What is ETL – what is it now? The basics of ETL

What is an ETL tool?

ETL refers to the extraction (Extract), processing (Transform), and export (Load) of data stored in a company's critical systems, etc., to a target database, as well as the software that supports these series of processes. By the way, (Load) the target database, as well as the software that supports these series of processes.

Alcor will tell you about the tools that a specialist must master to get an ETL developer salary and specialization. ETL tools include tools for the visual design of the data flow using a graphical interface, data format conversion functions, etc.

It can be utilized for the following:

Simplification of data collaboration (promotion of loose coupling)

ETL can be used as a tool for data integration infrastructure

  • As shown in (Figure A) below, if each server communicates with the other, it is tough to understand the exchange of data. Still, as shown in (Figure B), by consolidating the data linkage to the data linkage infrastructure, data linkage can be centrally managed, resulting in a simple architecture.
  • In addition, as shown in (Figure B), consolidating data exchange into the data federation infrastructure reduces the burden of responding to failures and investigating the degree of impact of modification and development.
  • By separating data coordination and update processing from the application, a loosely coupled system structure can be realized without the spaghetti of servers communicating with each other.

Improvement of development productivity

  • Because ETL is developed using GUI, it is easy to develop if you have a certain level of programming knowledge and open system development knowledge.
  • It is easy to understand the contents of the process because the process can be visually grasped as a flow.

data maze

Why do you need ETL tools?

  • In order for companies to leverage the information (data) scattered throughout the company and get useful insights for management
  • The information (data) required must be consolidated and stored in one place.

The more types of data sources there are, the more specialized knowledge is required to program each data source.

This requires a tremendous amount of man-hours to develop and is a major obstacle. 

ETL tools have attracted attention as an effective means of eliminating this barrier

With ETL tools, the high level of knowledge required for each data source can be absorbed by the tool.

This has greatly contributed to the removal of barriers in the development process.

  • In addition, most ETL tools have an intuitive development interface (GUI), which is a major factor in reducing development effort.
  • This has had a significant impact on reducing development efforts.

Challenges facing ETL tools: is processing performance secondary?

As connectivity to various data sources and the convenience of ETL tools have increased

In fact, there have been challenges.

That is, "many ETL tools have had little or no expansion in terms of processing performance."

  • As you know, big data has become widespread, and data that was not used in a conventional business is now widely collected and analyzed.
  • It is becoming commonplace to include data that has not been used by traditional businesses as a target for analysis.
  • However, when large amounts of data must be processed and transformed within a limited timeframe and linked to a data warehouse, it is difficult to use a single ETL tool.
  • ETL tools alone are no longer sufficient for the task.

In result

  • Paying higher costs and expanding machine resources and licenses to improve data processing performance.
  • Allowing the database to perform machine-intensive processes, such as aggregation and merging.
  • The resulting results are further linked to the DWH using ETL tools.
  • Configure to explicitly split processing and perform multiple operations to efficiently use CPU resources.
  • As described above, a need arose to use methods that could never be described as optimal.

The catch copy is the smartest and super-fastest ETL too

 We will introduce three reasons why it is the smartest and super-fastest, including a comparison with common ETL tools.

Easy development screen with step tree format

In general ETL tools, you draw a flowchart on a blank white screen and call the functions you want to use from many functions.

  • While this allows for a high degree of freedom because you can design as you like, it also means that you have to call each function from scratch.
  • However, it is necessary to call each function from scratch.
  • There are cases in which it is difficult to know how to build a program.
  • Program quality is easily affected by the developer's experience.
  • The quality of the program may vary unless the developer has a certain level of programming experience.

Automatic Tuning of Processing with Smart ETL Optimizers

  • Typical ETL tools are designed to automatically tune the functionality used during process development and the
  • The processing algorithms used internally are correlated with the functions used during process development.

If you try to change the internally used processing program for tuning purposes, redevelopment using a different function or configuration change would be required.

This would result in tuning costs that would bounce back.

  • It automatically acquires information on the input data source to be processed and the specifications of the machine running the process.
  • Based on this information, the processing algorithm is dynamically determined, and the process is executed.
  • Developers can concentrate on the business logic they originally wanted to implement, leaving the processing that they needed to consider to the performance tool.
  • The advantage is that developers can concentrate on the business logic they originally wanted to implement.

Hadoop support

ETL is recognized as a process that occurs when creating a DWH that is primarily created for BI.

  • However, in recent years, there has been an increase in the amount of data that cannot be contained in a DWH or data that is too large for a DWH to process.
  • Unstructured data that cannot be processed by DWH, etc.
  • Data lakes are gaining attention as a platform for storing all kinds of data.
  • Data lakes also store unstructured data.
  • Hadoop, a distributed data processing platform, was chosen as the system infrastructure, not a DBMS.
  • Hadoop stores a variety of data, both structured and unstructured, but
  • The use of data is impossible without processing the data stored in Hadoop.

In addition, it is said that even IT departments have a high threshold for processing data in a Hadoop environment.