ETL Tools cannot vanish… They are Irreplaceable

ETL Tools are too Important to be Replaced..

ETL tools cannot vanish and Business Intelligence derived from the entire Extract, Transform, and Load process cannot fail. ETL tools have undoubtedly carved out an undisputable space when it comes to data warehousing, but not many resources are aware of their actual capabilities and powers. However, this does not imply that ETL tools can be replaced. ETL tools are simple irreplaceable because of their marked efficiency in extracting, transforming, and loading data into data warehouses – an activity that makes available significant data for business processes. As far as the predictions state, ETL tools will be appreciated till the day data-driven businesses are on the cards, and such businesses are not going to die. Thanks to digitalization, commercialization, and globalization of the globe!

ETL Process

ETL is an initialism expanding to mean E – Extract, T – Transform, and L – Load. The ETL process is as follows:

Extract:

During the extraction process, data is collected from disparate data sources or a specific subset of data is extracted from a particular source database. Extraction is done from multiple sources for the ultimate goal of deriving some meaningful business insights. This data may be heterogenous enough to include OLTP, social media data, log files, sensor data, unstructured and semi-structured data.

Transform:

The second function is transformation of the extracted data. The extracted data is then checked for validation which implies that data having a desired schema is processed further and the remaining data that fails the validation test is processed in a different way in order to make it schema-specific, and hence ready for the rest of the process that includes loading data into the data warehouse. Therefore, during the transformation phase of the ETL process data is processed to conform to a uniform schema that is accepted by the data warehouse. This transformation of data into a desired state include functions such as data formatting, splitting data, joining data, creating rows and columns, using lookup tables or creating combinations within the data.

Load:

The final step of the ETL process is loading the transformed data into the target data warehouse. This data, after transformation, is schema-specific catering to the demand of the data warehouse. Unlike the unstructured or semi-structured data available before the ETL process, the data is now structured, integrated, subject-oriented, time-variant, and non-volatile. This data is loaded to the data warehouse, thereby allowing data scientists to analyse data, gain insights, and create promising business policies.

ETL process is indeed important, and ETL tools are certainly irreplaceable.

Wikipedia throws light on the significance of ETL:

“By using an established ETL framework, one may increase one’s chances of ending up with better connectivity and scalability. . . ETL tools have started to migrate into Enterprise Application Integration, or even Enterprise Service Bus, systems that now cover much more than just the extraction, transformation, and loading of data. Many ETL vendors now have data profiling, data quality, and metadata capabilities. . . ETL tools have become a convenient tool that can be relied on to get maximum performance”

Significance of ETL tools can not go unnoticed. Data is meaningful only after the process of Extraction, Transformation, and Loading. Without ETL it is largely impossible to extract meaningful data and to transform it into a homogenous lot, ready to be stored in data warehouse. It is the process of transformation that converts data into desired state to facilitate smarter business intelligence that is applaudable and definitely towering in its excellence.

Allied to this excellence is the need to excel in the market with an excellent skill set. Since ETL tools are irreplaceable, with such a skill set, you will never have to face setbacks. Get trained in Informatica ETL and lead the market. ETLhive is considered as the best training institute in Informatica ETL and it provides hands-on training on various modules of Informatica ETL such as DataWarehousing and Business intelligence, Informatica Architecture, Informatica services, Informatica Administration, Informatica Client tools, Transformations, Advanced Informatica, and Production scenarios. Come, join, learn, and excel with ETLhive because We Deliver What We Promise!

What is Business Intelligence (BI) Testing?

Business Intelligence (BI) Testing helps companies to make decisions based on hard facts or data to progress deeper and better vision so they can manage it. Business Intelligence (BI) Testing helps to verify the data, format, and performance of the reports, subject areas and security aspects of the BI Projects. Insistence on a thorough BI Testing is key for improving the quality of the BI Reports and user adoption.

How is BI Application Testing done?

