Data Warehouse ETL Testing Concepts and Benefits - 【2020】
data warehouse etl testing

Data Warehouse ETL Testing Concepts and Benefits

90% of the data available today was only acquired in the last two years. According to statistical studies, our current data production is about 2.5 billion bytes per day. Over time and thanks to technological development, data has become an essential part and a key factor for the success of economic businesses. Above all, processing has become a crucial component for many companies. But before we delve into this subject, we should start with the basics: what is data really?


The definitions of Big Data on the Internet are now a dime a dozen. For our purposes we define data as raw data or unprocessed information. In the last few decades, this raw data has become increasingly important because companies have realized that this data can change the way we live, work and think. And so began the Big Data rampage.

But big data is only of great value as raw data if it can be well structured, analyzed and interpreted. Only well analyzed and interpreted data can provide meaningful business and market information. But this was precisely the problem: with conventional data integration solutions like the classic ETL, it was not possible to organize and structure the large data in such a way that it could be quickly and easily made available for analysis.

Data integration solutions are designed to meet most enterprise BI requirements. To achieve this, some ETL providers are expanding their product lines horizontally, offering data tools and capabilities for real-time data capture, and even complete data management solutions. Then there are others that are expanding vertically by adding more functionality to provide a complete business intelligence solution.


Data warehouses and ETL tools were invented so that all data could be used. Many ETL tools were originally developed to facilitate the development of data storage. Today, the major ETL tools on the market have significantly improved their functionality with data profiling, data cleansing, enterprise application integration (EAI), large data processing, data governance, and master data management. Once data is available in a data warehouse, it is typically provided with analyzed and visualized BI software. BI software supports you in reporting, data determination, data mining, and dashboarding.

But what exactly are the data warehouse and ETL? Over the years, many different notions of these two terms have been developed. So let’s first establish a common basis for this article. The most common definition of a data warehouse in the marketplace is: a system that extracts the source data, cleans it, fits it, and delivers it to a target memory so that it can be used for querying and analysis. The main task is to provide reliable and accurate data that can be used to make important business decisions. To achieve this, data must be extracted from one or more operating systems and copied into the data repository, which is done using ETL tools.

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ETL is short for Extraction, Transformation and Loading. And what these tools basically do is extract data from one or even several databases and load it into a database, the so-called target memory.



At the extraction stage, data is extracted from one or more source systems and made available for further processing. The aim is to make the data available in such a way that it can be recovered with as few resources as possible. It is also important that this step is designed in such a way that the source system is not affected in terms of performance and response time.


In this step, the data is transformed from the source database to the target database. It is crucial that all extracted data is recovered from the source systems with as few resources as possible. This includes the conversion of all extracted data in the same dimension with the same units so that they can be merged later. The transformation step also collects data from different sources, generates recalculated values and applies advanced validation rules.


During the loading stage, care must be taken to ensure that loading is carried out correctly and with the minimum possible resources. During the loading process, the data is written to the target database.


The main advantage of ETL tools is that they are much easier and faster to use than traditional methods that move data by hand writing and code ETL. ETL tools include graphical interfaces that speed up the mapping of tables and columns between source and target memory.


Easy operation through automated processes

As mentioned at the beginning, the greatest advantage of ETL tools is their ease of use. Once the data sources are selected, the tool automatically identifies the data types and formats, defines the rules of how to extract and process the data, and finally loads the data into the target memory. This eliminates the need for coding in the traditional sense, where every procedure and code has to be written.

Visual flow

ETL tools are based on a graphical user interface (GUI) and provide a visual flow of system logic. The GUI allows you to visualize data processing via drag and drop.

Operational Resistance

Many data stores are vulnerable during operation. ETL tools have built-in error handling functionality that helps data engineers develop a robust and well instrumented ETL process.

Suitable for complex data management situations

ETL tools are ideal for moving large amounts of data and transferring it in batches. ETL tools simplify complicated rules and transformations and support you in data analysis and chain manipulation, data changes, and integration of multiple data sets.

Advanced data profiling and cleansing

The extended functions address the processing requirements that often occur in a structurally complex data store.

Improved Business Intelligence

Data access is easier and better with ETL tools because they simplify the process of extracting, transforming and loading. Improved information access directly impacts data-based strategic and operational decisions. ETL tools also enable executives to access information based on their specific needs and make appropriate decisions.

