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App Development
6 minutes read

Understanding ETL Data Integration

By Robert Kazmi
By Robert Kazmi
App Development
6 minutes read

ETL data integration is a critical component of HiTech data analytics tools. You can’t generate valuable insights using Machine Learning and AI if you don’t have good, clean data to analyze. The challenge for many businesses is that they have several data sources generating important data in different formats, which is why data integration has become vital. 

There are several different approaches your organization can take to data integration. However, ETL is one of the most popular and well-tested. This post will explore ETL data integration. First, we will give you a better sense of what it is and why it is important. In addition, we will also briefly touch upon some of the popular data integration alternatives to ETL.

If you want to utilize powerful data analytics tools in your mobile application or web service, data quality needs to be one of your top priorities. 

What Is ETL?

ETL is a data integration process that combines information from multiple data sources in a unified data warehouse or other target data storage system. ETL stands for extract, transform, and load. These are the three main steps in the ETL data integration process.

Raw data is copied from multiple data sources into a staging area during extraction. This data can come from structured and unstructured sources, including email, CRM, ERP, web pages, SQL and NoSQL servers, and other files. 

Once the raw data is compiled in the staging area, it undergoes transformation to make it usable by data analytics tools. Transformation can include several tasks, such as filtering, authenticating, validating, encrypting or removing sensitive data, and more. Often, data is transformed in this step to match the data format of the target data warehouse.

Finally, once all the data has been extracted and transformed, it is loaded into the target data warehouse. If data changes, then there will be periodic loading of data changes. In some cases, there will be full data refreshes to erase and replace all the data in the warehouse. 

Typically, the ETL data integration process is automated during off hours when databases, data warehouses, and other tools, such as CRM, are not in active use and traffic is at its lowest point. 

The ETL data integration process forms the backbone of data analytics and Machine Learning tools. This process can also meet business needs like monthly reporting requirements, etc. In addition to unifying data, ETL data integration is commonly used for extracting data from legacy systems and cleansing data.

ETL Vs. ELT

If you have seen the acronym ELT data integration, it is not a typo. ELT is a modified version of ETL. Instead of transforming data before loading it into a data warehouse, ELT loads data to a target source before transforming it. As a result, ELT data integration is more beneficial when businesses have high volumes of unstructured data since loading can be accomplished directly from data sources. 

ELT has been gaining popularity in big data settings because it requires less upfront data extraction and storage planning. ETL requires more initial planning before implementation, such as identifying data points for extraction. However, ETL is a well-established data integration process with strong, thoroughly tested best practices, whereas ELT is newer with less established best practices.

The Benefits of ETL 

Now that you have a better understanding of the ETL data integration process, let’s explore the benefits of ETL including:

  • Save time 
  • Simplify complex data 
  • Reduce human error 

Save Time 

ETL data integration helps businesses save time because it can be automated. For example, collecting, transforming, and loading data manually is time-consuming. However, thanks to the ETL data integration tools, these tasks can be automated to give your team members more time to focus on analysis instead of rote, mundane tasks like formatting and importing data. 

Simplify Complex Data 

When various data sources are being used to gather information, the data points quickly become complex with timestamps, locations, campaign names, URLs, etc. ETL data integration can simplify the complexities associated with multiple data sources. However, formatting and collating all of this data quickly becomes complicated without an ETL data integration process. 

Reduce Human Error 

Mistakes are natural. No one is perfect. However, data collection and analysis mistakes can lead to poor outcomes for organizations relying on data analytics. ETL data integration reduces human error and softens the impact when errors are made. An established ETL process can help your business ensure that one mistake does not lead to another and disrupt your data. 

Data Integration Alternatives to ETL 

We have already covered ELT. However, ELT is not a major departure from the ETL data integration process. If you’re looking for different data integration approaches, you might be interested in the following alternatives to ETL:

  • Data virtualization 
  • Change data capture 
  • Stream data integration 

Data Virtualization 

Data virtualization is a data integration method that utilizes a software abstraction layer to build a unified, integrated, and usable view of data. However, in this approach, data is not copied, transformed, or loaded to a target data warehouse or system. As a result, data virtualization allows organizations to create virtual data warehouses without spending money or time managing a separate storage platform. 

Change Data Capture

Change data capture is an integration process that identifies and loads only the data that has changed from the source to the target system. Often, change data capture is used in tandem with the ETL process. As a result, it can be used effectively to minimize the resources used during the extraction phase of ETL. In addition, this data integration approach can be used independently from the ETL process to move formatted data that has changed into a target system. 

Stream Data Integration

The stream data integration approach consumes data streams in real time, transforms them, and loads them into a target system for data analysis. The key difference between ETL and SDI is that data integration happens continuously. As soon as data is available, it is integrated through SDI tools. This type of data integration is used to power fraud detection and other applications that require real-time data analysis. 

Final Thoughts

ETL data integration is a tried and true process for organizations with multiple data sources. But will it be the best choice for your business? That depends on your data needs and systems. If you want to learn more about ETL data integration, contact an experienced app development partner for guidance.

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