How to Carry out ETL Data Integration

This article was contributed by Alex Burak who works as a project manager for Visual Flow.

If you often have to deal with tons of customers' data, try ETL tools to get real benefits from them. Due to this, you can gain valuable information about your stakeholders, similar to what data scientists and data engineers would gather after years of coding with data science and data integration. Doesn't that sound like a win-win deal? Let's dive deeper and explore various etl data integration tools in detail.

Moreover, no-code ETL Tools can provide a simple solution to manage data in such a way as to provide more opportunities for your business growth. You do not need ETL developers to perform the ETL process, which makes everything much easier, more convenient, and cost-effective. Due to the many benefits associated with the no-code ETL tools, these tools have become a new reality in the business world, especially for companies with large amounts of data.

A few words about ETL

ETL (Extract, Transform, and Load) is an essential process in the data warehouse. In this process, data from multiple information sources are converted into one by integrating data to provide decision-makers with credible information to be relied on.

The low-code and no-code development industry is expected to reach $150+ billion by 2030. Annual revenue growth is explained by an increase in enterprises using no-code ETL technology. Over 75% of companies are expected to implement these tools and contribute to the growth of the data integration industry. 

The growth of the no-code sector is not specific to the IT industry; on the contrary, half of the sector's growth is expected to come from non-IT companies. And if you need some help with your projects, you can always rely on Visual Flow.

An introduction to each step of the process

  1. Data extraction

This step accesses the various data streams used by your company. All data is stored in a single repository, after which it can be moved between different programs and systems for further processing by data science.

2. Conversion

At this stage, data and the data warehouse need to be cleaned up and made effective for further use. Some basic rules in the conversion process include deduplication, verification, sorting, standardization, and data integration.

3. Uploading

The upload involves the appearance of data in a new location that can be easily used for the following processes such as reporting and decision-making. There are two main boot mechanisms: full loading and incremental loading. Regardless of the loading mechanism used, the result is to simplify data analysis.

What is no-code ETL?

No-code ETL means performing the whole extraction, conversion, and download process without any code. It forms a backend for data integration. No-code ETL tools are designed to automate the entire process, and users do not need to enter a single line of code to make it work efficiently. Businesses can use such tools without hiring ETL developers or data experts.

No-code Visual Flow ETL tools work in the cloud and often have a drag-and-drop interface to make it easier for the non-technical user to understand how to use it correctly. With these no-code ETL tools, your organization can easily create your March data or data warehouse that will ultimately influence strategy and decision-making.

Tool types no-code ETL

There are four main kinds of ETL data integration tools. In this section, we will briefly discuss each of these types:

  1. ETL enterprise software tools

These are tools developed and maintained by commercial organizations. As pioneers in the development of no-code ETL processes, these companies have already advanced the learning process and provided users of these tools with a graphical user interface, easy-to-use features, and other features which provide greater accessibility and ease of use.

However, the price charged for all these features is often higher than other no-code ETL solutions available on the market. Large organizations usually prefer such data integration tools with a constant flow of data and a high demand for information from data analysts within data pipelines.

2. ETL Open Source Tool

ETL's open-source tools are free to use, like any other open-source software. They can provide users with basic functionality, allowing your organization to find and study the source code. However, these tools opportunities and ease of use vary widely.

When choosing a manual ETL, you may need a full-time developer to modify the main code specifically for your organization if you do not want to rely only on basic functions. However, the open-source ETL provides more customization than any other ETL tool.

3. ETL cloud tool

With the development of cloud technologies, ETL tools are also available in this work. You can expect high latency, resource availability, and elasticity with cloud computing, which allows for scaling computing resources and meeting the organization's needs. But one of the problems with cloud data platforms is that they only work in a cloud server environment.

4. Custom ETL tool

The last type of ETL tool includes custom versions. They are developed by large companies using their teams and can be personalized according to the organization's requirements. Some of the computer languages that can help create such software include. SQL, Python, and Java.

The problem with these instruments is often their cost and excessive resource requirements. Creating, testing, and supporting these tools takes time and requires continuous process updating. Therefore, you should be prepared to allocate a specific budget for custom ETL tools.

Summing it up

No-code ETL Solutions can be helpful for your business because they can work without encoding. In a typical no-code ETL, you can use a simple user interface tool to create a data map to represent the path to the server. The server can execute the entire process automatically without requiring additional help.

Adding conversion rules is also one way ETL can help you. Data sets can be cleaned up, restructured, separated, or deleted to provide up-to-date and up-to-date information. Verifying the quality of the recovered data is also possible by applying some simple rules to the process.

You can schedule the entire ETL process, so it will not need to be done manually to obtain updated datasets and information for strategic decision-making. In addition, the visual, intuitive, and user-friendly interface ensures that everyone can use these ETL tools to save time, improve performance and get better results.

About the author

Alex Burak works as a project manager for Visual Flow. He is passionate about open source and data, and he believes that it helped him inspire their team and develop a product that simplifies the development of ETL on Apache Spark.