Nowadays, it is common for companies to use ELT and ETL processes in working with Big Data. This allows running documentation from the different sources of information used in the data lake and data warehouse.
Moreover, ELT and ETL processes are quite affordable for every interested company or institution. So, as you may have already guessed, we are going to discuss ETL data modeling practices today. We believe that this will have a positive impact on your business growth.
First of all, you need to know the differences and similarities between ETL and ELT. What’s more, it is recommended to understand the most used words on this topic, like data lakes and data warehouses. Let’s start!
How do ELT and ETL processes work?
The ELT (extract, load, transform) is the process in which the data is modified after having been transferred to the receiving database, without making any changes. The first stage of this operation is to extract the data. The maneuver of loading the data to the database it receives is the intermediate stage where the ELT admits that the target system executes the respective transformations.
For this reason, we recommend you apply ELT and ETL processes while working with large amounts of data. In addition, unstructured data, the source, and the target database use the same technology when the amount of converted data is massive.
The transformations made on the data are improved by the database that receives them. Usually, it can be NoSQL databases or Hadoop clusters. As we can see, in the ETL process, the data circulates from the origin to its final destination. So, the whole responsibility for the success lies in the ETL and the chosen database technology.
What is more, ELT and ETL processes are responsible for:
- mobilizing large amounts of data;
- integrating them;
- introducing them into a common site.
What are the main differences between ETL and ELT?
ETL and ELT are very similar. Yet, the most obvious difference is in the order in which the ETL and ELT processes in Big data execute the various required operations. These methods are best handled in different situations.
So, here are to your attention some of the most crucial differences between ETL and ELT:
- ETL is the process of extracting, transforming, and loading data.
- ELT is the process of extracting, loading, and transforming data.
- Within ETL, data is moved from a data source to an intermediate data store.
- ELT uses a data warehouse to perform basic transformations. There is no need for data staging.
- ETL can help ensure privacy and compliance by cleaning sensitive and secure data before it is loaded into the data warehouse.
- ETL can perform complex data transformations and can be more cost-effective than ELT.
The difference between ETL and ELT can be explained quite easily. However, realizing the whole picture and the cool benefits of ETL over ELT requires some time. Plus, a deeper understanding of how ETL works with data warehouses and how ELT works with data lakes is also necessary.
The result of using both ETL and ELT
Organizations, companies, and institutions always need to make the most of the advantages of these two computer methodologies. They use ELT and ETL processes in Big Data, where ELT is responsible for quick introductions of unstructured data. As for ETL, it is helpful to make them more flexible and secure.
For this reason, the vision has expanded towards ETLT, which executes the following steps that you need to know:
- Extraction. Data from the different sources are collected and transferred to the development area for preparation.
- Loading. At this stage, data is uploaded to the Data Warehouses.
- Transformation. It is the last stage, but not the least. It is where operations are executed to transform and integrate data from various sources.
This result of the operations carried out on the existing data. It allows adjusting the times and technologies used to improve the amount of work. Therefore, the first group of changes is faster and more effective in providing the data with the necessary preparation and greater security.
Can the ETLT bring any benefits to the ETL and ELT processes?
The ETLT in Big Data provides us with the advantages of both – ETL and ELT processes. In this way, it manages to lighten the introduction of data. At the same time, the ETLT provides the security and quality required in modern companies, organizations, and institutions that use such technologies.
ETLT processes are commonly used when it is necessary to filter, anonymize or mask data for regulatory reasons, before capturing it in the data warehouse.
How to choose the best solution
As we have already mentioned, the ELT and ETL process in Big Data have become fundamental and essential for numerous organizations. The ETL can brag about many years of popularity. As well, it has a sufficiently high maturity and flexibility. This is really impressive, as it was not designed to work correctly with structured data and relational databases.
As for ELT, it was created to execute activities with NoSQL solutions. For this reason, the difficulty of the operations it performs is less. Still, the size of the data it can process is greater than those processed by ETL.
In this order of ideas in the execution of the two processes, it is possible to observe the accuracy, in terms of the type and structure of the data in the ETL processes. This hinders future transformations. At the same time, in ELT processes it is normal to make movements of unstructured and structured data with equal procedures.
In summary, the data lake is unlimited, but you should have deep knowledge and comprehensive documentation about the ETL and ELT processes. This will help you achieve the required transformations and maximum quality for the use of the stored data.
Luckily, you can choose an option to work with professional IT services, like Visual Flow, to save your time and energy. This way, your team, and customers will get a better experience with data analytics. Moreover, obtaining a data warehouse is a reasonable investment that can raise your profits.
