The data warehouse testing is also called ETL testing.
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The data warehouse testing or ETL testing includes the following techniques:
- Data transformation testing: Here the data from various sources are collected and verified to transform as per the business rules .
2. Data transfer count testing: counting the target of records loaded in the data warehouse sources should match with the expected count.
3. Data transfer testing: Verifying all the data which is collected is loaded properly in the data warehouse without any loss or truncating.
4. Data Quality testing: here the quality of the data is tested it makes sure that improper and invalid data is reported and replaced with proper default values.
5. Performance Testing: Here it verifies that the data is loaded in data warehouse within the prescribed time slots to improve its performance and scalability.
6. Production data testing: Validating or checking the data in production process against the data which is in sources.
7. Data Integration Testing: Verifying that all data from the sources are loaded properly and checked in each point and then transformed properly.
8. Software Migration Testing: In this testing it is made sure that the data from the data warehouse is working efficiently in the new environment or platform.
9. Data and Constraint case check: In this type of testing data type, length, constraints are checked.
10. Data integrity testing: here it is checked for any duplicate data in the target systems.
Database testing can be often confused with data warehouse testing. Database testing is done on smaller volumes of normalised data to validate the changes that affect the data from the software application. Data warehouse testing is performed on huge volumes of data that is not normalised.
Check your understanding:
1. Identify the possible challenges in data warehouse testing.
49 Responses
The challenges in data warehouse testing are:
• Unavailability of inclusive test bed at times
• Lack of proper flow of business information
• Loss of data might be there during the ETL process
• Existence of many ambiguous software requirements
• Existence of apparent trouble acquiring and building test data
• Production sample data is not a true representation of all possible business process
• Incompatible & duplicate data.
Challenges in data warehouse testing:
.Loss of data (ETL, extract, load, transform)
. Duplicate data
.Lack of proper flow of business information
.Difficulty extracting data from other source
Difficulty of building test data
.Unavailability of inclusive test bed at times
.Volume and complex data
The challenges in data warehouse testing
-Loss of data
-incorrect data
-data not working efficiently in new environment
-Unable to load /transform
-duplication of data
Challenges in data warehouse testing:
Loss of data during ETL
Duplicate data and incompatibility
Data volume and complexity is huge
Lack of proper flow of business information
Errors occur while extracting data from different sources
*Handling huge volume of data
*Loss of data
*Duplicate of Data
*Transferring Complex Data Structure
Challenges in data warehouse testing are:
– Data loss during the ETL process.
– Incorrect, Incomplete or duplicate data.
– Due to huge volume of data and contains historical data, testing is complex.
– Testers normally don’t have the access to see job schedules in the ETL tool.
– Tough to build test cases because of high and complex volume.
– Involves various complex sql concepts for data validation.
– Unstable Testing environment delay the development and testing of a process.
– Testers normally don’t have an idea of end-user report and business flow of information.
Because data warehouse testing uses a huge volume of data, there are challenges that comes with it: Below are the sample challenges:
Data loss
duplicate data
tough to generate and build test cases because volume is too high and complex
unstable testing environment that will results to delay of development and testing
Challenges in datawarehouse testing:
– complexity of data
– high volumes of data
-duplicate data issues
– loss of data
– data should be able to work in new environments.
Data warehouse testing has many challenges:
– Dealing with large volumes of data
– Counting the target records in the large databases
– Time consuming
– Data transfers errors, data loss may be frequent
– Checking for duplicate data
– Loss of data ETL, extract, load, transform
– Difficulty of building test data
– Incorrect data
– Unable to load /transform
– Data volume and perplexity is huge
– Lack of proper flow of business information
– Transferring complex Data Structure
Possible challenges in data warehouse testing:
Not possible to test real time data.
Data will be Huge to visually identify the issues if any.
Difficult to cover all test case senarios.
Time consuming.
Difficult to test on real time production environment.
