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 in a not-so-distant past, terms like automated ETL process, data lake, or warehousing would’ve been incomprehensible to most people, or worse, a buzzword people use to sound smarter.
Presently, 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. Organizing that data in a manner that provides actionable insights to decision makers is the real challenge.
Good data facilitates better decisions, and using the ETL tools to manage your data, is a powerful way to do so.
ETL Process: What is it?
To put it simply, the process of 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, is known as the ETL process. Short for Extract – Transform – Load, Process of ETL is an important component in the data ecosystem of any modern business.
Since data coming from multiple sources has a different schema, every dataset must be transformed differently before it can be utilized for BI and analytics. For instance, if you are compiling data from source systems like Amazon Redshift and Google Analytics, these two sources will need to be treated individually with the entire process of ETL.
ETL Process Implementation: The Three Steps
When it comes to the implementation of the ETL process layer, the itinerary of tasks can be divvied up into the full form of its acronym.
- E – Extraction
- T – Transformation
- L – Loading
Let’s dive in and learn how to convert raw data into intelligible insights.
1st Step – Extraction
The first step before you can begin organizing your data is pulling or extracting the data from all the relevant sources and compiling it. These sources may include on-premise databases, CRM systems, marketing automation platforms, unstructured and structured files, cloud applications, and any other data sources you wish to draw insights from via analytical processing.
Once all the critical data has been consolidated, you’ll notice that data from different sources are dated and structured in different formats. In this step, the compiled data must be organized in terms of date, size, and source to suit the transformation process. There is a certain level of consistency required in all the data so it can 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 in data warehouse. Here the compiled data is 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 sets of 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 needs of the business. It includes validation of data as well as rejection if they’re not acceptable.
Good sources of data won’t require many transformations, while other datasets might require it significantly. To meet technical and business requirements of your target database, you can subject it to several transformation techniques.
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 is the act of loading the datasets that’ve been extracted and transformed earlier, into the target database. There are two ways to go about it; first is a SQL insert routine that involves the manual insertion of each record in every row of your target database table. While, the other loading approach uses a process called bulk load of data, reserved for massive loading of data.
The SQL insert may be slow, but it conducts integrity checks with each entry. While the bulk load is much faster for loading massive amounts of data, absent integrity checks for every record. Bulk loading is ideal for datasets you’re confident are free of errors.
ETL Loading Steps
- Load well transformed datasets through bulk loading.
- Load questionable datasets through SQL Inserts.
Significance of ETL in your Business
There are quite a few reasons for embracing the ETL extraction process within your organization. Let’s discuss some key advantages:
Heightened Business Intelligence
ETL technology essentially improves the level of access you have to your data. For every business decision you need to make, you can pull up the most relevant datasets to assist you. This directly impacts your operational and strategic undertakings, giving you an upper hand. With data-driven insights at
your fingertips, you can pave the way for your business and give your competition sweat.
Managing large volumes of data is no small feat. Without the ETL extraction process to organize data and make it intelligible, a business would be wasting resources collecting data in the first place. The ETL process layer implementation means you can put all the data collected to good use, thus enabling the generation of higher revenue. In fact, the International Data Corporation conducted a study that has disclosed that the ETL implementations have achieved a 5-year median ROI of 112% with mean pay off of 1.6 years.
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 on top of it, simplifying the subsequent data processes. In addition, there are several performance-enhancing tools that come as an add-on for ETL process in data warehouse.
These include the tools to extract unstructured data, data virtualization solutions, and automated data warehousing platforms.
Data gathered through the ETL extraction process provides an in-depth historical context of your business for the decision makers. A worthy ETL tool to be in the tool chest of every business that aims to capture the data advantage.