If you're looking for CloverETL Interview Questions for Experienced or Freshers, you are at right place. There are lot of opportunities from many reputed companies in the world. According to research CloverETL has a market share of about 16.32%. So, You still have opportunity to move ahead in your career in CloverETL Development. Mindmajix offers Advanced CloverETL Interview Questions 2018 that helps you in cracking your interview & acquire dream career as CloverETL Developer.
Q. What is data integration? How do you think it could be beneficial for any organization?
It is basically an approach that is helpful in deriving useful data for an organization. Basically, a few processes are combined so that meaningless or unwanted data can be converted into an information or data that businesses can use for development, planning, growth, tackling competition and so on. There are certain benefits data integration approach can for to an organization. It cut down complexity from several processes and tasks. At the same time, it ensures the less human interference in a task. Moreover, the organizations can make sure that are making the right efforts towards a process.
Q. Manual data integration is safe or it is safe through a software? What do you think?
Both have their own pros and cons associated. The biggest factor of not considering manual approach is it couldn’t assure data authenticity and consumes a lot of time than what a software needs. It is almost very difficult to ensure error-free results with more human engagement due to which software is always preferred. As far as the matter of cost is concerned, software approach is beneficial.
Q. Explain what do you know about CloverETL?
Well, it is a basically a powerful tool for data transforms and rapid development. In addition to this, data migration, cleansing, and distribution of same into warehouses, as well as applications can also be done through it. Data extraction from any source can be made extremely simpler with this approach.
Q. Name a few tasks for which an organization needs data for their accomplishment?
Most of the process needs true data for their accomplishment. Some of these tasks are developing new projects, understanding the frequently changing needs of the customers, implementing policies, understanding market flow, protocol management, monitoring security, planning processes etc.
Q. What do you mean by the term data debugging?
When data from different sources are combined together to convert it into useful information, there are certain chances that some data is corrupt and is not much accurate. It can have bugs. Debugging means locating errors and eliminating them so that final outcome can be trusted and organizations can assure the desired goals.
Q. What exactly is the purpose of CloverETL cluster?
When it comes to processing very large or bulky data, this approach is considered. It is nothing but just having multiple instances of the server for running the network nodes. It is useful against disasters, hackers attack, power failure and during other similar issues.
Q. What are the operating systems on which CloverETL is compatible with? Is there any special requirement
Well, cloverETL is a java based approach. There is no strict upper limit on the Operating Systems it is compatible with. Any Os that supports JAVA 1.6 or later can run CloverETL. There is no other special requirement for same.
Q. Is CloverETL free software? If no, why one should invest in it?
No, CloverETL is not a free software. It comes with a 45-days free trail pack. The free version is also available, but there is a strict upper limit on the features in it. CloverETL comes with several benefits. It can cut down all the errors associated with data integration and can make tasks extremely user-friendly. Also, organizations can simply save a lot of future money on data integration by investing in this approach.
Q. Suppose you are working in an organization and is handling data integration with CloverETL. Would you need CloverCARE? why?
Yes, I need CloverCARE which is basically a support package. It is true that things can go wrong anytime and can transform the issues to level next. With this support, they can be eliminated and support is always required while handling bulk operations. In addition to this, it makes the maintenance every simple which is one of the leading requirements in the present scenario.
Q. Is it possible to handle a large volume of data through CloverETL?
Yes, it’s possible. In case, data exceeds beyond a limit, one can simply go with the CloverETL Cluster which simply eliminates all the limits on same.
Q. Mention two situations in which data integration can be an excellent choice for any organization?
Organizations often have to combine the outcomes of a research project with each other. At this stage, data integration could be really helpful. Second is when an organization wants to merge their databases, they can go with this strategy. In both the situations, they can assure a lot of benefits and clear understanding of data.
Q. What are the major challenges associated with Data Integration? Is it possible to overcome them? If so, How?
Data integration is a complex approach. Of course, there are some key challenges which are associated with it. A few of them are:
1. Distinct formats
2. Data authenticity
3. Data collection
4. Errors management
5. Specialized data analysts
6. Minor bugs results into major ones
These challenges can be eliminated through expertise and having a good tool such as CloverETL.
Q. Suppose a business get lot of online queries daily about a specific topic. They have kept all their data in different databases for that particular topic. How is it possible for them to manage and handle the situation?
This can be done through an approach which is pretty common these days and i.e. Data Integration. Several software and tools are available to make this task easier.
Q. How many total schemas are there in which Data Integration Systems are defined? Can you name them?
There are total 3 schemas. They are named as G, S and M where G represents global schema, S represents heterogeneous group of source schema and is used for mapping the queries.