Loss of data
Data mapping issues
Duplicate data
Handling Huge volumes of data
Time consuming
The few challenges in ETL or data warehouse testing are:
Loss of data during transfer
duplicating data
Proper storage of large amounts of data
Eliminating invalid data
loading of data in prescribed time
performance of transformed data in new environment
The challenges in data warehouse testing are:
-loss of data
-incorect data
-duplication of data
-unable to load, transform
-data not working efficiently
-data volume and complexity.
The main challenges of Data warehouse testing includes:
• Data loss during testing.
• Duplicate data and Incompatibility.
• Lack of inclusive test bed.
• Testers have no benefits to execute ETL jobs by their own.
• Data volume and complexity is huge.
• Inefficient in procedures and business process.
• Inconvenience securing and building test data.
• Absence of business course information.
Identify the possible challenges in data warehouse testing.
1. Data warehouse testing deals with large volumes of data from multiple system, a small change may have impact on multiple places and it may affect the project schedule.
2.Since it deals with huge volumes of data complete testing may not be possible due to time and budget constraints.
3.Due to security issues sufficient data may not be available from various systems for testing
Lots of challenges are involved in data warehousing testing which are as follows:
1.Errors occurs while extracting data from different sources.
2.Loss of data collected and stored.
3.Time consuming.
4.Lack of flow of business information.
5.Duplicate data.
6.Inefficiently working of data from data warehouse in new environment.
7.Huge volume of data.
8.Budget and cost involved.
Unavailability of inclusive test bed at times
Lack of proper flow of business information
Loss of data might be there during the ETL process
Ambiguous software requirements
Trouble acquiring and building test data
Production sample data is not a true representative of all possible business processes
Not having a good knowledge of SQL coding skills
Certain testing strategies are time consuming
Data warehousing testing is done with large volume of databases to assure that data that has been loaded from source to destination after business transformation is accurate. some challenges in this testing are:
1. Performing data completeness checks for transformed columns is very tricky.
2. Certain testing strategies used are time consuming.
3. Unavailability of inclusive test bed at times.
4. Lack of proper flow of business information.
5. Loss of data might be there during ETL Process.
6. Incorrect, incomplete or duplicate data.
7. Unstable testing environment delay the development and testing of a process.
8.sometimes the testers are not provided with the source to target mapping information.
9. Tough to generate and build test cases, as data volume is too high and complex.
10. Loss of data during theETL PROCESS.
Some Challenges of ETL or Data Warehouse Testing are:
– Ambiguous or duplicate data
– Very large volumes of data
– Tester privileges to perform data transfer
– test data availability for the business function
– missing business flow information
– loss off data during ETL process
Identify the possible challenges in Data Warehouse Testing.
Loss of data during ETL
Duplicate data and incompatibility
Data volume and complexity is huge
Lack of proper flow of business information
Errors occur while extracting data from different sources
Some of the challanges in data ware house testing:
– verification of data loading for performance and scalability
-Ensuring data working efficiency in new environment
– Duplicacy of data
– Loss of data during ETL process
-time conumption
– challanges in transformation and loading of data
– Lack of proper flow of business information
As we know that Data warehouse testing must deal with a huge volume of data to be tested. On like ordinary testing which identifies the defeats and validates the quality, and correctness of the client’s requirements. Because Data warehouse testing are done by more expects tester, there is a credibility of data testing integrity. However, there many possible challenges which faces the data warehouse testing:
1. Due to the large volume of information to be tested, the possibility of data loss is real
2. Duplication of data is also real
3. Incompatibility of data is common
4. Business decision making can be delayed or become erroneous due incomplete or missing information.
Unstabilized source systems, This is because any bug in the source systems potentially injects unwarranted defects in data warehouse. Disparate data sources add to data inconsistency
Prioritizing performance . Data warehouses should be built for performance rather than tuned for performance.
Setting realistic goal
Performance by design Performance is a consequence of design. So performance goals can be best addressed at the time of designing. If that’s not done, meeting up performance criteria can be an overwhelming challenge.
Like anything in data warehousing, performance should be subjected to testing
Because of such high dependencies, regression testing requires lot of planning. Time consuming.
Reconciliation is complex
the challenges of making a newly built data warehouse acceptable to the users. No matter how good or great you think your data warehouse is, unless the users accept and use it wholeheartedly the project will be considered as failure. In fact, most of the data warehouse projects fail in this phase alone.
