ETL tools extract the data from all the different data sources, transforms the data and (after applying joining fields, calculations, removing incorrect data fields etc.) and loads it into a data warehouse.

ETL testing is done to ensure that the data has been loaded from a source to destination after business transformation is accurate. It also involves the verification of data at various stages that used between source and destination

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Prerequisite

Before learning the ELT testing, we should have basic knowledge of computer functionality, basics of mathematics, logical operators and computer language.

What is ETL?

ETL stands for Extract Transform and Load. ETL combines all the three database function into one tool to fetch data from one database and place it into another database.

Extract: Extract is the process of fetching (reading) the information from the database. At this stage, data is collected from multiple or different types of sources.

Transform: Transform is the process of converting the extracted data from its previous form into the required form. Data can be placed into another database. Transformation can occur by using rules or lookup tables or by combining the data with other data.

Load: Load is the process of writing the data into the target database.

ETL is used to integrate the data with the help of three steps Extract, Transform, and Load, and it is used to blend the data from multiple sources. It is often used to build a data warehouse.

In the ETL process, data is extracted from the source system and convert into a format that can be examined and stored into a data warehouse or any other system. ETL is an alternate but a related approach which is designed to push processing down to database to improve the performance.

Example:

We are taking an example of a retail store which has different departments like sales, marketing, logistics, etc. Each of them is handling the customer’s information independently, and the way each department store the data is quite different. Sales department stored it by the customer’s name and marketing department store it by customer id. Now, if we want to check the history of the customer and want to know what the different products, he/she bought owing to various campaigns; it would be very tedious.

The solution for this is to use a data warehouse to store information from different sources in a uniform structure using ETL. ETL tools extract the data from all these data sources and transform the data (like applying calculations, joining fields, removing incorrect data fields, etc.) and loads into a data warehouse. ETL can transform unique data sets into a unified structure. After that, we will use BI tools to find out the meaningful reports, dashboards, visualization from this data.

Need of ETL

There are many reasons the need for ETL is arising:

ETL helps the companies to analyze their business data for making critical business decisions.
Data warehouse provides a shared data repository.
ETL provides a method of moving data from various sources into a data warehouse.
As the data sources change, the data warehouse will automatically update.
Well-designed and documented ETL system is essential for the success of the data warehouse project.
Transactional databases cannot answer the complex business questions that can be solved by ETL.
Well designed and documented ETL system is essential to the success of a data warehouse project.
ETL process allows the sample data comparison between the source and target systems.
ETL process can perform complex transformation and requires extra area to store the data.
ETL helps to migrate the data into a data warehouse.
ETL is a process which is defined earlier for accessing and manipulating source data into a target database.
For business purpose, ETL offers deep historical context.
It helps to improve productivity because it is codified and can be reused without a need for technical skills.

ETL working

Data is extracted from one or more sources and then copied to the data warehouse. When we are dealing with a large volume of data and multiple sources systems, data is consolidated. ETL is used to migrate data from one database to another database. ETL is the process which requires loading data to and from data marts and data warehouse. ETL is a process which is also used to transform data from one format to another type.

ETL Process in the data warehouse

We need to load our data warehouse regularly so that it can serve its purpose of facilitating business analysis. The data from one or more operational systems needs to be expected and copied into the data warehouse. The challenge in the data warehouse is to integrate and rearrange the large volume of data over many years. The process of extracting the data from source systems and bringing it into the data warehouse is commonly called ETL. The methodology and tasks of ETL are known for many years. Data has to share between applications or systems trying to integrate them.

ETL is a three steps process:

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Extraction

In this step, data is extracted from the source system to the ETL server or staging area. Transformation is done in this area so that the performance of the source system is not degraded. If corrupted data is copied directly into the data warehouse from the source system, rollback will be a challenge over there. Staging area allows validation of the extracted data before it moves in the data warehouse.

