Your company’s customer data sits in one place, website analytics live in another, and financial records exist on a third platform altogether, but do these systems talk to each other?
When executives need a complete picture of customer lifetime value, analysts spend a lot of time manually combining data from these separate sources.
To overcome this, the ETL process provides an approach to extract data from various sources, transform it into a consistent format, and load it into a centralized data warehouse where teams can analyze it.
Understanding how ETL works will help you consolidate your company’s data, improve decision-making accuracy, and create a foundation for business intelligence that drives growth.
What is the ETL Process?
The ETL process is a data management workflow that Extracts data from source systems, Transforms it into a usable format, and Loads it into a target database or data warehouse. It enables data consolidation, ensures consistency, and supports analytics and reporting.
ETL modeling process refers to Extraction, Transformation, and Loading. As the name implies, this process extracts information or data from source systems and transfers them into a data warehouse.
The process requires active or updated information from developers, testers, analysts, stakeholders, and top executives. ETL is the best method for a data warehouse system that will automate and neatly document the information daily, weekly, or monthly. There are two types of ETL processes:
- Traditional ETL
- Data warehouse ETL process
Traditional ETL vs. Data Warehouse ETL Process
The Traditional ETL approach requires a data scientist or analyst to develop the on-premise databases and data pipelines manually. Since it’s a manual job, this way of conducting the ETL takes time to process. This type of technology is difficult to scale and evaluate and requires sacrificing raw data for the data volumes.
In contrast to the Traditional ETL, there’s the Data Warehouse ETL. This one allows the transformation and data modeling in the SQL database, allowing all sides, such as data analysts, scientists, BI team, etc., to gain control as they all understand the language.
In the following sections, we’ll review these ETL processes separately.
Traditional ETL
The traditional ETL method is manual so it is slower. All manual adjustments complicate the adaptation process in the business environment, as such, this model is recommended for relational databases that aren’t loaded with unstructured data. This is because engineers and developers will need more interaction if the ETL extracts more data from more sources.
Additionally, if there is an increase in data, the traditional ETL requires more disk space to store all the information.The calculations also require fast processors to execute the operation successfully.
If you sum up the costs for this type of ETL, you will realize that it’s time-consuming and costly. However, with today’s alternatives, it is unlikely for a company to choose the traditional ETL process.
Data Warehouse ETL
With the technology we use every day, the data circulating in a company doesn’t have only a single source of information. These sources could be enormous, making it difficult to integrate them into your system. Monitoring or managing this type of information is also problematic.
To increase your business performance, you need to incorporate a data warehouse architecture that can organize the data and building blocks and keep the data hygiene in order. Part of this architecture involves a Data Warehouse ETL tool.
As mentioned, this ETL process allows better control over the information gathered. Considering that data today is analyzed in a raw form, opposite to the previous preloaded OLAP summaries, the Warehouse ETL type is more suited, as it is more flexible and transparent.
How Does ETL Process in a Data Warehouse Work?
The ETL process in a data warehouse works by extracting data from source systems, transforming it into a consistent format, and loading it into the warehouse for analysis. ETL ensures data quality, supports integration from multiple sources, and prepares data for business intelligence tools.
As we mentioned, both ETL types, although different, revolve around the same ETL. Below we explain the ETL process methodically.
Extraction
The extraction refers to gathering processed and unprocessed data from various sources and storing it in a single archive. Unlike before, when information extraction came from a few sources, now the data inflow comes from many sources, including management data systems, social media, and Google Analytics.
This is the first step in the ETL process and is usually the most time-consuming. The main reason is that the data from the sources may be too complicated or otherwise difficult to process, hence taking more time to extract them. Another reason is that the gathered information may come in multiple formats.
Transformation
As the name implies, the second stage of the ETL process includes transforming the gathered data. There are several types of ETL transformation.
But in general, two ETL transformation types are used:
- Data cleaning – It verifies the existing data and corrects the irregularities. The irrelevant data is dismissed or deleted.
- Data enriching – Checking if there are any missing parts in the data and filling them with new and correct information.
Out of the two, data cleaning is used more often today.
Loading
The final stage of the ETL process is loading the processed data. Specifically, it relocates the data from the second stage to a target database. This target system could be a data warehouse, a data lake, SQL, NoSQL, etc. Simply put, it is a place where it will be ready for major data analysis.
There are two main ways of data load:
- Full load – Loading the entire data at once, occurring the first time the data is loaded.
- Incremental load – Loading data in certain intervals. Depending on the data type you’re loading, you can choose between streaming incremental load (for small volumes) and batch incremental load (for big volumes).
Top 5 Challenges in the ETL Process
Although a great way to accumulate data, the ETL process naturally comes with some challenges. These are some of the most common ones:
- Loss of data along the way
- Hard-to-read software requirements that slow down the process
- Difficulties in acquiring and creating test data
- Data quality during loading
- Scaling complexity
Top 5 Benefits of the ETL Process
Every business, regardless of size, needs regular ETL checks to see improvements. It is essential for business growth because you can only expect an improved performance with full data insight. The main benefits of the ETL process are the following:
- Providing clear sight of the data
- Providing a development framework to simplify the decision-making process
- Advanced data profiling and filtration
- Correction of wrong information and clearing spam
- Improving overall performance
What is the Difference Between ETL and ELT?
Although some use these terms interchangeably, ETL and ELT are different. The key difference is the order of the steps. In the ETL process, the data transformation happens before the loading phase, while the ELT process involves transforming the collected data after it’s loaded.
The ELT will load the collected raw data straight into a data warehouse instead of moving it to a processing server. You can expect all data transformation, enrichment, and cleansing within the final stage. The ELT is a new data processing approach used to improve scalability.
How Does Boomi Help in Automating Your ETL Process?
If you want to use or improve your ETL process, Boomi is here to help.
Boomi is not only an extract and load tool but a complete end-to-end platform. Boomi offers not only ETL services but also data orchestration, transformation, and reverse ETL. With reverse ETL, you can quickly push data to where you want it, adopt operational analytics, and eliminate manual processes.
Boomi also offers ETL processes with comprehensive integration capabilities.
Speak to a Boomi expert today, and see how you can employ the ETL process best!
Key Boomi Features:
- Cloud-native integration platform that connects applications, data, and people across hybrid IT environments
- Pre-built connectors for popular business applications like Salesforce, NetSuite, and ServiceNow
- Visual drag-and-drop interface for building data integration workflows without coding
- Real-time data synchronization capabilities for keeping systems updated automatically
- Master data management tools for maintaining consistent data quality across all systems
- API management and governance features for secure data sharing between applications
- Built-in data transformation functions for cleaning and formatting data during transfers
- Scalable architecture that handles both small business needs and enterprise-level data volumes