Data Warehousing and Its Concepts

ETL Testing

By- SrilakshmiSudhaker

Data warehousing and its Concepts:

What is Data warehouse?

Data Warehouse is a central managed and integrated database containing data from the operational sources in an organization (such as SAP, CRM, ERP system). It may gather manual inputs from users determining criteria and parameters for grouping or classifying records.

Data warehouse database contains structured data for query analysis and can be accessed by users. The data warehouse can be created or updated at any time, with minimum disruption to operational systems. It is ensured by a strategy implemented in ETL process.
A source for the data warehouse is a data extract from operational databases. The data is validated, cleansed, transformed and finally aggregated and it becomes ready to be loaded into the data warehouse.

Data warehouse is a dedicated database which contains detailed, stable, non-volatile and consistent data which can be analyzed in the time variant.
Sometimes, where only a portion of detailed data is required, it may be worth considering using a data mart.

A data mart is generated from the data warehouse and contains data focused on a given subject and data that is frequently accessed or summarized.

Data warehouse Architecture:

Data warehouse Architecture (Contd):

Advantages of Data warehouse:

Data warehouse provides a common data model for all data of interest regardless of the data's source. This makes it easier to report and analyze information than it would be if multiple data models were used to retrieve information such as sales invoices, order receipts, general ledger charges, etc.

Inconsistencies are identified and resolved prior to loading of data in the Data warehouse. This greatly simplifies reporting and analysis.

Information in the data warehouse is under the control of data warehouse users so that, even if the source system data is purged over time, the information in the warehouse can be stored safely for extended periods of time.

Because they are separate from operational systems, data warehouses provide retrieval of data without slowing down operational systems.

Data warehouses enhance the value of operational business applications, notably customer relationship management (CRM) systems.

Data warehouses facilitate decision support system applications such as trend reports (e.g., the items with the most sales in a particular area within the last two years), exception reports, and reports that show actual performance versus goals.

Disadvantages of Data Warehouse:

Data warehouses are not the optimal environment for unstructured data.

Because data must be extracted, transformed and loaded into the warehouse, there is an element of latency in data warehouse data.

Over their life, data warehouses can have high costs. Maintenance costs are high.

Data warehouses can get outdated relatively quickly. There is a cost of delivering suboptimal information to the organization.

There is often a fine line between data warehouses and operational systems. Duplicate, expensive functionality may be developed. Or, functionality may be developed in the data warehouse that, in retrospect, should have been developed in the operational systems and vice versa.

ETL Concept:

ETL is the automated and auditable data acquisition process from source system that involves one or more sub processes of data extraction, data transportation, data transformation, data consolidation, data integration, data loading and data cleaning.

E- Extracting data from source operational or archive systems which are primary source of data for the data warehouse.

T - Transforming the data – which may involve cleaning, filtering, validating and applying business rules.

L- Loading the data into the data warehouse or any other database or application that houses the data.

ETL Process:

ETL Process involves the Extraction, Transformation and Loading Process.

Extraction:

The first part of an ETL process involves extracting the data from the source systems. Most data warehousing projects consolidate data from different source systems. Each separate system may also use a different data format. Common data source formats are relational databases and flat files, but may include non-relational database structures such as Information Management System (IMS) or other data structures such as Virtual Storage Access Method (VSAM) or Indexed Sequential Access Method (ISAM), or even fetching from outside sources such as through web spidering or screen-scraping. Extraction converts the data into a format for transformation processing.

An intrinsic part of the extraction involves the parsing of extracted data, resulting in a check if the data meets an expected pattern or structure. If not, the data may be rejected entirely or in part.

Transformation:

Transformation is the series of tasks that prepares the data for loading into the warehouse. Once data is secured, you have worry about its format or structure. Because it will be not be in the format needed for the target. Example the grain level, data type, might be different. Data cannot be used as it is. Some rules and functions need to be applied to transform the data

One of the purposes of ETL is to consolidate the data in a central repository or to bring it at one logical or physical place. Data can be consolidated from similar systems, different subject areas, etc.

ETL must support data integration for the data coming from multiple sources and data coming at different times. This has to be seamless operation. This will avoid overwriting existing data, creating duplicate data or even worst simply unable to load the data in the target

Loading:

Loading process is critical to integration and consolidation. Loading process decides the modality of how the data is added in the warehouse or simply rejected. Methods like addition, Updating or deleting are executed at this step. What happens to the existing data? Should the old data be deleted because of new information? Or should the data be archived? Should the data be treated as additional data to the existing one?

So data to the data warehouse has to loaded with utmost care for which data auditing process can only establish the confidence level. This auditing process normally happens after the loading of data.

List of ETL tools:

Below is the list of ETL Tools available in the market:

List of ETL Tools / ETL Vendors
Oracle Warehouse Builder (OWB) / Oracle
Data Integrator & Data Services / SAP Business Objects
IBM Information Server (Datastage) / IBM
SAS Data Integration Studio / SAS Institute
PowerCenter / Informatica
Elixir Repertoire / Elixir
Data Migrator / Information Builders
SQL Server Integration Services / Microsoft
Talend Open Studio / Talend
DataFlow Manager / Pitney Bowes Business Insight
Data Integrator / Pervasive
OpenTextIntegrationCenter / Open Text
Transformation Manager / ETL Solutions Ltd.
Data Manager/Decision Stream / IBM (Cognos)
Clover ETL / Javlin
ETL4ALL / IKAN
DB2 Warehouse Edition / IBM
Pentaho Data Integration / Pentaho
Adeptia Integration Server / Adeptia

ETL Testing:

Following are some common goals for testing an ETL application:

Data completeness - To ensure that all expected data is loaded.