Q. What if the sensitive information needs to be stored into the CloveETL server?
There are encrypted parameters that the CloveETL server can easily support. Thus, all the sensitive information such as passwords can be stored in encrypted formats in the CloveETL server.
Q. Do you have any idea about Milestones? Would they be considered or not, what do you think?
They basically offer access to the advanced or the future features in CloveETL. They can be used for testing purposes. It is also possible to provide feedback to the support team in case anything wrong is there. However, it is not good to consider them in bulky or long term projects related to data integration. This is because they can be changed anytime if the support team doesn’t get favorable feedback.
Q. Give 4 reasons why an organization should consider Data Integration even if they have a limited budget?
Data Integration is not a costly procedure unless one knows the right tools and technology to consider. It makes monitoring reporting convenient as well as flexible. In addition to this it adjusts risks and allows reliable and timely reporting. Moreover it ensures quality data that can simply be trusted.
Q. What do you know about data profiling?
It is basically a procedure to understand the useful data for an organization. This is achieved by understanding quality, structure, as well as content of data. When it comes to studying schematic differences, it is one of the very useful steps to consider. Most of the unaddressed issues are simply rectified through this approach.
Q. How can you say Cleansing is an important module in Data Integration?
It is useful because it simply help in knowing the data filed which are missing. At the same time, conflicting, data, poorly managed content, and other issues can be highlighted in a very simple manner. Sometime the information can also be merged without making a lot of efforts.
Q. What is the true purpose of data transformation according to you?
Well, it is done to reconcile among the data elements that are placed at different sources. It simply allows information to pass from one form to another without facing any issue. The complex data hierarchies as well as their relationships can easily be understood with the help of this approach.
Q. Suppose you find some errors after integrating the data through a tool, would you repeat the integration process again from beginning or take another appropriate action?
It actually depends on the type of errors and the source of their origination. Next factor on which it depends is the tool. If there is a powerful tool such as CloverETL, there is no need to start the process from the beginning, only the missing fields can be considered and tasks can be made easier.
Q. What if you lost any data which is important for deriving useful information?
The simplest way is to consider backups. In any Data Integration approach, the data is not directly taken from the server. It is first copied in the tools itself or at any other trusted location in the service. Thus, the chances of data loss are less and there is no need to worry about it.
Q. How many stages are there in the Data Mining, Name them?
There are three stages and they are:
1. Model building
2. Initial Exploration
Q. In Data Integration approach, Is it possible to use SAP as a Database? Why or Why not?
No, it is not possible. This is because SAP is basically an application that is useful for handling and managing the other databases provided by third-party vendors, For example, SQL server.
Q. What data tells you about the structure of MetaObjects? What do you know about Transaction and Master Data?
Meta data gives useful information regarding the same. In addition to this, transaction and Master Data is also there. Transaction Data gives useful information that is related to regular processes and activities in a business. On the other side, as the name indicates, Master Data is the data that have key information regarding a business. For example, it contains all the customer information. It can be considered as reference data for both Meta Data and Transaction Data.
Q. Do Data Integration system models have layers in them? What is their purpose? Name them?
Yes, there are three layers in a Data Integration system models and they are database layer, presentation layer and Application layer. Their purpose is to handle security related tasks such as authenticity, data controlling, packets monitoring, handling queries and so on.
Q. What do you call a data warehouse, what is its purpose?
Every organization has some historical data related to its history. The same is stored electronically and its purpose is reporting, analyzing, and handling related processes. A warehouse can also be used for Data Integration and data management
Q. What is the difference between OLPA and OLTP?
OLTP is responsible for data collection where as OLAP purpose is to simply analyze and report on that data. OLTP are normalized systems while OLAP are not that normalized. They are used for limited data with quick operations. Both these systems are highly reliable and can be considered in Data Integration without facing any issue.
Q. Can you tell us something about the dimensional modeling?
It basically consists of fact tables and the related dimensions. When it comes to storing the different transactional measurements, Fact tables are considered. It aims simpler and quick data retrieval rather than enhanced degree of normalization. One of the common and in fact widely used proponents in dimensional modeling is Ralph Kimball. It defines limits so that data can easily be managed for integration and other similar tasks.
Q. Tell us the prime responsibilities of Data Integration Administrator?
There are certain responsibilities of a Data Integration administrator and a few of them are:
1. Configuring and monitoring the real-time services
2. Executing the batch jobs
3. Publishing batch jobs
4. Assuring Data security
5. Repository usage
6. Providing and cancelling data access to users
7. Defining Data limits
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