Skip to main content

5 Top Data warehousing Skills in the age of Big data

5 Top Data warehousing Skills in the age of Big data
#5 Top Data warehousing Skills in the age of Big data:
A data warehouse is a home for "secondhand" data that originates in either other corporate applications, such as the one your company uses to fill customer orders for its products, or some data source external to your company, such as a public database that contains sales information gathered from all your competitors.

What is Data warehousing

If your company's data warehouse were advertised as a used car, for example, it may be described this way: "Contains late-model, previously owned data, all of which has undergone a 25-point quality check and is offered to you with a brand-new warranty to guarantee hassle-free ownership."

Most organizations build a data warehouse in a relatively straightforward manner:
  • The data warehousing team selects a focus area, such as tracking and reporting the company's product sales activity against that of its competitors.
  • The team in charge of building the data warehouse assigns a group of business users and other key individuals within the company to play the role of Subject-Matter Experts. Together, these people compile a list of different types of data that enable them to use the data warehouse to help track sales activity (or whatever the focus is for the project).
  • The group then goes through the list of data, item by item, and figures out where it can obtain that particular piece of information. In most cases, the group can get it from at least one internal (within the company) database or file, such as the one the application uses to process orders by mail or the master database of all customers and their current addresses. In other cases, a piece of information is not available from within the company's computer applications but could be obtained by purchasing it from some other company. Although the credit ratings and total outstanding debt for all of a bank's customers, for example, aren't known internally, that information can be purchased from a credit bureau.
  • After completing the details of where each piece of data comes from, the data warehousing team (usually computer analysts and programmers) create extraction programs. These programs collect data from various internal databases and files, copy certain data to a staging area (a work area outside the data warehouse), ensure that the data has no errors, and then copy it all into the data warehouse. Extraction programs are created either by hand (custom-coded) or by using specialized data warehousing products.
Different roles in Data warehousing projects:

Data modeling.: Design and implementation of data models are required for both the integration and presentation repositories. Relational data models are distinctly different from dimensional data models, and each has unique properties. Moreover, relational data modelers may not have dimensional modeling expertise and vice versa.

ETL development: ETL refers to the extraction of data from source systems into staging, the transformations necessary to recast source data for analysis, and the loading of transformed data into the presentation repository. ETL includes the selection criteria to extract data from source systems, performing any necessary data transformations or derivations needed, data quality audits, and cleansing.

Data cleansing: Source data is typically not perfect. Furthermore, merging data from multiple sources can inject new data quality issues. Data hygiene is an important aspect of data warehouse that requires specific skills and techniques.

OLAP design: Typically data warehouses support some variety of online analytical processing (HOLAP, MOLAP, or ROLAP). Each OLAP technique is different but requires special design skills to balance the reporting requirements against performance constraints.

Application development: Users commonly require an application interface into the data warehouse that provides an easy-to-use front end combined with comprehensive analytical capabilities, and one that is tailored to the way the users work. This often requires some degree of custom programming or commercial application customization.

Production automation: Data warehouses are generally designed for periodic automated updates when new and modified data is slurped into the warehouse so that users can view the most recent data available. These automated update processes must have built-in fail-over strategies and must ensure data consistency and correctness.

General systems and database administration: Data warehouse developers must have many of the same skills held by the typical network administrator and database administrator. They must understand the implications of efficiently moving possibly large volumes of data across the network, and the issues of effectively storing changing data.

Comments

Popular posts

Blue Prism complete tutorials download now

RPA blue prsim tutorial popular resources I have given in this post. You can download quickly.Learning Blue Prism is really good option if you are learner of Robotic process automation. The RPA is also called "Robotic Process Automation"- Real advantages are you can automate any business process and you can complete the customer requests in less time.

The Books Available on Blue Prism 
Blue Prism resourcesDavid chappal PDF bookBlue Prism BlogsVideo Training
RPA training The other Skills you need
Basic business skills and Domain skills are more than enough to be successful in this automation careerScripting languages like Perl/JSON/JavaScript/VBScript.  The interesting point is learning any RPA tool is not a problem. You can learn tool quickly. The real point is how quickly you apply your knowledge to implement automated tasks is important.


Also read
Robotic RPA Software developer skills you needBlue Prism tutorials download to learn quicklyPopular RPA tools functionality differen…

Three popular RPA tools functional differences

Robotic process automation is growing area and many IT developers across the board started up-skill in this popular area. I have written this post for the benefit of Software developers who are interested in RPA also called Robotic Process Automation.

In my previous post, I have described that total 12 tools are available in the market. Out of those 3 tools are most popular. Those are Automation anywhere, BluePrism and Uipath. Many programmers asked what are the differences between these tools. I have given differences of all these three RPA tools.

BluePrismBlue Prism has taken a simple concept, replicating user activity on the desktop, and made it enterprise strength. The technology is scalable, secure, resilient, and flexible and is supported by a comprehensive methodology, operational framework and provided as packaged software.The technology is developed and deployed within a “corridor of IT governance” and has sophisticated error handling and process modelling capabilities to ensu…

Robotic RPA Software developer skills you need

Robotic process automation is an upcoming and becoming most popular skill. As I said there are three popular tools. To become proficient in any one of the tool is really good to get a job in Developer role.
To get a job in this line, I found in my research that some programming skills and Hand-on training on any one of the tools is required. Also, try to to know differences in other popular rpa tools.

Most people are asking experience in tools like Automation anywhare, Blue Prism and Uipath. But, you cannot be proficient in all. So just know what are the differences. Ok...
You may ask a question like how to know. First join one good coaching institute and learn one tool perfectly. And start taking online training. Really good for you. Whatever you are lacking quickly you can learn online way.

To learn Uipath try here. Also, you can learn Automation anywhere tool online way.

The following are the list of IT skills commonly asking:
Automation anywhere/Blue Prism/Uipath.Net/C#/Java/SQL ski…