Data Warehouse Architecture Training
Introduction to Data Warehouse Architecture Training:
Data Warehouse Architecture Training at Global online trainings – Now let’s understand what exactly is the Data warehouse? The concept of Data warehousing is pre assemble. Data is extracted on a periodic basis from source systems which could be applications such as ERP, CRM, Sales, Finance and so on. Data from the systems is moved to the dedicated server that contains the Data warehouse. So when the data has moved it is cleansed, formatted, summarized and supplemented with data from many other sources and this resulting Data warehouse will become the main source of information for report generation and analysis via reporting tool that can be used for things such as ad-hoc queries and dashboards. Global online trainings is rich in providing Data warehouse architecture training by real time experts from India.
Prerequisites of Data warehouse architecture training:
- Basic understanding on database concepts is require to learn Data warehouse architecture training.
- Good knowledge on ER model.
- Good knowledge on Structured Query language.
What is the need of Data warehouse?
The goal of every business is to make better decisions and this is where business intelligence comes in. Business Intelligence training turns the massive amount of data from operational systems into a proper format which is easy to understand so that this data could be analyzed timely and accurately to obtain actionable insights of business and also to bring effectiveness in decision making for Data warehouse architecture training .
For instance in e-commerce industry we maintain data about products, customer login credentials, checkout details, other technical and non-technical information. All of this information would be available in different servers. So we need system which can combine all this information so that companies can next track and analyze this data in an integrated manner and that’s exactly why the requirement of Data warehouse comes into picture.
- Data warehouse architecture training at Global online trainings – Data warehouse gives you an option to analyze particular subject area. Thus subject matter expert can answer relevant questions from the data. A Data warehouse supports consistency by arranging data from different sources in a uniform and traditional format. So it should not allow any conflicts in the field names. Having accomplished the sturdiness we can refer to the integrated warehouse. The another key feature is Non-volatile so as the name suggests Non-volatile refers to no data change once created. It is the important and relevant feature since the aim of developing the feature is to evaluate.
- In Data warehouse architecture training, Sometimes it has been noticed that many companies say we have a data warehouse but they don’t really have a data warehouse rather it is actually a dumping ground for tables that are copy from source systems with little modification. Are you interested in learning advance topics on this course,We provide Data warehouse architecture training with live projects.
- So for a company to be successful in future we must make good decisions and to make upright choices they need all the related data to be taken into attention and the best source for the data is a well-defined Data warehouse. So Data warehouse is specially designed to perform business intelligence activities and enables professionals and employees to improve organizations overall performance in Data warehouse architecture training.
The two major functions of a Data warehouse architecture training are to keep past and present records and also benefit organizations to take actual business choices with precise data analysis. Additionally the Data warehouse environment also supports ETL testing training, Data mining capabilities, Statistical analysis, Reporting and OLAP tools. Which help in interactive and efficient data analysis in a multi facet view in Data warehouse architecture training.
Learn ETL and Data Marts in our Data warehouse architecture training:
- ETL stands for Extraction, transformation and loading and these are the three stages you have to go through to create a data warehouse. First of all you have to extract the data from the in-house databases. So that’s getting the data from the original database into the staging area. When it’s in the staging area we have got to transform it to make it useful. So we have to get all the data into the same format names, surnames and titles in Data warehouse architecture training.
- Then we have to load it into the data warehouse itself. Once we have got it into the data warehouse. Data marts are the subsets of the data warehouse. So we have these subsets so that people don’t mess with each other’s data also it keeps simpler for the user. Are you passionate in learning this course? we provide Data warehouse architecture training by industry experts from India.
- In this case the marketing director is not going to be interested in what the finance director is interested. So let’s keep their queries separate it makes it simpler there are fewer options for each person to look at and therefore less chance of them getting lost. The other big advantage is that if one of them comes up with another question. We are trying to solve that problem on a smaller set of data. So it’s going to be quicker and easier to solve where you have got the general idea of a data warehouse.
