In the modern world today, companies gather data from multiple sources for analysis. This data can be further processed by various BI Tools to gather valuable business insights or stored in a Data Warehouse for later use. In order to do any of this, the process of ETL Testing is required. ETL Testing is derived from the original ETL process.
ETL stands for Extract, Transform and Load and is the primary approach Data Extraction Tools and BI Tools use to extract data from a data source, transform that data into a common format that is suited for further analysis and then load that data into a common storage location, normally a Data Warehouse. ETL Testing comes into play when the whole ETL process needs to get validated and verified in order to prevent data loss and data redundancy.
This article focuses on providing a comprehensive guide on ETL Testing. It will talk about the process of ETL Testing, its types and also some challenges. It also explains the potential of Testing Tools. Read along to find out about this interesting process.
Table of Contents
- What is ETL?
- What is ETL Testing?
- ETL Test Scenarios and Test Cases
- Types of ETL Bugs
- Difference Between ETL Testing and Database Testing
- 8 Key Steps to Perform ETL Testing
- 8 Types of ETL Testing
- Challenges Faced in the ETL Testing Process
- Capabilities of an ETL Testing Tool
What is ETL?
ETL stands for Extract, Transform and Load and is the process of integrating data from multiple sources, transforming it into a common format, and delivering the data into a destination usually a Data Warehouse for gathering valuable business insights.
With the introduction of Cloud technologies, many organisations are trying to migrate their data from Legacy source systems to Cloud environments by using ETL Tools. Organisations may have Legacy data sources like RDBMS, DW (Data Warehouse), etc. that lack performance, and scalability. Hence, to get better performance, scalability, fault-tolerant, and recovery systems, organizations migrate to Cloud technologies like Amazon Web Services, Google Cloud Platform, Microsoft Azure, Private Clouds, and many more.
An ETL process automatically extracts the data from sources by using configurations and connectors and then transforms the data by applying calculations like filter, aggregation, ranking, business transformation, etc. based on business requirements. The ETL process consists of 3 main steps:
- Extract: Extraction is an important process because it gathers valuable information from structured and unstructured data that is present in multiple sources like Databases, other Data Warehouses, files, marketing tools, CRM (Customer Relationship Management) information (etc). ETL Tools make the process easy by not writing any complex code and helps the user extract data with a few clicks.
- Transform: Transformation is the process of converting the data extracted to a common format that can be understood by the Data Warehouse or any BI tool. It “cleans” the data to make it in a more readable format for its users. Some transformation techniques include sorting, cleaning, removing redundant information, and verifying the data from these data sources.
- Load: Loading is the process of storing the transformed data onto a destination, normally a Data Warehouse, and also supports loading any unstructured data into data lakes that various BI (Business Intelligence) tools can use to gain valuable insights.
The ETL process is shown below:
In order to understand the ETL process in a more detailed fashion, click here.
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What is ETL Testing?
ETL Testing is a process of verifying the accuracy of data that has been loaded from source to destination after business transformation. The verification of data takes place at multiple stages during the ETL process.
ETL Test Scenarios and Test Cases
|Test Scenario||Test Cases|
|Mapping doc validation||It Verifies for mapping doc whether the corresponding ETL data is provided or not. Change log should maintain in every mapping doc.|
|Validation||Validates the source and target table structure with the mapping doc. |
The data type for source and target should be the same. Source and target should have equal lengths of data type.
Verify that data field types and formats are specified.
The data type length of the source should not be less than the data type length of the target.
Validate the name of columns in the table against mapping doc.
|Constraint Validation||It ensures that the constraints defined for each table are correct.|
|Data consistency issues||The data type and length for a particular attribute may vary in files or tables through the semantic definition is the same.|
|Completeness Issues||It ensures that all data is loaded into the target table.|
Compare record counts between source and target.
Check for any rejected records.
Check data should not be truncated in the column of target tables.
Compares unique values of key fields between data loaded to WH and source data.
|Correctness Issues||Data that is misspelled or inaccurately recorded.Null, non-unique, or out-of-range data.|
|Data Quality||Number check: Need to number check and validate it|
Date Check: They have to follow date format and it should be the same across all records
|Null Validate||Verify the null values, where “Not Null” is specified for a specific column.|
|Duplicate Check||Needs to validate the unique key, primary key and any other column should be unique as per the business requirements are having any duplicate rows.|
Check if any duplicate values exist in any column which is extracted from multiple columns in the source and combined into one column.
