Methods of organizing data include: ETL – extract, transform, load, as well as ELT – extract, load, transform. Their main function is to change the location of information. But each of these methods has its own individual features and is designed for different purposes. While ETL changes the structure of data before putting them on the server, ELT performs such modifications after copying.
ETL is considered an outdated data engineering method. It is most useful for making major changes in small amounts of information. The ETL process is also ideal for users who place a high priority on data preservation.
ELT is a newer method suitable mainly for analysis. It is used to successfully edit organized data as well as information without structure.
To decide which of the two ETL vs ELT methods is better to use, you should know what you need to do with the information: store it, edit it, or perform analytics. It depends on your business line and what kind of information you need. A Salesforce to BigQuery can always help you answer this question.
The Concept And Main Features Of ETL
Transferring, processing, and loading is the order in which data is first copied from other resources, then modified on a secondary server, and finally placed in the desired database. So, how to solve the ETL vs ELT problem?
The ETL method is used when you want to bring the information to a form suitable for the resulting database. It was first introduced in the 1970s and it is still in demand today for local databases that do not require significant memory resources.
How does ETL work in practice? The system of storing information OLAP works only with relational databases adapted for SQL. In such archives, ETL will be responsible for consistency by first addressing the received information to the processing server and then converting the problem data for processing using SQL. The copied information is placed in the archive only after its necessary modification on the intermediate server.
What Eoes ELT Do?
ELT differs from the previous method in that the incoming data will not be modified before being uploaded to the server. Data in its original form will go directly to the place of storage without modification on the intermediate server.
The ELT algorithm assumes a data load, as well as all works on editing and modifying the information inside the final archive. Raw clusters of data can stay in the archive as long as you want, so they will be available for modification all the time.
ELT is an innovative solution, applicable due to the creation of scalable cloud information stores. These include Amazon Redshift, Snowflake, Microsoft Azure and Google BigQuery. All of them use digital processing methods that improve and simplify all modification and storage processes. The ELT system has no global application today, but demand for it is steadily growing as cloud storage becomes more popular.
The Main Differences Between ETL And ELT
The methods under consideration differ from each other by the place of data processing, as well as by the way the data is stored. ETL changes information in an intermediate storage location, while ELT performs the same procedure at the final data storage location. ETL does not transfer primary information to the archive, while ELT does it.
When using ETL, the information extract comes with some delays due to its modification on the dedicated device before being placed in the storage. ELT, on the contrary, loads data at a higher speed, as it is sent to the receiving server without processing. This interface allows information to be entered and processed at the same time.
Due to the loading of information in its original form with ELT, it is possible to create an information-rich archive that is useful for subsequent analysis by specialists in different business areas. As the BI user refocuses their goals and objectives, he can request the information again for processing using new strategies. However, ETL does not form initial information clusters, requests for which can be repeated as many times as desired.
Due to these characteristics, ELT has high performance, as well as the ability to extend functions. This mainly concerns the reception of information in large volumes and the processing of arrays of a modern data analytics stack with ordered primary data as well as analytical information.
Conversion of raw information plays a key role in the process of working with the repository. At the moment, ELT is considered one of the most reliable solutions for such a purpose. The system is more qualitative and reliable than ETL in processing the differently structured information blocks used for further storage and use.
Unordered information includes pictures, videos, text files, and presentations, in other words, most of all the data received. Ensuring its availability and further processing is quite a time-consuming process. The next tasks of the ELT upgrade include solving the key problems of recording unordered data, which will make obtaining and uploading information into the repository as simple and a low-cost process as possible.
In addition to the mentioned advantages, ETL is excellent at coping with complex computational tasks, outdated algorithms, and ways of organizing information that requires deleting identifying private data or performing other additional actions before gaining access to the target system.
ETL and ELT pipelines have their own filters and deletion functions. These features are basic for data structuring. Due to the fact that ETL finishes processing information before putting it on the server, this standard is the best option for following compliance and private information transfer.
Almost all companies are obliged to encrypt, cleanse or conceal information in order to support the privacy of their users. Organizations that fail to do this risk neglecting compliance rules and therefore leaking private, unclassified information. Of course, this can also happen by accident. But ETL keeps you safe from unauthorized data transfers. This is because the information is deleted and filtered before it is transferred to another storage medium.
ETL And ELT: Detailed Description Of Features
In the previous section, the main differences between these data acquisition and handling systems were discussed. A detailed description of the technical features and properties is given in the table.