High Return on Investment (ROI)

ETL tools help companies save costs and achieve higher revenues. A study by the International Data Corporation found that the application of ETL resulted in an average 5-year return on investment of 112% with an average payback of 1.6 years.


ETL tools simplify the construction of a high quality data warehouse. In addition, several ETL tools are equipped with performance-enhancing technologies. For example, cluster awareness applications, which are actually software applications designed to call cluster APIs to determine operational status. This happens if manual failover between cluster nodes is activated for planned technical maintenance or if automatic failover is required when a computer cluster node meets the hardware.


The advantages described above are related to traditional ETL. However, traditional ETL tools cannot keep up with the rapid pace of change that dominates the large data industry. Let’s take a look at the shortcomings of these traditional ETL tools.

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Traditional ETL tools are very time consuming. Data processing with ETL means developing a multi-step process when data needs to be moved and transformed. In addition, traditional ETL tools are inflexible to change and cannot load readable live data into the BI front end. It should also be mentioned that not only is this a costly process, but it is also very time consuming. And we all know that time is money.

There are a number of factors that affect the way ETL tools and processes work. These factors fall into the following categories:


Similarity of source and target data structures

The more the structure of the source data differs from that of the target data, the more complex the traditional ETL processing and maintenance effort becomes. Because of the different structures, the loading process usually has to analyze the data sets, transform the values, validate the values, replace the code values, and so on.

Data Quality

Common data quality problems are missing values, incorrect code values, data and reference integrity problems. There is no point in loading the data warehouse with poor quality data. If the data store is used for database marketing, for example, the addresses must be validated to avoid a skipping / bouncing of emails.

Complexity of source data

Depending on the background of the procurement team, some data sources are more complex than others. Examples of complex sources may include multiple record types, bit fields, and packed decimal fields. This type of data translates into requirements for the ETL tool or a custom solution, as it is unlikely to be present in the target data structures. People on the procurement team who are not familiar with these types may need to do some research in these areas.

Data dependencies

The dependencies in the data determine the order in which the tables are loaded. Dependencies also tend to reduce parallel loading processes, especially when data from different systems that are in a different business cycle is merged. Complex dependencies will also make processes more complex, create bottlenecks, and make support more difficult.


Technical metadata describes not only the structure and format of the source and target data sources, but also the mapping and transformation rules between them. Metadata must be visible and usable by both programs and people.



ETL processes must record information about the source of the data they read, transform and write. Key information includes the date of processing, the number of lines read and written, the error that occurred, and the rules applied. This information is crucial for quality control and serves as an audit trail. The logging process must be strict enough to allow you to trace the data from the data warehouse back to its source. In addition, this information should be available as the processes run to reduce processing times.


ETL requirements should define what constitutes an acceptable burden. The ETL process must notify the appropriate support personnel if a load fails or is in error. Ideally, the notification process should be integrated into your existing problem tracking system.

Cold Start, Hot Start

Unfortunately, the systems are failing. You must be able to take appropriate action if the system crashes while the ETL process is underway. Partial loads can be painful in the true sense of the word. Depending on the size of your data store and your data volume, you may want to start from scratch, the so-called cold start, or continue from the last successfully loaded data sets, the so-called hot start. The registration process should provide you with information about the status of the ETL process.


The comfort of management with technology

How familiar is your management with the Data Warehouse architecture? Will you have a data warehouse manager? Does your management have development in the background? You may suggest running all ETL processes with Visual Basic. Comfort level is a legitimate concern, and these concerns will limit your options.

In-House Knowledge

What is your company’s tradition? SQL server? ETL solutions are derived from today’s concepts, capabilities, and toolkits. Capturing, transforming, and loading the data repository is a continuous process and must be maintained and expanded as the data storage is expanded to include other topics. With the right tool, you will consume fewer resources in the long run.

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Once ETL processes are in place, they should ideally be integrated into an existing support structure, including people with the right skills, new reporting mechanisms and problem tracking systems. If you are using an ETL tool, support staff may need to be trained. In general, the ETL process should be documented, especially in the area of audit reporting.


Interoperability between platforms

There must be a way for systems on one platform to communicate with systems on another platform. FTP is a common way to transfer data from one system to another. FTP requires a physical network path from one system to another and the Internet Protocol in both systems. External data sources usually come on a diskette tape or an Internet server.