- Data warehouse architecture training at Global online trainings – How is the data held and is held as a star, In the middle you have a fact and these fact is linked to a number of dimensions. Here we have got four but you could have three or twenty three.
For example you might have Sales, Sales is the item in the middle so for each sale you have the member ID, wine ID, area ID and time ID. Time is needed so you get the idea of trends and you have that for every sale. Now that may not be applicable for example one member may have bought many things but this is in the second normal form. So even though one member has bought say three items that member’s details are now going to be recorded against each sale. For each of those sales and then finally we could look at it as if it were as a constellation. We have the sales and then we can link the different dimensions together with other facts. So the wine, the member or the area and then we have got a proper data warehouse. We can make the constellation as complicated as we want in Data warehouse architecture training .
Dimension and Fact in Data warehouse architecture training :
- Dimensions and Blocks are basic building blocks in data warehouse. They are focus of dimensional modelling. Everything revolves around them in data warehouse. For warehouse developers this is the primary challenge to find out that from available data set which entities are dimension? And which would be considered as fact in Data warehouse architecture training .
- Data warehouse architecture training at Global online trainings – For any retail sale we gather date, when the sale happens store where the sale happens product what is sold quantity, unit price and sells amount of sold products and a transaction number or invoice number of sales. The first step to create a data warehouse is to examine data and analyze data so that I can categorize dimension and fact.
- If you look closely you can see some data as text values and some are numeric values. You can pull date, store name and product in text group and quantity, unit price, sales amount and transaction number will fall into numeric group. If you pick values for quantity sold this numbers 10,20, 30 themselves are meaningless. They are not providing any information we need some descriptive levels that show relation with these numbers or at least inform about context in which these numbers are showing to establish that context.
- In Data warehouse architecture training ,What I want to convey here is that every business process contains two types of data. One that provides quantitative information about business process and other that describes these quantitative numbers. Data that is level based and descriptive and that explains business numbers and typically termed as dimensions and entities that are numeric in nature are mostly facts.
- In majority this definition is true but there may be cases when we are defining number values as dimension. Fact in data warehouse provides quantitative information about business process, that’s why they are also called as measurements or matrix quantity sold, sales amount, total earning profit margin, total turnover these are some common examples of facts.
- So fact is measurement of business and is mostly a numeric quantity. But being just a numbers it is not providing any information. We cannot generate any context or meaning with this matrix, unless there is something that will describe these numbers something that will provide perspective to view these numbers and dimension is that entity in Data warehouse architecture training .
- It is an object that describe facts or business numbers , through dimension we analyze numbers and categorize facts and measures and that facilitates end user to answer business questions without the dimensions.
- We cannot measure the facts, some common examples of dimensions are from people may be customers, vendors or in health care patients, providers or physicians products or product group, place that is geographical dimensions like country region market state or city or time dimension like year quarter, month or date. So with these dimensions we provide a medium for business users to do data analysis from various viewpoints.
- Users can do slicing and dicing on data to get business information like sales by customer or sales by customer in the year or sales by customer in a year by a particular product group. So basically dimensions prepare us for doing filtering, grouping and leveling functions in Data warehouse architecture training .
In Data warehouse architecture training, A question comes how can we recognize dimension? So dimensions are generally level based and descriptive entities. Wikipedia has a good trick to ask questions on context of the business like analyzed by what? Analyzed by time or Analyze by product or analyzed by geography. This analyzed by what gives a hint on possible dimension fields.
Conclusion of Data warehouse architecture training:
Want to know the best part? To identify a fact in a business process we need to ask two basic questions that will help us to pick facts like from our retail process. What is total number of product A that is sold or what is this amount of product B clearly quantity and sales amount are primary target of computation and are good candidates for facts. They are providing metrics for retail business. For example quantity sold by month or sales amount by product, by date. There are lots of opportunities in the present market for Data warehouse architecture training with the exciting packages. Join today in Global online trainings for best Data warehouse architecture training. Hurry up!