What is the ETL process?
The ETL process is responsible for the extraction of data from one or many source systems, the transformation of data and the loading into your data warehouses. This process is needed to make your data an analyzable whole. It consists of three phases, Extract, Transform, Load. First, we’ll go in-depth into the three phases. Later, the ways an optimized ETL process can help your business will be described.
First, you need to extract the data from its sources. You probably know your data sources as the standard databases, such as operational system, flat files, XML data sets, relational tables, web log sources, legacy systems, and spreadsheets. However, that is not the only place you can extract data from.
80% of data is hidden or uncaptured, the so-called Dark Data. This is because it is hard for companies to reach this data and because it is often unstructured. However, with recent technologies, we are able to extract data from more sophisticated sources, such as network transactions, IoT, sensors, Wi-Fi, and more.
The extraction process consists of two phases, initial extraction, and changed data extraction. In the initial extraction, it is the first time to get the data from the different operational sources to be loaded into the data warehouse. This process is done only one time after building the data warehouse.
The incremental extraction is called changed data capture (CDC) where the ETL processes refresh the data warehouse with the modified and added data in the source systems since the last extraction. This process is periodic according to the refresh cycle and business needs.
After the data is captured, it needs to be transformed into the appropriate format because it is likely that the operational systems do not have the same data formatting as the data warehouse. When the data is transformed it is moved to a data staging area. This transformation includes steps such as cleaning, format revision, restructuring, matching, and summarization. Data transformation is needed so that the final data will comply with the data warehouse you will store it in.
The transformation phase can happen before loading it into the data warehouse or after.
When data is transformed after the loading phase, this process becomes ELT (Extract, Load, Transform). The major difference is that with ELT, the extracted data goes immediately into a data lake storage system, whereas with ETL it first moves into a temporary staging area. The primary advantage ELT has over ETL is flexibility and ease of storing new unstructured data.
The final step in the ETL process involves loading the transformed data into the final destination, the data warehouses or database. This can be done in two ways, full load or incremental load. Full load involves an entire data dump that occurs the first time the source is loaded into the warehouse. The entire process is done in a bulk process. The incremental load takes place at regular intervals. The incremental load is done in a batch process or a streaming process, depending on what fits your business needs.
Reasons to invest in an ETL tool
ETL software used to be hand-coded, but today it might be better to use an optimized ETL tool. ETL tools are a category of specialized tools with the task of dealing with data warehouse homogeneity, cleaning, transforming, and loading problems. An ETL tool can provide the business with multiple benefits. An ETL tool is fast, provides trustworthy analytics, and it prevents losing data when extracting with a Dark Data capturer.
Since your business is continuously changing, your data changes continuously as well. Therefore, it is important to invest in ETL software that keeps up to date with your business. As your business changes, your data warehouse needs to change too, in order to remain a valuable tool for decision-makers.
Quick and easy
The main benefit of ETL tools is that they are much easier and faster to use than traditional methods that move data by manually writing codes. ETL tools contain graphical interfaces which speed up the process of mapping tables and columns between the source and end storages.
A clear overview for decision-making
Whether you store your data in a traditional data warehouse or in an online cloud, it still needs to be brought to one central target storage. In the end, ETL provides a consolidated overview of your companies’ data. This makes it easier for reporting and analyzing. Also, it will give decision-makers a clearer overview of their business’ activities.
A solid ETL process can help your company build a competitive advantage. If you invest in the process of ETL, you can get near real-time data insights, giving you the best information to make accurate business and operational decisions.
All in all, it provides clean and filtered data structures for exploitation by different end-user tools, increases data quality and value, and enables decision optimization. Transactional databases cannot answer complex business questions that can be answered thanks to optimizing ETL.
There are multiple reasons why an ETL process is a must for your business. In the end, what your business needs for a successful ETL process are an ETL tool and an additional instrument that extracts data from all your business and operational activities. Depending on your business needs and wishes, there are many ETL tools available to fit these desires. Having a successful ETL process can bring better insights into your data analytics, build a competitive advantage, and improve decision-making.