As per the client requirements, needs to be ensured that no duplicates in a combination of multiple columns within target only.
|Date Validation||To know the row creation dateIdentify active records as per the ETL development perspective|
Identify active records as per the business requirements perspective
Sometimes based on the date values the updates and inserts are generated.
|Complete Data Validation||To validate the complete data set in source and target table minus a query in the best solution|
We need to source minus target and target minus source
If the minus query returns any value those should be considered as mismatching rows
Needs to match rows among source and target using intersect statementThe count returned by intersecting should match with individual counts of source and target tables
If minus query returns of rows and count intersect is less than source count or target table then we can consider as duplicate rows exist.
|Data Cleanness||Unnecessary columns should be deleted before loading into the staging area.|
Types of ETL Bugs
|Type of Bugs||Description|
|User interface bugs||The bugs that are related to the GUI of the application such as colors, alignment, spelling mistakes, navigation, etc.|
|Equivalence Class Partitioning (ECP) bugs||It involves valid and invalid types.|
|Input and Output bugs||The correct values are accepted the rest are rejected.|
|Calculation bugs||Mathematical calculation bugs or any wrong output.|
|Load Condition bugs||It does not allow multiple users and expected load.|
|Race Condition bugs||System interrupts or crashes.|
|Version control bugs||Version information is not available.|
|H/W bugs||The device is not responding to the application.|
|Help Source bugs||Mistakes in reference documents.|
Difference Between ETL Testing and Database Testing
|ETL Testing||Database Testing|
|It Verifies whether data is moved as expected.||It checks if the data is following the rules/ standards defined in the Data Model.|
|It Verifies for the counts in the source and target are matching.||It Verifies that there are no orphan records and foreign-primary key relations are maintained.|
|Verifies that the foreign primary key relations are preserved during the ETL.||Verifies that there are no redundant tables and database is optimally normalized.|
|Verifies for duplication in loaded data.||Verify if data is missing in columns where required.|
8 Key Steps to Perform ETL Testing
ETL Testing is the process that is designed to verify and validate the ETL process in order to reduce data redundancy and information loss. As Testing is a vague concept, there are no predefined rules to perform Testing.
However, the ETL Testing process can be broken down into 8 broad steps that you can refer to while performing Testing:
- Gathering Business Requirements
- Identifying and Validating Data Sources
- Creating Test Cases
- Executing Test Cases
- Creating Reports
- Re-testing Bugs
- Preparing Reports
- Closing the Reports
1) Gathering Business Requirements
The first and foremost step in ETL Testing is to know and capture the business requirement by designing the data models, business flows, schematic lane diagrams, and reports. It’s important to understand business requirements so that the tester can be aware of what is being tested. The team should thoroughly document the scope of the project so that the tester can fully understand it.
2) Identifying and Validating Data Sources
The next step is to identify the source data and perform preliminary checks like schema check, counts, validation of tables, etc. to check whether the ETL process aligns with the business model specification. In this stage, the primary keys are checked as per the model and care is taken to prevent any duplicate data otherwise it will lead to inaccurate aggregation.
3) Creating Test Cases
The next step is to create a source to target mapping and to design the test cases, including transformation according to business test cases, SQL scripts to perform source to target comparison, execution flows, etc. Then, validating the documents against the business requirements to ensure it aligns to business needs.
4) Executing Test Cases
The next step involves executing the created test cases on the QA (Question-Answer) environment to identify the types of bugs or defects encountered during Testing. Analysts must try to reproduce the defect and log them with proper comments and screenshots. The ETL tests must be executed as per business requirements.
5) Creating Reports
After logging all the defects onto Defect Management Systems (usually JIRA), they are assigned to particular stakeholders for defect fixing. Analysts must ensure that they have captured all the relevant screenshots, mentioned steps to reproduce the test cases and the actual vs expected results for each test case.
6) Re-testing Bugs
Once the developer fixes the bug, the bug is tested in the same environment again to ensure there are no traces of the bug is left. Also, Regression Testing is performed to ensure there are no new bugs introduced while fixing the earlier one.