. Inconsistent data, duplicates, logic conflicts, and missing data all result in data quality challenges.
b. Lack of proper flow of business information
c. Loss of data might be there during the ETL process
d. Existence of many ambiguous software requirements
e. Unstable testing environment
f. Fault in business process and procedures
g. Volume and complexity of data are very huge
h. Trouble acquiring and building test data
i. Cost
j. Performance
k. User expectation
DATA WAREHOUSE TESTING
CHALLENGES IN DATA WAREHOUSE TESTING:
1. Validity of data & source, duplicate, incorrect, etc
2. Time consuming in testing
3. Volume of data
4. Incorrect data
1. Identify the possible challenges in data warehouse testing.
~Testers donot have the privilege to execute the ETL jobs on their own
~Incompatible and duplicate data
~Unavailability of inclusive test bed
~faults in business process and procedures
~volume and complexity of huge data
~trouble acquiring and building test data
~missing business flow information
~loss of data during ETL process
Some of the important ETL Testing Challenges are:
Unavailability of inclusive test bed at times
Lack of proper flow of business information
Loss of data might be there during the ETL process
Existence of many ambiguous software requirements
Existence of apparent trouble acquiring and building test data
Production sample data is not a true representation of all possible business processes
Some of the important issues with Data Warehouse Testing are:
Data Warehouse/ETL testing requires SQL programming: This has become a major issue as most of the testers are manual testers and have limited SQL coding skills, thus making data testing very difficult
Performing Data completeness checks for transformed columns is tricky
Certain testing strategies used are time consuming.
Some issues/possible errors that could occur with Data warehousing:
The common skills required by the data warehouse testers are sql basics, knowledge of database, etc. However, if the data warehouse testers have insufficient skills that could lead to incorrect interpretations of the data.
Errors could occur during the Data transformation testing where the data from various sources are collected and verified to transform as per the business rules.
During Data transfer testing the data could be loaded incorrectly. There could be loss of data due or truncation.
Improper and invalid data might not be reported or replaced with the incorrect default values.
In the Software Migration Testing, the data from the data warehouse might not be working efficiently in the new environment or platform.
During the Data integrity testing: duplicate data could be missed in the target systems.
Possible challenges are :
1.Ambiguous or duplicate data
2.Very large volumes of data
3. test data availability for the business function
4. missing business flow information
5.loss off data during ETL process
6.Certain testing strategies used are time consuming.
7.Improper flow of business information.
8.Loss of data might be there during ETL Process.
9.Incorrect, incomplete or duplicate data
Data loss during the ETL process.
Incorrect, incomplete or duplicate data.
The system contains historical data, so the data volume is too large and extremely complex to perform ETL testing in the target system.
Tough to generate and build test cases, as data volume is too high and complex
ETL testing involves various complex SQL concepts for data validation in the target system.
1.Unavailability of inclusive test bed at times
2. Lack of proper flow of business information
3. Loss of data might be there during the ETL process
4. Existence of many ambiguous software requirements
5. Existence of apparent trouble acquiring and building test data
6. Production sample data is not a true representation of all possible business processes
Data Warehouse testing/ETL testing: The Data warehouse testing is one of the different types of testing performed when the project involves the huge volume of data.
Challenges:
• Incompatible and duplicate data
• Loss of data during ETL process
• Unavailability of the inclusive testbed
• Testers have no privileges to execute ETL jobs by their own
• Volume and complexity of data are very huge
• Fault in business process and procedures
• Trouble acquiring and building test data
• Unstable testing environment
• Missing business flow information
Possible Challenges in data warehouse testing are
1.Data loss during ETL testing.
2.Duplicate/incorrect/incomplete data
3.Production sample data is not a true representation of all possible business processes
4.Data volume and complexity is huge.