Volume and frequency of uploads

Since the data store is loaded with batch programs, a high volume of data will tend to reduce the size of the batch window. The volume of data also affects the recovery work. Fast-loading programs reduce the time it takes to load data into the data warehouse.

Hard disk space

The data warehouse not only requires a lot of storage space, but also a lot of hidden storage space for the test areas and the intermediate files. For example, data can be extracted from source systems into flat files and then transformed into other flat files for uploading.


The data warehouse load can include hundreds of source files created on different systems with different technology and at different times. A monthly upload may be common for some parts of the repository and a quarterly upload for others. Some uploads may be on demand, such as product lists or external data. Some picking programs may run on a different system than your scheduler.


In the following list you can see the most common traditional ETL tools these days:

  • Oracle Warehouse Builder (OWB)
  • SAP Data Services
  • IBM Infosphere Information Server
  • SAS data management
  • PowerCenter Informatica
  • Elixir Repertoire for Data ETL
  • Data migrator (IBI)
  • SQL Server Integration Services (SSIS)
  • Talend Studio for Data Integration
  • Sagent Data Flow
  • Alteryx
  • Actian DataConnect
  • Open Text Integration Center
  • Oracle Data Integrator (ODI)
  • Cognos Data Manager
  • Microsoft SQL Server Integration Services (MSSIS)
  • CloverETL
  • Centerprise Data Integrator
  • IBM Infosphere Warehouse Edition
  • Pentaho Data Integration
  • Adeptia Integration Server
  • Syncsort DMX
  • QlikView Expressor
  • Realtional Junction ETL Manager (Sesame Software)


Companies with clearly defined IT practices are taking an innovative step to take the next step in technology transformation by building their own data repository to store and monitor data in real time. We need to explain in detail how each step of the ETL process can be performed.

The first part of an ETL process is to extract the data from the source system. In many cases, this is the most important aspect of ETL, since data extraction is the foundation for the success of subsequent processes.

There are several ways to perform the extraction step:

  • Update notification: if the source system can report a change in a data record and describe the change
  • Incremental extraction: Some systems may not be able to report an update, but can identify the changed records and provide an extract of those records. For the next steps of the ETL, the system must identify the changes and propagate them downwards. Note, however, that when using the daily extract, you may not be able to handle deleted data records correctly
  • Full extract: Some systems are not able to detect which data has been changed at all, so a full extract is the only way to get the data out of the system. The full extract requires that a copy of the last extract be kept in the same format in order to detect changes.

In the data transformation phase, a series of rules or functions are applied to the extracted data to prepare it for loading into the target memory.

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Data transformation includes the following tasks:

  • Application of business rules
  • Data Cleansing
  • Filtering the data
  • Dividing a column into several columns
  • Merging data from different sources
  • Transpose rows and columns
  • Application of any type of simple or complex data validation

The loading process loads the data into the target memory, which can be either a simple flat file or a data store. This process varies greatly depending on the needs of the organization. Because the loading phase interacts with a target memory, the constraints defined in the target memory schema, as well as the triggers activated when the data is loaded and its application, also contribute to the overall data quality performance of the ETL process.


ETL is an important part of today’s business intelligence. ETL is any business process in which data from different sources can be brought together in one place to programmatically analyze and uncover business knowledge. Implementing an integrated strategy using ETL tools and processes gives an organization a competitive advantage by enabling it to leverage its data and then make decisions based on it. But why is an ETL process so important? Simply put, it increases the value of your data.

This is achieved through documentation that increases confidence in the data, capturing the flow of transactions, collating the data from different sources, structuring the data into BI tools, and then processing the data analytically. According to the Harvard Business Review, it is not necessary to make a large initial investment in IT to use Big Data with ETL tools. Here is an approach to building a capability:

Choose a business unit to lay the foundation. You should have a quantitatively intelligent leader supported by a team of data scientists.

  • Challenge each key role to identify five business opportunities based on Big Data, each of which could be created in five weeks by a team of no more than five people.
  • Implement an innovation process that includes four steps Experiment, measure, share and replicate.
  • Remember Joy’s law: Most of the smartest people work for someone else. Open some of your records and analysis challenges to people interested in the Internet.


