ETL Testing

ETL Testing Process:

Similar to any other testing that lies under Independent Verification and Validation, ETL also go through the same phase.

-Business and requirement understanding

-Validating

-Test Estimation

-Test planning based on the inputs from test estimation and business requirement

-Designing test cases and test scenarios from all the available inputs

-Once all the test cases are ready and are approved, testing team proceed to perform pre--execution check and test data preparation for testing

-Lastly execution is performed till exit criteria are met

-Upon successful completion summary report is prepared and closure process is done.

It is necessary to define test strategy which should be mutually accepted by stakeholders before starting actual testing. A well-defined test strategy will make sure that correct approach has been followed meeting the testing aspiration. ETL testing might require writing SQL statements extensively by testing team or may be tailoring the SQL provided by development team. In any case testing team must be aware of the results they are trying to get using those SQL statements.

Difference between Database and Data Warehouse Testing

There is a popular misunderstanding that database testing and data warehouse is similar while the fact is that both hold different direction in testing.

 Database testing is done using smaller scale of data normally with OLTP (Online transaction processing) type of databases while data warehouse testing is done with large volume with data involving OLAP (online analytical processing) databases.

 In database testing normally data is consistently injected from uniform sources while in data warehouse testing most of the data comes from different kind of data sources which are sequentially inconsistent.

We generally perform only CRUD (Create, read, update and delete) operation in database testing while in data warehouse testing we use read-only (Select) operation.

Normalized databases are used in DB testing while demoralized DB is used in data warehouse testing.

There are number of universal verifications that have to be carried out for any kind of data warehouse testing. Below is the list of objects that are treated as essential for validation in ETL testing:

- Verify that data transformation from source to destination works as expected
- Verify that expected data is added in target system
- Verify that all DB fields and field data is loaded without any truncation
- Verify data checksum for record count match
- Verify that for rejected data proper error logs are generated with all details
- Verify NULL value fields
- Verify that duplicate data is not loaded
- Verify data integrity

ETL Testing Challenges:

ETL testing is quite different from conventional testing. There are many challenges we faced while performing data warehouse testing. Here is the list of few ETL testing challenges I experienced on my project:
- Incompatible and duplicate data.
- Loss of data during ETL process.
- Unavailability of inclusive test bed.
- Testers have no privileges to execute ETL jobs by their own.
- Volume and complexity of data is very huge.
- Fault in business process and procedures.
- Trouble acquiring and building test data.
- Missing business flow information.

Data is important for businesses to make the critical business decisions. ETL testing plays a significant role validating and ensuring that the business information is exact, consistent and reliable. Also, it minimizes hazard of data loss in production.

Hope these tips will help ensure your ETL process is accurate and the data warehouse build by this is a competitive advantage for your business.

Moreover, ETL or Data warehouse testing is categorized into four different areas irrespective of technology or ETL tools used:

New Data Warehouse Testing – New DW is built and verified from scratch. Data input is taken from customer requirements and different data sources and new data warehouse is build and verified with the help of ETL tools.

Migration Testing – In this type of project customer will have an existing DW and ETL performing the job but they are looking to bag new tool in order to improve efficiency.

Change Request – In this type of project new data is added from different sources to an existing DW. Also, there might be a condition where customer needs to change their existing business rule or they might integrate the new rule.

Report Testing – Report are the end result of any Data Warehouse and the basic propose for which DW is build. Report must be tested by validating layout, data in the report and calculation.

Why do organizations need Data Warehouse?

Organizations with organized IT practices are looking forward to create a next level of technology transformation. They are now trying to make themselves much more operational with easy-to-interoperate data. Having said that data is most important part of any organization, it may be everyday data or historical data. Data is backbone of any report and reports are the baseline on which all the vital management decisions are taken.

Most of the companies are taking a step forward for constructing their data warehouse to store and monitor real time data as well as historical data. Crafting an efficient data warehouse is not an easy job. Many organizations have distributed departments with different applications running on distributed technology. ETL tool is employed in order to make a flawless integration between different data sources from different departments. ETL tool will work as an integrator, extracting data from different sources; transforming it in preferred format based on the business transformation rules and loading it in cohesive DB known are Data Warehouse.

What is a staging area?

Do we need it? What is the purpose of a staging area?

Staging area is place where you hold temporary tables on data warehouse server. Staging tables are connected to work area or fact tables. We basically need staging area to hold the data, and perform data cleansing and merging, before loading the data into warehouse.

