Data-driven insights are required to make critical business decisions. Indium with its in-depth knowledge and expertise in data assurance helps to balance the opportunities and risks that your enterprise may face from data operations. Our Focus is on mitigating data risk at all levels – from individual attributes right through to organizational and regulatory risk.
We always deliver on time and ensure the data is of the right quality so that the end result is in line with your business environment. Indium has a team of testers who can assist on data warehousing, test data management, data migration and BI projects.
Data Quality check and ETL testing
Data Model Validations
Application upgrade testing
Creating Business Rule and verification
Cloud BI & Report validation
Reports Performance & security Test
User access Testing & Data Regression Testing
ETL Testing Stages
Performance Testing in ETL
- To validate if the ETL system can handle multiple users and transactions.
- To check if performance-related constraints are unearthed in source databases, target databases, sessions, system, etc.
- To confirm scalability and performance by ensuring that data is loaded within the defined SLA
Indium’s Data Validation testing process ensures the user to check that the provided data, they deal with, is valid or complete.
Indium also ensures the following details as part of Data Validation testing,
- Compare record count (which is preliminary test) between data sources
- Ensure all projected data are loaded in target without any loss or truncation in original data
- Rejects invalid data and report
- Compare unique values and report
- Validate the format of the data
DW/BI Test Strategy
The backbone of any test cycle is a comprehensive test strategy. Our DW/BI test strategy covers test planning at every stage. The key areas our strategy focuses to ensure testing readiness are
Set up the test environment
The scope of testing
Availability of Test data
Data quality and performance acceptance criteria
To ensure data accuracy, completeness, security, consistency, and reliability throughout, it is necessary to test all these aspects at every data entry point in the Business Intelligence architecture.
In the end, all we need is credible data. That is the goal of BI testing and we make sure the data is credible by creating an effective testing cycle.
BI Testing strategy involves 4 important phases
Data Acquisition is one of the important phases of DW/BI testing. Here we understand the different data sources and look for any special cases that have to be considered. In this phase, we focus on
- The availability of data sources
- Data validation
- Data profiling
Data Integration is crucial as data transformation happens at this phase. In this phase, we focus on
- Validating the Data Model
- Validating the Source to Target Mapping
- Reviewing the Data Dictionary
The loading of data into the data warehouse happens at this phase. The loading of data can happen just once or multiple times. In data storage phase we focus on
- Performance and Scalability of the system
- Validating data loads based on time intervals
- Parallel Execution and Preference in the ETL process
- Verifying exception handling, error logging and recovery from failure points.
Data Presentation is the final phase of the testing cycle. Here the test data is represented graphically. Our key focus here will be on
- End to End Testing
- Validating the Report Model.
The Indium Advantage:
Indium can help overcome the basic challenges in DW/BI testing
- Overcome the Variety, Volume and Complexity of Data
- Ensure there are no data anomalies that occur from various data sources
- Ensure there is no data loss during the process of data integration and handshaking within sources
- Testers with hands-on experience in executing data validation and verification process
- Save time on the entire process, as it is time-consuming
Benchmarking the Data warehouse-based application
Performance and Scalability should be included as part of any comprehensive data warehouse benchmark.
- Append more hardware and measure the impact on performance on the same data set and workload.
- Append more hardware to accommodate additional users. Measure the performance for larger numbers of concurrent users after adding more hardware.
- Append more hardware to accommodate extra data. Measure the performance on the same workload over a larger data volume.
The simplest area to include in a benchmark is user scalability. A benchmark’s query workload should never solely consist of running queries as a single user. Data warehouse benchmarks should measure user concurrency
Another area is data scalability. Most data warehouse-based application stores more historical data. Hence, we should consider running query tests for 3 years of data and 5 years of data. Data scalability could also be measured by maintaining at least 2 years of data,
The critical parameters to consider are the number of users, the amount of real data, and the server configuration. The performance benchmark test should be based upon how we anticipate the workload to change in the future as per the SLA agreed.
Data Assurance is completely different from testing traditional software testing applications. We have strong expertise in DW/BI Testing platform, and we have been in testing for more than two decades.
- Good experience in data quality testing in agile env.,
- Test automation of high volume and high complexity data warehouse/BI systems
- Early identification of Data related issues
- Increased coverage
- Improved data quality
- Reduction in test cycle
- Faster time in market