The world of data management has been evolving rapidly; the industry is now almost unrecognizable from what it was half a decade ago. If we take a glimpse into a not-so-distant past, very few people had the answer to “What is the ETL process.” Terms like automated ETL process, data mart, data lake, or warehousing would’ve been incomprehensible to most people, or worse, a buzzword people use to sound smarter.
What is ETL, though? Simply speaking, it is the process by which a user extracts, transforms, and loads data to a destination.
In the modern world, it is paramount that we pay attention to the strides data management technology is making and the potential opportunities it presents for a company. Companies these days are overflowing with raw data that desperately needs sifting through. The real challenge is getting insights through this data; an ETL process makes that possible.
Good data facilitates better decisions, and using the right tools to manage the ETL process is a powerful way to do so.
This blog will explain the ETL process in detail and how it can help organizations grow. Let’s begin by defining ETL.
What is the ETL Process?
The ETL process includes 3 steps: extract, transform, and load. Additional steps include data validation and analysis. The ETL process involves extracting and compiling raw data, transforming it to make it intelligible, and loading it into a target system, such as a database or data warehouse, for easy access and analysis.
ETL, short for Extract, Transform, and Load, is an important component in the data ecosystem of any modern business. ETL helps break down data silos and makes data easier to access for decision-makers.
Since data from multiple sources has a different schema, every dataset must be transformed differently before utilizing BI and analytics. For instance, if you are compiling data from source systems like SQL Server and Google Analytics, these two sources must be treated individually throughout the ETL process. The importance of this process has increased since big data analysis has become a necessary part of every organization.
ETL Process Implementation: Three Easy Steps
The acronym E-T-L can be divided into three phases which implement the entire process.
- E – Extraction
- T – Transformation
- L – Loading
Let’s dive in and learn how to convert raw data into insights through the three-step ETL process.
1st Step – Extraction
Before you can begin organizing your data, the first step in the ETL process is to pull or extract the data from all the relevant sources and compile it. This step will include the necessary preparation for carrying out data integration. The sources may include data from multiple sources: on-premise databases, CRM systems, marketing automation platforms, cloud data warehouses, unstructured and structured files, cloud applications, and any other sources you wish to draw insights from.
Once all the critical data has been consolidated, you’ll notice that data from different sources is dated and structured in different formats. In this step, the compiled data must be organized according to date, size, and source to suit the transformation process. Consistency is required in all the data to be fed into the system and converted in the next step. The complexity of this step can vary significantly, depending on data types, the volume of data, and data sources.
ETL Extraction Steps
- Compile data from relevant sources
- Organize data to make it consistent
2nd Step – Transformation
Data transformation is the second step of the ETL process. The second phase involves transformation; data extracted from the sources are compiled, converted, reformatted, and cleansed in the staging area to be fed into the target database in the next step.
The transformation step involves executing a series of functions and applying rules to the extracted data to convert it into a standard format to meet the schema requirements of the target database. The level of manipulation required in ETL transformation depends solely on the data extracted and the business needs. It includes validation and rejection of data.
Quality data sources won’t require many transformations, while other datasets might require it significantly. You can subject it to several transformation techniques to meet your target database’s technical and business requirements.
ETL Transformation Steps
- Convert data according to the business requirements
- Reformat converted data to a standard format for compatibility
- Cleanse irrelevant data from the datasets
- Sort & filter data
- Clear duplicate information
- Translate where necessary
3rd Step – Loading
The concluding step in the three-step ETL process is loading the datasets extracted and transformed into the target database. There are two ways to go about it; the first is a SQL insert routine that involves manually inserting each record in every row of your target database table. The other loading approach uses a bulk load of data reserved for massive data loading.
The SQL insert may be slow but conducts data quality checks with each entry. While the bulk load is much faster for loading massive amounts of data, it does not consider data integrity for every record. Bulk loading is ideal for datasets you’re confident are free of errors.
ETL Loading Steps
- Load transformed datasets through bulk loading
- Load questionable datasets through SQL Inserts
Significance of ETL Process in Business
There are quite a few reasons for embracing the ETL process within your organization. Let’s discuss some key advantages:
Heightened Business Intelligence
The role of the ETL process in an organization is of great importance as it allows well-informed and quicker decision-making.
ETL technology essentially improves the level of access you have to your data. You can pull up the most relevant datasets for every business decision you must make to assist you. This directly impacts your operational and strategic undertakings, giving you an upper hand. You can pave the way for your business with data-driven insights and make your competition sweat.
Greater ROI
Managing large volumes of data is no small feat. Without the ETL process to organize data and make it intelligible, a business would be wasting valuable resources in collecting data. The ETL process layer implementation means you can put all the data collected to good use, thus enabling higher revenue generation. The International Data Corporation conducted a study that has disclosed that the ETL implementations have achieved a 5-year median ROI of 112% with a mean payoff of 1.6 years.
Scalable Performance
As the business grows and market dynamics change, so must your organization’s resources and the technology it employs. The ETL system’s infrastructure allows you to add further technologies, simplifying the subsequent data processes. In addition, several performance-enhancing tools come as an add-on for the ETL process in the data warehouse. These include the tools to extract unstructured data, data virtualization solutions, and automated data warehousing platforms.
Document ETL Process
Some ETL requirements are necessary to streamline the data process. You must create external documentation for each configuration’s steps and data maps. These data maps should have graphs, including source data, destination datasets, and summary information for each step of the data ETL process.
The document will help fix errors faster and allow beginners to learn the process of ETL easily.
Data gathered through the ETL extraction process provides an in-depth historical context of your business for decision-makers. Knowing what ETL is and why it is important, it is wise for a worthy ETL tool to be present in the chest of every business that aims for an efficient ETL process to capture the data advantage.
Astera Centerprise is a robust ETL tool that integrates data from different systems and provides a unified view of all your data assets. Want to try out how our solution can help streamline your ETL process? View the demo or download free trial.