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, 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.
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. The real challenge is organizing data in the ETL process in a manner that provides actionable insights to decision-makers.
Good data facilitates better decisions, and using the right tools to manage the process of ETL, is a powerful way to do so. Hence, for accurate insight, data in the ETL process needs to be error-free.
What Is ETL Process?
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, ETL is an important component in the data ecosystem of any modern business and mostly used for OLAP processing in a data warehouse. The ETL process is what helps break down data silos and makes data easier to access for decision-makers.
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 SQL Server and Google Analytics, these two sources will need to be treated individually with the entire process of ETL. The ETL process of data has increased in importance since big data analysis has become a necessary part of every organization.
ETL Process Implementation: Three Easy 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. This ETL requirement and gathering process will include necessary preparation for carrying out data integration. The data sources may include on-premise databases, CRM systems, marketing automation platforms, cloud data warehouses, 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 – Data Transformation
Data Transformation is the second step of the ETL process in data integrations. In the first step, the ETL deployment was carried out. Now, in the second step, data extracted from the sources is 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 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.
Quality data sources won’t require many transformations, while other datasets might require it significantly. To meet the technical and business requirements of your target database, you can subject it to several transformation techniques.
ETL Data Warehouse Transformation Steps
The ETL process in data warehouse includes the following steps during the transformation of data:
- 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 have been extracted and transformed earlier, into the target database. There are two ways to go about it; the 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 data loading.
The SQL insert may be slow, but it conducts data quality checks with each entry. While the bulk load is much faster for loading massive amounts of data, it does not take into account data integrity for every record. Bulk loading is ideal for datasets you’re confident are free of errors. This concludes the database ETL process.
ETL Loading Steps
- Load well 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 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 payoff 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 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
For streamlining ETL processes, it is important that you create an external documentation carrying all the steps and data maps for each configuration. These data maps should have graphs, including source data, destination datasets, and summary information for each step of the ETL data 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 the decision makers. A worthy ETL solution to be in the tool chest of every business that aims for an efficient ETL process to capture the data advantage.