ETL Data Warehouse Testing
Functional Testing Types of Software Testing

Understanding ETL Data Warehouse Testing | After all, Data is The King

As we know, a company is a collaboration of multiple departments handling different business and technical aspects – e.g. resourcing, sales, finance, HR, admin, etc. Each department has its own set of applications, tools and DATA, i.e. separate database with department-specific technology & schema. Now say the business team wants to analyze data from different departments for decision-making. What do you think will be the best approach? Asking for data from each department and then analyzing it individually OR to consolidate all the relevant data from each department and then analyze? Hope you know the answer 🙂 that’s where ETL tool and ETL Data Warehouse Testing comes into picture. Let’s see how…

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Data Warehouse

In laymen English – A warehouse is a store room for storage of goods. How does a retail chain works? All goods are stored in a warehouse and sold via different retail outlets, right? Now what do you think will be a Data warehouse? In terms of IT, just reverse the flow – each department gathers its own set of data individually (database) and then the data from these multiple heterogeneous sources is integrated to form a Data Warehouse!

Mostly, Data warehouse is used for analysis, reports and decision-making rather than for transaction processing (store & retrieve). It enables the company or organization to consolidate data from several sources and separates analysis workload from transaction workload. Why do you think it’s important? After all DATA (including historical data) is the most important part of any organization – it’s the backbone for any report and reports are the baseline on which all the vital management decisions are taken.

ETL

ETL stands for Extract, Transform, and Load – a process of how data is migrated from the source system(s) to a Data warehouse. The process of ETL allows a business to collect important data, change it to fit their goals and models, and then store it for use. In a world of digital enterprise, it is a critical part of running an effective and efficient business.

ETL Extract, Transform, Load

Extract

Extracting the data from different heterogeneous data sources such as transactional systems (Oracle, Microsoft, or any other relational database) or applications (Flat file, Excel, XML, etc.). It is generally done by running scheduled jobs in off-business hours (at night or over the weekend). Importance: Right decisions require right data to be fetched from the right place.

Transformation

Transforming the data into a suitable format that can be easily loaded into a DW system. E.g. Calculations, Concatenation, Joins, Data correction & cleansing, Removing incorrect data, fixing data errors, integrity checks, formatting, etc. This step is governed by ‘Transformation rules’ which in turn are based on the business requirement.

Loading

Loading the transformed data into a Data Warehouse system for analytical reporting and information. This would be the final step in ETL process.

ETL Tool

As stated earlier, 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. Later, you can use various Business Intelligence (BI) tools to generate meaningful reports, dashboards, and visualizations using this data.

The popular ETL tools available in the market are Informatica Power Center, IBM Websphere DataStage, SAP Business Objects Data Services BODS, IBM Cognos Data Manager, Microsoft SQL Server Integration Services (SSIS), Oracle Data Integrator ODI, SAS Data Integration Studio, Oracle Warehouse Builder and ABInitio.

Difference between ETL and BI Tools

Business Intelligence is the process of collecting raw data or business data and turning it into information that is useful and more meaningful, generally used for Reporting, Analysis, Data mining, Data quality and Interpretation, Predictive Analysis, etc.

An ETL tool is used to extract data from different data sources, transform the data, and load it into a Data Warehouse system; however a BI tool is used to generate interactive and ad-hoc reports, dashboard and Data visualizations for further decision making.

ETL Data Warehouse Testing

QuerySurge ETL Testing ToolNow that you know what ETL is, what do you think ETL Data Warehouse Testing would be? Yeah! In simple terms – Testing of the Extract, Transform and Load functions before data is actually moved into a production Data Warehouse system. It is sometimes also known as Table Balancing or production reconciliation.

Data is important for businesses to make the critical business decisions. A data-centric testing process, the main objective of ETL Data Warehouse testing is to identify and mitigate Data defects and general errors that occur prior to processing of data for analytical reporting. ETL Data Warehouse testing plays a significant role validating and ensuring that the business information is exact, consistent and reliable.

Note: Common ETL Data Warehouse testing tools include QuerySurge, Informatica, etc.

ETL Data Warehouse Testing vs. Database Testing

Database vs. Data Warehouse TestingThough both are related to data, but ETL and Database testing are two different concepts focused in two different directions. ETL Data Warehouse testing is not about database interactions – store, modify and retrieve. The main difference: ETL Data Warehouse testing is normally performed on high-volume data involving heterogeneous systems and a Data warehouse (extract-transform-load), whereas database testing is commonly performed on small-scale data involving homogeneous transactional system (CRUD create-read-update-delete operations to/from a single database).

Differences between an OLTP and OLAP system

OLTP stands for Online Transactional Processing system which is commonly a relational database and is used to manage day-to-day transactions. OLAP stands for Online Analytical Processing system which is commonly a multidimensional system and is also called a Data warehouse.

Key checklist for ETL Data Warehouse Testing scenarios

  • Structure validation
  • Validating Mapping document
  • Validate Constraints
  • Data Consistency check
  • Data Completeness Validation (count match, no loss of data)
  • Data Correctness Validation (erroneous, duplicate, etc.)
  • Data Transform validation (based on business transformation rules)
  • Metadata validation (data type, categories, length, constraints, etc.)
  • Data Quality Validation (invalid chars, date check, etc.)
  • Null Validation
  • Data operations (insert new data, update data, etc.)
  • Functional testing (GUI, screen rendering, reports, etc.)

ETL Data Warehouse Testing Challenges

As you might have understood by now – ETL Data Warehouse testing is quite different from conventional testing, there are many challenges. Here is the list of few frequently encountered ETL Data Warehouse testing challenges:

  • Huge volume & complexity of data including heterogeneous data sources
  • Unavailability of inclusive test environment
  • No permission & privileges to run jobs
  • Frequent Business requirement changes
  • Identification of Test data to verify all business rules
  • Data-centric testing
  • Minimal idea of the reports requirement (UI)
  • Involves complex SQL concepts for data validation
  • Non-availability of complete source-to-target mapping information

Because data is so important to a successful business, poor performance or inaccurate procedure can cost time and money. Well planned, well defined and effective ETL Data Warehouse testing scope guarantees smooth conversion of the project to the production. A business gains the real buoyancy once the ETL processes are verified and validated by independent group of experts to make sure that data warehouse is concrete and robust.

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