| Feature | ETL | ELT |
| Operation principle | Information is loaded from the source, processed on an intermediate PC, and forwarded to the destination. | Data sets are copied from the source directly to the destination and processed there. |
| Way to get the information | Primary data is copied using API connectors | Primary data is copied using API connectors |
| Processing and transformation | The primary data transformation takes place on an intermediate server | Primary data are processed and modified at the destination |
| Putting into storage | Previously processed information is loaded into the target repository. | The target repository receives data in its original form for further processing. |
| Promptness | Processing the information before uploading it to the target medium is a complex and time-consuming process. | The information is processed directly on the target medium. Therefore, the loading procedure takes much less time. |
| Processing by coding | This is done on an intermediate server. It is the most suitable solution for particularly complex processing or initial deletion. | It takes place in the final database. Information processing is performed simultaneously with uploading, which offers a significant gain in time and quality. |
| Period of existence | The method of data transfer has been known for more than 20 years. All its rules and characteristics are described in detail in the software documentation. | It is an innovative method that is not as thoroughly described and documented. |
| Information security | Pre-upload processing can reliably protect sensitive information. | Uploading information directly to the repository requires extra effort to protect unclassified data. |
| Service | The intermediate server requires additional measures for regular technical maintenance. | The absence of a data conversion server reduces the need for service work. |
| Financial costs | Additional server equipment causes an increase in costs. | The costs for a configuration with fewer components will be significantly lower. |
| Query creation | The information is modified before the final copy. Queries on unprocessed clusters are not possible. | Unprocessed information is loaded into the final database. It becomes available for an unlimited number of queries. |
| Compatibility with cloud structures | No support for cloud data. | It is possible to interact with cloud storage. |
| Information output | Structured output methods are most often supported. | Structured, unstructured, and partially structured information can be output. |
| Optimal amount of information to process | The best option for small information requiring time-consuming, detailed processing. | The advantageous solution for large amounts of information, work with which requires high accuracy and efficiency. |
Emergence And Development Of ELT And ETL
The development of digital technology has touched virtually all areas of business and economic sectors. Organizations that work actively with information need to process and store it intelligently in order to manage access and fulfill daily requests quickly and efficiently. Owners of large, medium and small businesses are forced to use multiple data sources to always have the right picture of the current situation in their direction. Voluminous structured queries require combining different sources of information into one cohesive form. But data integration isn’t just a modern need. People have been concerned about the issue in question since the distant historical era.
Opportunities for merging and quality data processing appeared in the late 1960s, when punched cards gave way to solid-state drives, guaranteeing fast access to the information you need. Then, IBM and other similar corporations rather quickly created universal Database management systems (DBMS). Such software complexes improved the interaction between individual machines, but the problem of communication between information sources, integrated arrays, and external computers also emerged.
Since the 1970s, ETL was popular as the first unified way to greatly simplify integration. At that time, large organizations were actively acquiring computer hardware that could work with a variety of information sources. Such companies were in desperate need of proper collection and processing of data required for key operations. These included all sorts of transactions, payroll calculations, accounting, inventory management and other operations in the search for strategies and resource management.
In the 1980s, ETL became even more important as the first structured information repositories emerged. They could combine information from different sources, but each required its own ETL. This situation led to a huge demand for this data processing system. As a result, by the early 2000s, such tools had been adapted to meet the needs of any average company, along with large market players.
The beginning of the XXI century was marked by the emergence of cloud-based ways to store and process data. At that time, new methods of processing information emerged. As a result, ELT appeared. With this new solution, it was possible to put unlimited amounts of primary data directly into the cloud. Now, users working in different areas could create as many SQL queries to structure information in this way and get the desired result. Business analysts received a particularly great benefit. This industry has experienced a powerful impetus, due to the ability to quickly process large amounts of data. Together with numerous visualization tools and structures based on DWH ELT, the world has entered a new age of analysis and fast, efficient information processing.
The Best Environment For Processing Large Data Sets
A number of special services have been created today to work with data pipelines that contain a large amount of information. One of them, Renta ETL SaaS ELT, is a universal set of programs designed for information pipelines, making schedules of work operations, and performing actions with data. The program has a full set of modern features for working with target clusters.
Main Features of Renta ETL
The Renta ETL SaaS ELT service is an example of speed and complex automation. The system includes more than 200 connectors which allow you to instantly connect to the necessary source: databases, storage, file servers, and other objects. Today, Renta ETL service actively uses widely used connectors, including
- Shopify;
- Snowflake;
- Amazon S3;
- Firebolt;
- Salesforce;
- MySQL;
- PostgreSQL;
- BigQuery;
- Rest API;
- Redshift;
- Databricks, and many other best ETL tools.
With Renta ETL, the user can activate the processing of the desired segment as quickly as possible. At the same time, the maintenance costs of the platform are minimal.
Compatibility with Python
In many cases, information sources need custom code. Especially for this, Renta ETL features Python compatibility so that the user can get differently structured information and process it according to his needs.
Useful, universal Renta ETL applications
1-Click Data Apps software will allow you to access all the workflows of your business as quickly as possible. It will use information templates, pipelines, processing tools, table views and logical solutions selected by the system based on long-term cooperation with corporate users.
Support for all stages of information processing and structuring
Every stage of data processing, from array creation to final conversions, is managed in a closed system, which simplifies the process and minimizes possible errors. Procedures are efficiently managed through the command line and powerful API solutions.
Professional Technical Support
Renta ETL SaaS ELT technical service demonstrates high efficiency and usefulness for those who need highly effective software in practice. The customer service of the software complex is positively evaluated by G2. Therefore, Renta ETL customers can effectively use this service to store and process the necessary data stack.
Which Service Is Better?
Many ordinary users, as well as experts, continue to argue about which data processing system, ETL or ELT, is the most suitable for solving modern information processing and storage tasks. The right choice between the two depends on the goals and objectives of the individual or corporate user.
Despite the rapid growth of ELT capabilities, some business participants still prefer ETL because this method is effective for some tasks in the field of creating, transferring, and processing small amounts of data.
The ETL pipeline has more security. Therefore, with its help, it is much better to work with sensitive information and guarantee full compliance with standards at the same time.
With any choice you make, you have the chance to achieve positive results using working integration methods with a productive data processing service.
If you don’t know what is the best choice for your task; if your biggest concern is ETL vs ELT, Renta‘s experts will definitely help you solve such a problem. They will recommend the most profitable and efficient data processing system, taking into account the specifics of your business.