5.Since there is lot of data for testing in various tables and business logic is complex hence complete testing
may not be practically possible due to time and budget constraints whereas client may expect the testing team to use all the available test data for testing
Possible Challenges in data warehouse testing are-
Huge volume of data stored in the data ware house. Data used by the organization for analysis and decision. Data ware house testing has lots of challenge like
1 Data integration which is collected from the verious resources. Data integrating testing is very important.
2. Data security: security testing is very important to find out the breaches
3.Data Quality testing: the Quality of the data is tested it makes sure that improper and invalid data is reported and replaced with proper default values.
4.Data transformation testing: Here the data from various sources are collected and verified to transform as per the business rule
Data warehouse testing is done on a high amount of data , the challenges can be due to not implementation of proper transformation rules, duplication of data, missing business rules for data and complexity of data .
Data warehouse testing is crucial to the process of ensuring the quality of information for the best possible results.
Data Security
Data Quality
Data duplication
Data loss
Possible challenges in data warehouse testing:
Loss of data might be there during ETL process.
Existence of many ambiguous software requirements.
Production sample data is not a true representation of all possible business process.
Certain testing strategy used are time consuming.
Lack of proper flow of business information.
I identify the possible challenges in data warehouse testing.
Data loss
Data transfer
Data equality
Data transformation
Data update
Data handling
Challenges with ETL:
Expensive
loss of data during transfer
complexity in testing
timing
reduced quality
duplicate data
1. Identify the possible challenges in data warehouse testing.
– the volume of data being tested
-test bed availability
-duplication of data
Possible challenges in data warehouse testing are:
High cost
loss of data
time sensitive
lack of proper flow in business information data volume
test bed availability
1.Loss of data when it is transferred to the data warehouse.
2.Improper, invalid and duplicate data.
3. Reduced quality.
4.difficulty in working efficiently in a new environment.
Challenges in the data warehouse testing-
1.Dealing with large volumes of data
2.Counting of target records loaded in the data warehouse sources to match with the expected count.
3.Checking for any duplicate data in the target systems.
4.Verification of all data from all sources to be loaded, checked and transformed properly.
5.Verification of all the data to be loaded within the prescribed time slots to improve performance and scalability.
6.Efficient working of the data in the new environment or platform.
7.Improper and invalid data to be reported and replaced with proper default value.
8.Loss of data during ETL.
Challenges in data warehouse testing:
 Data loss during the ETL process
 Incorrect, incomplete or duplicate date
 Datawarehouse system contains historical data so the data volume is tto large and extremely complex to perform ETL testing in the target system.
 Tough to generate and build test cases as the data volume is too high and complex
 ETL testing involves various complex SQL concepts for data validation in the target system.
 Sometimes the testers are not provided with the source to target mapping information.
 Unstable testing environment delay the development and testing of a process.
Possible challenges in data warehouse testing:
Data loss during the ETL process
Incorrect , incomplete or duplicate data
Managing the data structure and optimization
Data Quality
Data Accuracy
Cost of data warehousing
Managing user expectations
Adjusting to non technical users.
The Data warehouse testing is one type of testing that performed when the project involves a huge volume of data. Data warehouse is used in Business Intelligence which helps any organization to make proper decisions. The common skills required by the data warehouse testers are SQL basics, knowledge of databases, etc.
The challenges in Data warehouse testing are:
a) Data accuracy.
b) Duplicate Data.
c) Checking if all the data is loaded properly into the production environment.
d) Performance.
In data warehouse testing data is extracted from different databases, then transformed and loaded to the data warehouse via ETL. Since the database has data in a raw form and in an unorganized manner and involves huge data transfer, therefore data warehouse testing involves many challenges that needs to be faced. Some of those are listed below:
1. We have to ensure that the total number of data records extracted from the source matches with the target count.
2. All the data needs to be checked if it has been loaded properly in data warehouse and nothing has been lost.
3. Also, data needs to be verified if it is transformed and loaded as per the business requirements.
4. There can be some invalid data that needs to be reported and replaced with the correct value.
5. It’s important to verify that the data produced in the target system matches with the source system.
6. Data performance is important too to check the speed and accuracy of the data.
7. Another challenge is that we have to check if the data loaded is working appropriately in the new software environment.
8. All the data from different sources need to be validated if it is integrated, transformed and loaded properly