7) Preparing Reports
Reports are prepared based on the bugs and test cases and are uploaded into the Defect Management Systems. The report will help the stakeholders to understand the bug and the result of the Testing process in order to maintain the proper delivery threshold.
8) Closing the Reports
The last step involves, closing the reports once everything is completed by mentioning proper comments and attaching relevant files related to the test cases and the business requirements.
The whole Testing process is depicted in the figure below:
8 Types of ETL Testing
ETL Testing is designed to ensure that the data which has been loaded from a data source to a destination after transformation is accurate. It involves the verification of data at various stages, which is used between source and destination.
As you saw the general process of Testing, there are mainly 8 Types of ETL Testing types:
- Production Validation
- Source to Target Testing
- Metadata Testing
- Performance Testing
- Data Transformation Testing
- Data Quality Testing
- Data Integration Testing
- Report Testing
1) Production Validation
It is a table reconciliation or product balancing technique, which usually validates the data in the target systems, i.e. production system data and compares it against source data.
2) Source to Target Testing
In this type of ETL Testing, the count from source systems is tallied to check if it matches the target system’s expected records. It also validates the data’s completeness, i.e. it checks the loss/truncation of the data in the target systems.
3) Metadata Testing
Metadata Testing involves matching schema, data types, length, indexes, constraints, etc. between source and target systems.
4) Performance Testing
Performance Testing tests the systems’ performance which determines whether data is loaded within expected time frames to the systems and how it behaves when multiple users logs onto the same system.
5) Data Transformation Testing
In this type of Testing, SQL queries are run to validate business transformations and it also checks whether data is loaded into the target destination with the correct transformations.
6) Data Quality Testing
In this type of ETL Testing, checks are performed on the Data Quality. This includes invalid characters, patterns, precisions, nulls, numbers from the source, and the invalid data is reported.
7) Data Integration Testing
This type of Testing confirms that all the sources’ data has been loaded to the target Data Warehouse correctly and it also checks threshold values.
8) Report Testing
This type of ETL Testing, reviews data in the summary report, verifying whether the layout and functionality are as expected, and makes calculations for further analysis.
Challenges Faced in the ETL Testing Process
Although ETL Testing is a very important process, there can be some challenges that companies can face when trying to deploy it in their applications. Some of those challenges are given below:
- Frequent changes in the requirement of the customers which causes re-iteration of test cases and execution.
- Failure to understand business requirements or employees are unclear of the business needs.
- Due to changes in requirements by the customer, a tester might need to re-create/modify mapping documents and SQL scripts, that leads to a slow process.
- Data loss can occur during migration because of which it is hard to perform source to target reconciliation.
- Change in the data source or incomplete/corrupt source data.
- Realtime data may impact the reconciliation process between data sources and target destinations.
- The huge volume of historical data may cause memory issues in the system.
- Using inappropriate Testing tools.
- Having an unstable Testing environment.
Capabilities of an ETL Testing Tool
Not all the tools can be applied to every user’s needs. Every Testing team has different requirements, and thus it is important to choose the ETL Testing tool to avoid future bottlenecks carefully. Some of the parameters to consider when choosing an ETL Testing Tool are given below. Find out the difference between ETL vs ELT here.
- Cloud Compatibility: The ETL Tool should have built-in compatibility with Cloud systems such as Amazon Web Services, Google Cloud Platform, Salesforce, Snowflake and more.
- User Interface: A good UI solves many problems. The Testing Tool should have a nice UI to display the results and reports. It also helps users to view the data and execute SQL queries against the data.
- Built-in Connectors: An ETL Testing Tool should have in-built connectors to connect source and target datasets to carry out Testing. It should also have the capability to read different file formats like CSV, XML, JSON, etc.
- Automatic Code Generation: A good Testing Tool must have the Automatic Code Generation ability. This is because this can speed up the development process and reduces the chances of human error.
This article gave a comprehensive overview of ETL Testing. It explained the process of Testing, its types and some of its challenges. It also gave some parameters that companies can consider when opting for a good ETL Testing Tool. Overall, Testing plays an important part in governing the ETL process and every type of company must incorporate it in their business.
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