First of all we can use the ETL architecture in more simple way to reach target point but that will not be working at the highest level of our expectancy. That is why we can use more complicated way to use the same system to transform our data but in other available way and when we finish we will have program in which will be able to work more effectively because as it is in the live easy ways to do things something are not necessary good in longer terms. So we have to sometimes choose ways where is the more work throughout the project and at the end of it we can enjoy program with whom we can work easier and our work is more effective, simpler and bring some good results.

As we can notice that all tools are used very often nowadays in business where we need to have certain information, we can say while the client is waiting, very fast plus must be accurate and reliable.

ETL

ETL - extract, transform, load

ETL architecture is used in order to manage and optimize the data together with the process of its transfer which, as a result, leads to simplification and standardization of data storage in the data warehouse.

Purpose of the ETL tool is to create a universal framework for different processes of transforming data into target destination.

The ETL letters are the abbreviations for extraction, transformation and loading of data, which means that the ETL tools extract data from a source, transform it into new formats and load it into target data warehouse.

System’s architects created a scheme which eliminates repeatedly appearing actions. Loading the data into warehouse requires two types of actions: one, in which data is transformed from the identifiable source system according to source-specific rules – this action results in transforming data into a standardized format. Each action has to be done by individual method for every data storage system.

The other, conformed type of ETL architecture means creating universal way of data entity, regardless of the source and purpose; it allows to follow reusable rules which are applied in business and work in different conditions with variety of data. Both systems has pros and cons. At the beginning the first one requires fewer actions to be performed in order to work smoothly and seems easier. However, looking at the overall life cycle of the data warehouse, in the long run using the conformed ETL is more efficient.

It is because data warehouse is being developed and has been growing continually when the new data is added to the system, so when the traditional architecture is taken into consideration, though it may seem easier to make it, in the long run it turns out that the same or similar actions are repeated, needlessly lengthening the development time and increasing its complexity. Implementing the universal for one entity ELT architecture requires more work than doing the same for a particular task, however in the long run it brings more benefits as the data is stored and the scheme may be used further.

Thus the most obvious advantage of using ETL is that the storage data is easily reused which results in improving its quality. Moreover it’s easier to make and use, because in fact it is less complex than storing data in different traditional systems. Additional advantage is also that it’s less difficult to add and acquire new source systems. One should also bear in mind that the conform ETL architecture is not to be applied everywhere, it should be proceeded by detailed analysis, which would ensure the benefits that would possibly result from following the conform architecture. Otherwise it may not be so beneficial.

This data architecture enables direct access to data in operational systems, which is very important and in fact shorten the time considerably, which otherwise would be spent on data implementation and simplify the data usage.

EII – Enterprise Information Integration

EII – Enterprise Information Integration

This fine abbreviation applies to information integration only at the commercial line. EII is a data integration from the many systems to compact and unified form that offered viewing and manipulation of data. Data is matching, selected and restructured and only then present to the user.
Data integration is primarily connected with manipulation and the valuation of historical data to discover existing trends which for example are not visible.
On the other hand, application integration is focused on data integration between application library and system. When data is changing in one system, the reversal is instituted to the other system of interest, usually via irregular messaging. Information integration is simply target at the end users who are working with multiple systems.
As You can see and as stated earlier, data integration is focused on the integration of data in multiple systems which I called “big tree”. In fact, not many people know about this database in spite of maybe accidental

Data Integration

Have You ever heard about Data Integration?
Do You know what exactly it is?

Data Integration - it is almost as simple as it sounds and it is pretty much intangible goal which software industry is still running for. In this article I will try to explain why data integration is so huge bite for this companies.

Data integration is like big tree where every branch connected and cooperating with the other. All the data living in this “branch” affords to the users unification view of this data. The process of data integration occurring in every part of our live. For example: when two similar company have to merge their databases or at education to merge different results. However, in our contention world the ITC infrastructure played major role in a learning environment which can drawn in the right students, sponsoring and research project. The most important think is the role to data integration. It means that two system can be integrate when:
Both look the same;
Act seems to be the same;
and both of this system produce and consume the same data.
In sum, data integration is the ability to control, make, share and consume the same data.

Example

Some web application have information about the cities such a weather, demographics, cinemas, hotels, tourism, etc. Typically, the information have to be in one type of database with a single schema. But it is not as simple as seems to be. Single enterprise would find the information which can be difficult to collect. Even if resources gather volumes of information about weather or tourism, it would probable duplicate the data.

The originators of this idea try to develop the best model of virtual schema that the users want the most. This wrapper escorted to resources or application to improve on compatibility of the crime database weather websites. This adapters for each data source transform the query results into a straight form. Finally, the database connect the performance to show unified view and answer for the users question.

Business Intelligence

Business Intelligence - the main tool of managers and strategy’s experts.

First of all: BI is very wide concept. Generally speaking, it is an advance of transforming data to information, and information to knowledge, which can be used to increase the competitiveness of the company.

Techniques of presentation are adapting especially to user necessities. Thanks that it can be clear and obvious. If we want to make a fast decision regarding our business we have to analyse a lot of data. To the effect avoiding of looking lots of numbers, visualisations of present state are realised in form of picture or diagram.
Corporations own in their informatics systems many data. Usually they are archivered and seldom using in business intelligence. Building business strategies is impossible if corporations don’t have tools using for fast reviewing, analysing or making reports.
If BI is put into a practise, then we have possibility of integrate data and analysing them. BI analyses are usually standard. The corporations are interested in analyse of several key indicators. But it helps not only in supporting decisions and process of planning. It can also warn about possible danger which let work the ‘security plan’. The main element is time which we saved. After introduced the business analyses tools with the data’s warehouse, the time of making reports is falled off.
The main thing is that BI is not a ‘ready-made’ product , which helps you to solve all your problems. It is just useful way of helping you in development of yours business. What also should been told is that Business Inteligence is not a single product, technology or methodology. It matches all that concepts in one. It combine three specified things in one system to organize key information that is needed to improve profit and performance.
The typical schematic diagram of BI based on generating of standard reports or enumerating Key Performance Indicators. On this base are made hypothesis, and then they’re verificated.

Costs and profits

In business the primary things are reduce costs and increasing profits. Analysis of market helps to achieve this purposes. For example: Managers and experts are collecting business information, analyses and decisions. Then they match it in whole- the Context of Core Business Processes. This concept include management processes, (e.g. planning, budgeting, forecasting, monitoring and controlling), revenue generating processes (e.g. marketing, sales, campaign, management and channel management) and operating processes (e.g. customer service, billing, manufacturing, logistics and inventory management). These elements helps specialist to increase sales, reduce costs and increase profits.
Business Intelligence can be used in every value corporations: also big ones like that smaller.

Business Intelligence software

Business intelligence includes also the systems of automatic reaction for events, e.g. sending to client reports about realization of the order.
The basic systems which are included to BI are:

EIS- Executive Information Systems

DSS- Decision Support Systems

MIS- Management Information Systems

GIS- Geographic Information Systems
The wrong thing is to expect from BI system simple answers and “the only right solution”. It is just impossible to realizate. The second thing is tendentious presentation of data.