Testing BI applications is to achieve credible data. And by making the testing cycle effectively data security can be attained. A complete test strategy is the stepping stone of an effective test cycle. The strategy should cover test planning for each stage, every time the data moves and state the authority of each stakeholder

Data Acquisition during BI Testing

The prime aim of data acquisition is to ensure that all of the data is extracted that needs to be loaded in the target. During the data acquisition, development is important to understand the various data sources, the time boundaries of the data selected and any other special cases that need to be considered.

Data Integration testing of your BI app

Testing within the data integration state is the crux as data transformation takes place at this stage. Once the data is translated, thorough testing needs to be executed to ensure underlying data complies with the expected transformation logic. Business requirements get translated into transformation logic.

Checking the Data at the source for your BI app

Business Data does not come from one source and in one format it is in large data type. Make sure that the source and the type of data that it sends matches. For example, a student’s details are sent from a source for subsequent processing and storage. Make sure that the details are correct. If the GPA shows as 7, this is clearly over than the 5 point system. Such data can be damaged or corrected without taking it for further processing.

End to End Testing of your BI app

End to end testing is needed otherwise we may see issues such as data reconciliation discrepancies, even such as resource contention or deadlocks.

The factor of the data warehouse may be behaving as expected; there is no assurance that the entire system will behave the same. Thus execution and authorization of end-to-end runs are recommended.  The end-to-end runs will further help in ensuring the data quality and performance acceptance criteria are met.

Conclusion
A BI application should be thoroughly tested to reap the benefits of Big Data Analytics.
With ETLHive you can learn the skills of Data Analytics(R, Python, Spark) and AI. We are also pioneers in the field of Data Analytics and will help you adopt Data Analytics and AI in your organization.

Leading Training Institute For Software Testing In Pune

             

Software Testing Syllabus

             Itelligence is a leading advanced software training institute in Pune. We also provide online training for Software Testing. The main objective of our institute is to provide production based knowledge for each of the training modules, we also have well equipped lab and experienced trainers for every technology. These all course are job oriented flexible training program so after completion of specified technology successfully we will conduct campus drives as well as job fares. 

Testing a BI Application

What is Business Intelligence (BI) Testing?

Business Intelligence (BI) Testing helps companies to make decisions based on hard facts or data to progress deeper and better vision so they can manage it.

Business Intelligence (BI) Testing helps to verify the data, format, and performance of the reports, subject areas and security aspects of the BI Projects.

Insistence on a thorough BI Testing is key for improving the quality of the BI Reports and user adoption.

How is BI Application Testing done? 

Testing BI applications is to achieve credible data. And by making the testing cycle effectively data security can be attained.

A complete test strategy is the stepping stone of an effective test cycle.

The strategy should cover test planning for each stage, every time the data moves and state the authority of each stakeholder

• Data Acquisition during BI Testing -

The prime aim of data acquisition is to ensure that all of the data is extracted that needs to be loaded in the target.

 During the data acquisition, development is important to understand the various data sources, the time boundaries of the data selected and any other special cases that need to be considered.

• Data Integration testing of your BI app

Testing within the data integration state is the crux as data transformation takes place at this stage.

 Once the data is translated, thorough testing needs to be executed to ensure underlying data complies with the expected transformation logic. Business requirements get translated into transformation logic.

• Checking the Data at the source for your BI app

Business Data does not come from one source and in one format it is in large data type. Make sure that the source and the type of data that it sends matches. For example, a student’s details are sent from a source for subsequent processing and storage. Make sure that the details are correct. If the GPA shows as 7, this is clearly over than the 5 point system.Such data can be damaged or corrected without taking it for further processing.

• End to End Testing of your BI app 

End to end testing is needed otherwise we may see issues such as data reconciliation discrepancies, even such as resource contention or deadlocks.

The factor of the data warehouse may be behaving as expected; there is no assurance that the entire system will behave the same.

Thus execution and authorization of end-to-end runs are recommended.  The end-to-end runs will further help in ensuring the data quality and performance acceptance criteria are met.

Conclusion

A BI application should be thoroughly tested to reap the benefits of Big Data Analytics.

With ETLHive you can learn the skills of Data Analytics(R, Python, Spark) and AI. We are also pioneers in the field of Data Analytics and will help you adopt Data Analytics and AI in your organization.