According to Spaceworks, technical projects often go beyond time and budget. More precisely, 45% over budget, 7% over time and achieve 56% less value than expected. Your ETL project will probably not be immune. These are the most common errors that occur in an ETL project:

  • Forget about long-term maintenance
  • Underestimation of the requirements for data processing
  • The abandonment of the client’s development process
  • Narrow connection of different elements of your data pipe
  • Building your ETL process based on your current data volume
  • I don’t recognize the warning signs
  • Concentration on tools and technologies rather than basic practices


ETL tests can be performed manually or with tools such as Informatica, Querysurge, etc. However, most of the ETL testing process is done through SQL scripts or manually in spreadsheets. The use of automated testing tools ensures that only reliable data is transferred to your system.

The types of tests that can be performed with ETL tools include unit, function, regression, continuous integration, operational monitoring, and more. Your organization can reduce testing time by 50% to 90% and decrease resource utilization. ETL test reduces business risk and builds confidence in the data.

ETL examination plays an important role in testing, validating, and ensuring that business information is accurate, consistent, and reliable. Part of ETL testing is data-centric testing, in which large amounts of data are compared between heterogeneous data sources.

This data-centric testing helps achieve high data quality by quickly and efficiently correcting faulty processes. ETL and data warehouse testing should be followed by impact analysis and focus on strong alignment between development, operations, and business teams.


The types of ETL tests are as follows:

  • Centered data test: here the quality of the data must be checked. The objective of data-centric testing is to ensure that there is valid and correct data in the system. It ensures that ETL processes are correctly applied to the source database by transforming and loading the data into the target database. It also ensures that the system is properly migrated and updated
  • Data accuracy check: this ensures that data is transformed and loaded exactly as expected This test identifies errors caused by character separation, incorrect column mapping and implementation errors in the logic
  • Check data integrity: check whether all expected data from all data sources have been loaded into the target memory. Helps to check if the number of rows in the driver table matches the number of rows in the target table
  • Data integrity test: helps to check the number of unspecified or unadjusted rows
  • Business test: ensures data meets critical business requirements
  • Data transformation test: is more or less the same as the business test. But this test also checks whether the data has been moved, copied and loaded completely and accurately
  • Production validation tests: are performed in many cases, as they cannot be achieved by writing source SQL queries with the target.
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Companies must recognize the need to test data to ensure its integrity and completeness. They must also recognize that extensive testing of data at any point in the ETL process is important and unavoidable as more data is collected and used to make strategic decisions that affect their business forecasts. However, different strategies are time and resource consuming and inefficient.

Therefore, a well thought-out, clearly defined and effective ETL test environment is needed to ensure a smooth transition from project to final production. Now is the time to look at some of the issues involved in ETL and data warehouse testing.

Some of the major challenges in testing ETL and data warehouse are

  • Temporary disability of the inclusive test bed
  • Missing commercial information flow
  • Possible loss of data during ETL process
  • There are many unclear software requirements
  • Presence of apparent problems in the collection and creation of test data
  • The data in the production sample does not represent all possible business processes
  • Certain testing strategies are very time consuming
  • Checking the integrity of the data in the transformed columns is a complicated process.


In the digital age, new requirements are emerging more quickly than ever before, and previous requirements are changing so quickly that agility and responsiveness have become two key success factors. Due to issues such as those mentioned above, traditional data warehouses simply cannot meet the requirements of today’s businesses and the associated digital transformation trends.

Due to the shortcomings of the traditional approach to ETL tools, new approaches to data processing have emerged, which are referred to in detail as automated ETL processes. Using the latest technologies in ETL tools, companies achieve remarkable results such as Doubling productivity through unified data integration, doubling cost reduction by increasing overall efficiency and optimizing the use of resources across multiple projects, and reducing measurable business impact in areas such as revenue.

Business costs, customer loyalty and more time to focus on the core market. This next generation ETL was offered by the fastest growing German start-up in the Big Data division, Data Virtuality GmbH, with solutions such as Data Virtuality Logical Data Warehouse, Data Virtuality Pipes and Data Virtuality Pipes Professional.

Data Warehouse ETL FAQS

What is data warehouse ETL?

ETL is a process in Data Warehousing and it stands for Extract, Transform and Load. It is a process in which an ETL tool extracts the data from various data source systems, transforms it in the staging area and then finally, loads it into the Data Warehouse system.

Why is ETL important in data warehouse?

Scheduled data integration, or ETL, is an important aspect of warehousing because it consolidates data from multiple sources and transforms it into a useful format. … Enhanced quality and consistency: Data warehouse deployment involves the conversion of data from numerous sources and transformation into a common format.