Extraction, Transformation, and Loading: What ETL Is Created For and How It Works

Extraction, Transformation, and Loading: What ETL Is Created for and How It Works     

The collection and analysis of information from different sources and departments is an integral part of many companies — particularly those working in the e-commerce sector. Data analysts process the information gathered from multiple sources and need to use the ETL process to interpret this data effectively and quickly. 

If your company has to work with databases, data warehouses, data lakes, and similar resources, then you certainly need ETL to improve the information extraction, transformation, and loading process. 

We have created this article to provide a detailed definition of ETL, how you can use it, and what advantages you can gain from its implementation.    

What is Extraction, Transformation, and Loading (ETL)?

Extract, Transform, Load, or ETL, is a set of processes for data storage management, including the following:

  • Data extraction from external sources (database tables, files)
  • Data transformation and cleaning according to business requirements
  • Processed data loading in corporate data storage  

The ETL data concept was created due to the appearance of multiple corporate information systems that needed to unify and analyse stored data.    

Usually, ETL processes are used when it is necessary to transfer a large volume of heterogeneous data, with the goal of collecting, unifying, loading it to a new system where all the information is saved.

Systems are different, and the primary ETL task is to adapt data from various sources for them.  

Let’s take a shop as an example. Accounting for offline customers is kept in one format,  with online customers in another. In this example, data formats are different, as well as the devices being used. Therefore, the data should be uploaded and transferred to a unified format in order to maintain a shared database with other branches. This is where the ETL database is applied. 

Many free and paid ETL solutions are available (aka ETL software or ETL tools). Developers can create some simple ETL solutions for a specific task. However, large systems work with different data out of the box. 

Thus, the applied purpose of ETL processing is to organise the unified data structure by integrating various information systems. ETL systems are critical tools for business intelligence (BI), where analysts at companies can monitor the business processes and gather up-to-date information.  

Need a detailed consultation? Contact Go Wombat right now. 

How ETL works

Regardless of the specifics of the construction and operation of the ETL system, it must ensure the implementation of three main stages of the ETL process:    

Extraction. The ETL databases extract data from one or a few sources and transfer them to an intermediate buffer for further processing. Also, at this stage, the ETL system may implement validation and data verification to ensure compliance with the specific criteria. The system verifies whether it can upload the data into the new warehouse without loss. 

Transformation. During this phase, the system modifies the data to fit the new storage requirements. ETL transformations change the format of the information being processed and the encoding (if necessary), separate the data from the unnecessary, and bring everything to a single format.    

Load. The final stage occurs when the modified data is uploaded to new storage and placed in appropriate locations. Apart from the information itself, the ETL system can transfer metadata (information about data structure).

Thus, the ETL process means the data transfer (data flow) from the source to the recipient through the intermediary data that contains temporary auxiliary tables, which are created solely to organise the loading process. The analyst describes the requirements for data flow arrangement. Therefore, ETL is not just a data transfer process from one application to another but also a tool to prepare data for analysis.  

ETL advantages: why your business needs it

The ETL system has multiple advantages for your business, we've listed the significant benefits below. 

Extract, Transform, and Load — this is the process that enables efficient data management so you can get acquainted with all the benefits of ETL.

Time-saving

The most significant advantage of the ETL process is that it saves time and effort from when data is processed manually. ETL helps you automatically collect, transform, and consolidate data. As a result, you can save time since you don’t need to import data manually.  

Streamlining

The ETL system makes it easy to work with complex data. As your business grows over time, you need to work with vast amounts of complex and diverse data. For example, there can be different time zones, client names, device identifiers, and locations. Besides that, incoming data files may have other formats and types. So, the ETL process makes your work better. 

Reduced risks

Regardless of how careful you are about data accuracy, none of us is immune from mistakes. For example, data can be accidentally duplicated, or you can manually input incorrect data. An ETL system makes it possible to avoid such scenarios since the process is automated and doesn’t require human intervention. 

Better decision-making

High-quality information is fundamental for making more effective corporate decisions. ETL may ensure that the data you receive for analysis has the highest possible quality since it automates work with critical data and reduces potential errors.

Higher ROI

As you save time, effort, and resources, the ETL process eventually helps you increase the return on investment (ROI). Additionally, the improvement of business analytics enables you to increase your profit. 

Where ETL Is Used 

The primary purpose of ETL is to facilitate data transfer from one location to another. This means that ETL can be used in multiple sectors where you need to combine information from numerous sources (e-commerce, logistics, healthcare, and so on). 

ETL may improve many business processes in almost all fields where you process large data amounts. You need to know the list of sectors where ETL will be beneficial.

Database. Any data storage, in one way or another, faces the need for migrations and transfers from locations. Sometimes it can be a one-time migration, but often companies have data entered into their databases from different sources simultaneously. When working with databases and data, the ETL process ensures it will be smooth and uniform.  