There is a need to integrate the system in the data warehouse which has different DBMS, hardware, operating systems, and communication protocols. Here is a need for a logical data map before data is extracted and loaded physically. This data map describes all the relationship between the sources and the target data.

There are three methods to extract the data.

FULL Extraction
Partial extraction- without update notification
Partial Extraction-With update notification

Whether we are using any extraction method, this should not affect the performance and response time of the source system. These source systems are live production system.

Validations during the extraction:

Confirm record with the source data
The data type should be checked
It will check whether all the keys are in place or not
We have to be sure that no spam/unwanted data is loaded
Remove all kind of fragment and duplicate data.

Transformation

Extracted data from source server is raw and not usable in its original form. Therefore the data should be mapped, cleansed, and transformed. Transformation is an important step where the ETL process adds values and change the data, such as the BI reports, can be generated.

In this step, we apply a set of functions on extracted data. Data that does not require any transformation is called direct move or pass-through data.

In this step, we can apply customized operations on data. For example, the first name and the last name in a table are in a different column, it is possible to concatenate them before loading.

Validation during the Transformation:

Filtering: For loading select only specific columns
Character set conversion and encoding handling
Data threshold and validation check
For example, Age cannot be more than two digits
The required field should not be left blank.
Transpose the rows and columns.
To merge the data use lookup

Loading

Loading the data into the data warehouse is the last step of the ETL process. The vast volume of data needs to load into the data warehouse for a concise time. For increasing the performance, loading should be optimized.

If the loading fails, the recover mechanism should be there to restart from the point of the failure without data integrity loss. Admin of data warehouse needs to monitor, resume, and cancel loads as per server performance.

Types of Loading

Initial Load – Full the entire data warehouse table
Incremental Load- Apply changes when needed.
Full Refresh- Erase the content of one or more tables and reloading with new data.

Wrap UP

ETL is known as Extraction, Load, and Transform.
ETL provides the method of moving the data from various sources into a data warehouse.
The first step includes the extraction of data from the source system into the staging area.
Transformation step includes the extracted data from the source is cleansed and transformed.
Loading the data into the data warehouse is the last step of the ETL process.

ETL ARCHITECTURE

ETL stands for Extract, Transform, and Load. In today’s data warehousing world, this term is extended to E-MPAC-TL or Extract, Monitor, Profile, Analyze, Cleanse, Transform, and Load. In other words, ETL focus on Data Quality and Metadata.

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Extraction

The main goal of extraction is to collect the data from the source system as fast as possible and less convenient for these source systems. It also states that the most applicable extraction method should be chosen for source date/time stamps, database log tables, hybrid depending on the situation.

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Transform and Loading

Transform and loading the data is all about to integrate the data and finally moving the combined data to the presentation area, which can be accessed by the front-end tools by the end-user community. Here, the emphasis should be on the functionality offered by the ETL-tool and using it most effectively. It is not enough to use an ETL tool. In a medium to large scale data warehouse environment, it is important to standardize the data as much as possible instead of going for customization. ETL will reduce the throughput time of the different source to target development activities which form the bulk of the traditional ETL effort.

Monitoring

Monitoring of the data enables the verification of the data, which is moved throughout the entire ETL process and has two main objectives. Firstly, the data should be screened. A proper balance should be there between screening the incoming data as much as possible and not slowing down the entire ETL process when too much checking is done. Here an inside-out approach which is used in Ralph Kimbal screening technique could be used. This technique can capture all errors consistently which is based on a pre-defined set of metadata business rules and enables the reporting on them through a simple star schema, which enables a view on the data quality evolution over the time. Secondly, we should have to be focused on ETL performance. This metadata information can be plugged into all dimension and fact tables and can be called an audit dimension.

Quality Assurance

Quality Assurance is a process between the different stages that could be defined depending on the need, and these processes can check the completeness of the value; do we still have the same number of records or total of specific measures between different ETL stages? This information should be captured as metadata. Finally, the data lineage should be foreseen throughout the entire ETL process, included the error records produced.

