Testing the ETL System Is the Data Right in VS .NET

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Testing the ETL System Is the Data Right
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Initial testing of the ETL system will be performed by the technical team members This ensures that the programs and processes run without errors and that they correctly perform their designed function This often includes basic checking to ensure that the data ows properly The team will make sure that if 100,000 transactions came in, then 100,000 transactions were processed Other checks may include con rming that the total units shipped on the incoming transactions matches the total units shipped from the resulting fact
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Implementation: Building the Database
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table The basic testing may also ensure that each customer is assigned a credit pro le and that each product has a package type Testing that the programs and processes run correctly is only part of the overall testing and quality checks that must be done Additional work is needed to ensure that the data itself is accurate The ETL system may have correctly processed those 100,000 transactions, but if all of the sales were off by a decimal point, then the tests previously mentioned would not detect it The business must be an integral part of the testing and quality assurance process to ensure that the database is correct A great deal of work is required to validate the database when it is rst loaded There will also be ongoing work to ensure that each time the ETL system runs, accurate data is loaded Who decides if the data is right The comprehensive test plan developed earlier by the business provides the basis to assess the data Representatives from the business community may be involved in running the test cases, but their involvement is critical in evaluating the results This needs to be someone who has the knowledge to recognize that an increase in market share of 14% is not possible, as changes in market share are usually measured in tenths of a percent
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T I P The ETL system should include many validation steps to check for data
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problems before the data is loaded Leverage your data access or business intelligence tool to help with data validation These tools are designed to identify exceptions, compare results with previous periods, and compare results to thresholds Develop a set of queries or reports that can be run after the data has been loaded as a nal data quality check
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Why Does It Take So Long and Cost So Much
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If all of the systems in the organization contained clean, reliable data with consistent production identi ers, then developing a robust ETL system would not take so long However, it is rare to nd an organization that has such clean, integrated systems already in place Therefore, with the reality that the source systems were designed without the requirement to integrate easily with one another, the data warehouse must overcome these problems A variety of factors contribute to the time it takes to build the ETL system Several of the biggest contributors include the following: Long decision cycle: Many choices need to be made throughout the design and development of the ETL system Often, decisions can be made based upon the requirements, or a single person or area needs to be consulted rst However, in many instances the decision impacts multiple areas It can take a long time to get the appropriate people
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Part IV
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Building the Project
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together to understand the choice, come to an agreement, and commit to the direction New perspectives on the data: As part of building the data warehouse, the data may be looked at in ways that have never been attempted before As the business and the marketplace evolve, new requirements also emerge These may result in identi cation of anomalies and challenges in the underlying source systems As new perspectives are de ned, the data may not yield meaningful or helpful results For example, it may take several iterations to de ne the business rule to identify long-term customers How many years are needed to consider someone a long-term customer What if the customer leaves and then comes back again do you count all of the years or just consecutive years Lack of business input: There are also many demands on the business community Business analysts who work with the data today are often swamped with demands for data, reports, and analyses In addition to a full load of regular reporting, there are often ad hoc requests that need attention However, these analysts have the most knowledge about how data is manipulated and which calculations are currently used Sometimes, the team must go through a series of meetings before identifying the person who can really help A lot of hard work: Often, developing the ETL system takes a lot of time and money simply because there is a lot of work that must be done This can be due to the complexity and volume of data or because of a lack of well-structured and reliable data to begin with Obviously, if the team is pulling data from two data sources, the integration work is much smaller than integrating data from ten different systems Insuf cient data pro ling: Whether the organization is using a sophisticated data pro ling tool or performing detailed data analysis by running queries against the database, it is important to understand what data is being stored When the actual data contents have not been studied, problems will arise during ETL development The types of data problems that emerge include nding that the data element is empty or that the contents of a data element are not what was expected Thorough study of the data prior to designing and building the ETL system should obviate these issues Indirect communication: Looking at the day-to-day work, questions often arise for which the team needs input from another IT resource or someone from the business community If the project team must go through a liaison to gain access to other IT or business people, this can greatly lengthen the project schedule While it may not seem like a big
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