Data Quality- It promises that the ETL application correctly rejects, substitutes default values, corrects and reports invalid data.

Data transformation- This is meant for ensuring that all data is correctly transformed according to business rules and design specifications.

Performance and scalability- This is to ensure that the data loads and queries perform within expected time frames and the technical architecture is scalable.

Integration testing- It is to ensure that ETL process functions well with other upstream and downstream applications.

User-acceptance testing- It ensures the solution fulfills the users’ current expectations and also anticipates their future expectations.

Regression testing- To keep the existing functionality intact each time a new release of code is completed.

Basically data warehouse testing is divided into two categories ‘Back-end testing’ and ‘Front-end testing’. The former applies where the source systems data is compared to the end-result data in Loaded area which is the ETL testing. While the latter refers to where the user checks the data by comparing their MIS with the data that is displayed by the end-user tools.

Data Validation:

Data completeness is one of the basic ways for data validation. This is needed to verify that all expected data loads into the data warehouse. This includes the validation of all the records, fields and ensures that the full contents of each field are loaded.

Data Transformation:

Validating that the data is transformed correctly based on business rules, can be one of the most complex parts of testing an ETL application with significant transformation logic. Another way of testing is to pick up some sample records and compare them for validating data transformation manually, but this method requires manual testing steps and testers who have a good amount of experience and understand of the ETL logic.

Data Warehouse Testing Life Cycle:

Like any other piece of software a DW implementation undergoes the natural cycle of Unit testing, System testing, Regression testing, Integration testing and Acceptance testing.

Unit testing: Traditionally this has been the task of the developer. This is a white-box testing to ensure the module or component is coded as per agreed upon design specifications. The developer should focus on the following:

a) That all inbound and outbound directory structures are created properly with appropriate permissions and sufficient disk space. All tables used during the ETLare present with necessary privileges.

b) The ETL routines give expected results:

i. All transformation logics work as designed from source till target

ii. Boundary conditions are satisfied− e.g. check for date fields with leap year dates

iii. Surrogate keys have been generated properly

iv. NULL values have been populated where expected

v. Rejects have occurred where expected and log for rejects is created with sufficient details

vi. Error recovery methods

vii. Auditing is done properly

c) That the data loaded into the target is complete:

i. All source data that is expected to get loaded into target, actually get loaded− compare counts between source and target and use data profiling tools

ii. All fields are loaded with full contents− i.e. no data field is truncated while transforming

iii. No duplicates are loaded

iv. Aggregations take place in the target properly

v. Data integrity constraints are properly taken care of

System testing: Generally the QA team owns this responsibility. For them the design document is the bible and the entire set of test cases is directly based upon it. Here we test for the functionality of the application and mostly it is black-box. The major challenge here is preparation of test data. An intelligently designed input dataset can bring out the flaws in the application more quickly. Wherever possible use production-like data. You may also use data generation tools or customized tools of your own to create test data. We must test for all possible combinations of input and specifically check out the errors and exceptions. An unbiased approach is required to ensure maximum efficiency. Knowledge of the business process is an added advantage since we must be able to interpret the results functionally and not just code-wise.

The QA team must test for:

Data completeness− match source to target counts terms of business. Also the load windows refresh period for the DW and the views created should be signed off from users.
Data aggregations− match aggregated data against staging tables.
Granularity of data is as per specifications.
Error logs and audit tables are generated and populated properly.
Notifications to IT and/or business are generated in proper format

Regression testing: A DW application is not a one-time solution. Possibly it is the best example of an incremental design where requirements are enhanced and refined quite often based on business needs and feedbacks. In such a situation it is very critical to test that the existing functionalities of a DW application are not messed up whenever an enhancement is made to it. Generally this is done by running all functional tests for existing code whenever a new piece of code is introduced. However, a better strategy could be to preserve earlier test input data and result sets and running the same again. Now the new results could be compared against the older ones to ensure proper functionality.

Integration testing: This is done to ensure that the application developed works from an end-to-end perspective. Here we must consider the compatibility of the DW application with upstream and downstream flows. We need to ensure for data integrity across the flow. Our test strategy should include testing for:

i. Sequence of jobs to be executed with job dependencies and scheduling

ii. Re-startability of jobs in case of failures

iii. Generation of error logs

iv. Cleanup scripts for the environment including database

This activity is a combined responsibility and participation of experts from all related applications is a must in order to avoid misinterpretation of results.

Acceptance testing: This is the most critical part because here the actual users validate your output datasets. They are the best judges to ensure that the application works as expected by them. However, business users may not have proper ETL knowledge. Hence, the development and test team should be ready to provide answers regarding ETL process that relate to data population. The test team must have sufficient business knowledge to translate the results in terms of business. Also the load windows, refresh period for the DW and the views created should be signed off from users.

Performance testing: In addition to the above tests a DW must necessarily go through another phase called performance testing. Any DW application is designed to be scalable and robust. Therefore, when it goes into production environment, it should not cause performance problems. Here, we must test the system with huge volume of data. We must ensure that the load window is met even under such volumes. This phase should involve DBA team, and ETL expert and others who can review and validate your code for optimization.

Summary:

Testing a DW application should be done with a sense of utmost responsibility. A bug in a DW traced at a later stage results in unpredictable losses. And the task is even more difficult in the absence of any single end-to-end testing tool. So the strategies for testing should be methodically developed, refined and streamlined. This is also true since the requirements of a DW are often dynamically changing. Under such circumstances repeated discussions with development team and users is of utmost importance to the test team. Another area of concern is test coverage. This has to be reviewed multiple times to ensure completeness of testing. Always remember, a DW tester must go an extra mile to ensure near defect free solutions.