Data warehouse. A data warehouse or DWH is a type of information storage. DWHs are specific databases for organisational purposes, internal analysis, and reporting. These are administrative and archive databases of the company that contain essential business information. Combining the information in one place is necessary to ensure that business processes and internal analytics function correctly. In the case of DWH, the data is always taken from different sources, so ETL for data warehouse settings8 is a frequent application. 

Big data. Working with big data implies the transfer of large information sets among various systems. ETL systems help big data analysts and developers solve complicated tasks and manage data massifs. 

OLTP/OLAP. Another ETL example is a layer between OLTP and OLAP. These systems process data, each in a different way:

  • OLTP stands for online transaction processing. In this model, systems focus on a continuous flow of small transactions, many of which are repeated. 
  • OLAP is online analytical processing. Unlike OLTP, OLAP is required to process substantial analytical requests with multiple parameters. 

OLAP works well where OLTP fails, and vice versa, so data sometimes needs to be transferred from one system to another. This is where the ETL process is applied. 

Internet of Things. IoT enables the connectivity of multiple smart devices between each other. Due to IoT, devices can communicate over a local network and solve more challenging tasks than when working separately. Smart homes are created using IoT. 

Data from different devices have different formats and characteristics. Therefore, you must apply ETL to store it in a single base. For example: a dashboard in a smart home that displays the information from sensors and information about the condition of all IoT devices. 

Machine learning.  Specialists in AI and machine learning sectors operate huge data massifs called datasets. These data should be processed, uploaded, and used for learning and analysis. Here ETL is used to migrate data to a single warehouse when creating a dataset, for example. 

Cloud technologies. Today cloud services are used to store different types of data. They replace on-premise storage. Most companies actively migrate their data to the cloud, using ETL solutions to transfer all data from different sources. 

Analytics. You can use ETL data analysis, marketing analysis and other types of analytics. Analytics means a large amount of information gathered from various sources: it needs to be compared and analysed, and then predictions are made on its basis. Therefore, ETL is used in this field to process all available information.

Our experts in ETL will help you deal with all the issues you have. Contact Go Wombat!

Types of ETL tools

Not all ETL tools are the same. There are four different types, and they work in different environments. You should choose the one that meets your requirements most. Here is the list: 

Your business works with the specific data type, and you should use the right ETL tool to meet the requirements of your business. We have the list of available tools.

Batch processing ETL tools

This type was used widely until very recently. Batch processing was performed in on-premises tools, which means that all the data gathered during the day was synchronised during off-hours (at night). The reason for this is that the processing of large data volumes takes a lot of time and resources. Today cloud-based tools replace batch processing since they allow specialists to do real-time extraction and processing, while batch processing can be the reason for outdated information. However, batch processing is still used by some today. 

Cloud-native ETL tools

As mentioned above, cloud-native ETL tools are a new generation of the ETL process. Cloud-based tools can extract and load data from sources directly to data storage. Then, the ETL app may transform data using the cloud scale, which is very important when working with big data, for example. Cloud-native ETL tools can be deployed in the company’s cloud infrastructure or be hosted like SaaS. 

Open-source ETL tools

When speaking about ETL, open-source tools mean a low-cost solution instead of commercial ETL applications. As a result, open-source ETL tools are used widely in businesses today. Apache Airflow and Apache Kafka are good examples. However, such tools have one drawback — they may have limited features, and enterprises may run into problems when they need to extract and transform large data amounts. 

Real-time ETL tools

Everything is simple here — this is another replacement for batch processing tools, and real-time tools allow processing with a distributed model and streaming capabilities in real-time. This option is vital for the financial sector especially. However, it shouldn’t be used in every case. As we mentioned before — everything depends on your requirements. 

How Go Wombat can help you

Whoever owns the information owns the world, right? Therefore, companies must be aware of the data they gather if they want to succeed.

To deal with enormous data amounts, the ETL process helps you extract and transform all this information into valuable insights to boost your business. 

However, this process remains challenging, and you need the assistance of a skilled software development company that has data scientists and business analysts to use your data correctly in your business's favour. 

Go Wombat is the right company to partner with. We focus on full-cycle development and can create your software from scratch, considering all the technical nuances from the get-go.

In addition, business intelligence, software discovery phase, and advanced analytics are are available through our services, so we can make software that will deal with data efficiently. 

Got questions? Let Go Wombat be your consultant! We’re looking forward to working with you — contact us!

FAQ

What is ETL, and how does it work?

ETL stands for Extract, Transform, and Load. It is the process when data is extracted from one or several locations. Then, extracted data is transformed to fit the targeted data warehouse. After that, it is loaded into the data warehouse so this information can be used for analytics and reporting. 

What is an example of ETL in use?

When you need to synchronise data from multiple sources and there are two different databases, using the ETL system, you easily unite these databases, and information will be migrated correctly. 

Why is ETL important?

ETL systems enable an efficient data management process so that massive data amounts can be processed quickly. The importance of ETL is in its opportunities for making data scientists’ work easier when they assess and analyse data. The ETL system may turn raw data into business intelligence to help companies improve their decision-making processes.

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