Decoding Data Mapping Sheets - ETL Process

An ETL (Extract Transform Load) process is all about moving a variety of data from Source System to the destination Data Warehouse System by taking data through a number of extraction, transformation, data cleansing & data validation processes.

Just imagine how easy it will get for someone as an ETL developer if he gets a chance to visualize all the transformations & business rules up front in an easy to interpret format. This is where mapping sheets come into picture.  

A carefully designed mapping sheet up-front can save a lot of pain as handling mapping information increasingly gets difficult as application grows with time. Only downside of using them is it takes good effort to create them & then keeping them up-to-date. But, trust me, rewards of using them easily outnumber the pain of not maintaining one as the system grows overtime

Each data migration project uses mapping sheets in one form or the other but the one which I have used too often & has worked exceptionally well for me is what I am detailing in this here.

To start with a sample Mapping Sheet would look something like this for any given entity as represented in Figure 1. The sheet can be extended further by using multiple sheets in the Excel workbook to represent other entities of the system.

Fig 1 – Sample Mapping Sheet structure

Generally, the extreme left of the sheet represents data from the Source System or the staging area. The middle layer represents data validation, data verification, data cleansing & business rule validation with a number of data-centric transformation rules in place. There can be more than 1 transformation layer in the centre depending upon how complex the ETL process is. The extreme right mostly represent Data Warehouse system.

In the Figure 2 below, I have taken example of Employee table in staging area to represent how actual table structure from database gets represented in a mapping sheet.

Carefully looking at the snapshot in figure 2, it provides almost all the information related to a table in a database. The information includes everything like - Schema name (stg), Name of the Table [Employee] along with name of the columns, data types & whether they allows NULL or NOT NULLS values. For eg - EmployeeID is INT & NOT NULL whereas EmployeeName is going to be varchar(255) NULL column.

On a similar line, mapping sheets can be extended to further represent structure of the given tables across different layers. Figure 3 below further helps you visualize how a field gets mapped, validated & transformed through different layers of the ETL process while being migrated from Source System to ODS to DW.

Fig 3 – Transformation rules at the table/field level

 Another efficient use of mapping sheet would be to documnt Business rules in layman’s term against respective field right next to them. Please refer to Figure 4 below. The figure represents only a selective few & a very basic level transformation rules. Please note, the transformation rules represented here are only from informative purpose & real time transformation could vary considerably depending upon project requirements. The mapping sheets only works as placeholders to store transformation information.

Some of the sample business rules could be like

1.  Use database defaults to set to Current Date & Time for CreatedOn, ModifiedOn fields.

2.  Use SQL Case statement to set a field to an integer value in warehouse depending upon text information coming in from source feed.
3.  Placeholder to store I, U, D flags in ODS layer to perform respective actions in warehouse for a given entity – just to name a few
4.  Other transformations would be something like these -

Figure 4 – Sample Transformation Rules :

Some of the key points that must be considered while designing a mapping information document:    

1        Design staging area with minimal constraints/indexes and with no or minimum data integrity checks in place. Consideration for Extraction process must be to load data from Source System as quickly as possible with minimal data leakage. Ideally, data type in this layer should be minimally restrictive to allow full data to pass through to staging area without any data loss. Like allowing NULLs, data size sufficiently big enough to hold data from feeding source system.

2        Operational Data Store Layer - In this layer most of the business rules are defined and data types are generally tightly coupled with data types in warehouse layer. Most of the data transformations, error handling & data filtering are done in this layer. An ideal ODS layer should be able to maintain audit trail information to keep track of I, U, D operations to Data Warehouse. In this example, “Action" field in ods.Employee table can be used to maintain current state of a record throughout the life cycle of ETL process.                         

3        Data warehouse Layer - This layer contains only current version of data. The records normally gets Inserted, Updated & Deleted in Data Warehouse depending upon incoming “Action” field from ODS source. Normally, only selective few transformation are performed in this layer, the ones which are specific to data available in data warehouse                      

With this, I will now sign off on this topic. I have tried keeping this information in its basic & simplest form so I hope this information will come handy in your respective projects. Please do share your inputs, feedback & comments & I will be more than happy to amend/improve this posting further.

Decoding Part Copied and Reference: - www.msbigeek.com

Thanks,

Mohit Bhatia