Data Profiling

It is used to generate statistics about the sources. The objective of data profiling is to know about the sources. Data profiling will use analytical techniques to discover the actual content, structure, and quality of the data by analyzing and validating the data pattern and formats and by identifying and validating redundant data across the data source. It is essential to use the correct tool, which is used to automate this process. It gives a huge amount and variety of data.

Data Analysis

To analyze the results of the profiled data, Data Analysis is used. For analyzing the data, it is easier to identify data quality problems such as missing data, inconsistent data, invalid data, constraint problems, parent-child issues such as orphans, duplicated. It is essential to capture the results of this assessment correctly. Data analysis will become the communication medium between the source and the data warehouse team for tackling the outstanding issues. The source to target mapping highly depends on the quality of the source analysis.

Source Analysis

In the source Analysis, the focus should not only on the sources but also on the surroundings, to obtain the source documentation. The future of the source applications depends upon the current data issues of origin, the corresponding data models/ metadata repositories, and receiving a walkthrough of source model and business rules by source owners. It is crucial to set up the frequent meetings with owners of the source to detect the changes which might impact the data warehouse and the associated ETL process.

Cleansing

In this section, the errors found can be fixed, which is based on the Metadata of a pre-defined set of rules. Here, a distinction needs to be made between completely or partly rejected records and enable the manual correction of the issues or by fixing the data by correcting the inaccurate data fields, adjusting the data format, etc.

E-MPAC-TL is an extended ETL concept which tries to balance the requirements with the realities of the systems, tools, metadata, technical issues, and constraint and above all the data itself.

So, this brings us to the end of blog. This Tecklearn ‘Overview of ETL Testing and its Architecture’ blog helps you with commonly asked questions if you are looking out for a job in ETL. If you wish to learn Data Warehousing and build a career in ETL domain, then check out our interactive, ETL Testing Training, that comes with 24*7 support to guide you throughout your learning period. Please find the link for course details:

https://www.tecklearn.com/course/etl-testing-training/

ETL Testing Training

About the Course

Today’s businesses have to work with data in multiple formats extracted from multiple sources. All this makes ETL Testing all the more important. Tecklearn’s ETL Testing training offers an in-depth understanding of Data warehousing and business intelligence concepts through real-world examples. You will also gain the essential knowledge of ETL testing, Performance Tuning, cubes, etc., through hands-on projects, and this will help you to become a successful ETL Testing expert.

Why Should you take ETL Testing Training?

An ETL Developer can earn $100,000 per year – indeed.com
Global Big Data Analytics Market to reach $40.6 Billion in 4 years.
Most companies estimate that they’re analysing a mere 12% of the available data. – Forrester Research

What you will Learn in this Course?

Introduction to ETL testing

Introduction to ETL testing
Life cycle of ETL Testing
Database concepts and ETL in Business Intelligence
Understanding the difference between OLTP and OLAP and data warehousing

Database Testing and Data Warehousing Testing

Introduction to Relational Database Management Systems (RDBMS)
Concepts of Relational database
Data warehousing testing Versus database
Integrity constraints
Test data warehousing testing
Hands On

ETL Testing Scenarios

Data warehouse workflow
ETL Testing scenarios and ETL Mapping
Data Warehouse Testing
Data Mismatch and Data Loss Testing
Creation of Data warehouse workflow
Create ETL Mapping
Hands On

Various Testing Scenarios

Introduction to various testing scenarios
Structure validation and constraint validation
Data correctness, completeness, quality and Data validation
Negative testing
Hands On

Data Checks using SQL

Using SQL for checking data
Understanding database structure
Working with SQL Scripts
Hands On

Reports & Cube testing

Reports and Cube Testing
Scope of Business Intelligence Testing
Hands On

Got a question for us? Please mention it in the comments section and we will get back to you.

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