Often, businesses have a lot of data from different places and want to bring it to one central location. And that is where ETL can be beneficial and valuable.
ETL makes it easier for a business to analyze data as information. It also improves data accuracy for companies that must obey specific regulations. And it saves time by omitting the need for heavy coding through automation.
What is ETL?
ETL stands for Extract, Transform and Load. And it is an essential part of machine learning and data analytics.
ETL allows data integration for companies through a specific process. The process works by gathering data from many places. Then change the data, so it is helpful for any business needs. Finally, putting all the transformed data into a single location.
And there are three main steps to this process:
Data Extraction: The first step to ETL is data extraction. This step is about moving data from different sources into a new area. There are a variety of sources where this data can come from:
-Servers
-Web Pages
-Flat Files
-Mobile Devices and Apps
-Existing Databases
The different places listed above where the raw data is from are the source locations.
Data Transformation: Transformation is the most critical part of ETL. It is about changing the raw data into new formats after extraction.
And the purpose is to pass data that match the organization’s needs into its storage system. The organization’s analytical or operational requirements determine the change in the data.
Here are the processes that may happen at this step:
- Data Cleansing: Any inconsistencies or inaccuracies in the data need removal.
- Standardization: Apply formatting to data and put the data in the same format.
- Duplication: removing any duplications or redundant data that might exist.
- Other Tasks: Running new calculations, summarizing the data, and noting anomalies in the raw data.
Data Loading: The last step is putting the information into a new destination. The data may be to a data warehouse or data lake. The information would then be available for other users or departments to use.
Why Extract, Transform, Load Is Important?
The application of ETL can help give businesses insights in less time. But, beyond that, it also enhances the information from raw data through transformation.
Transforming the data and loading it into intelligence applications can reveal new trends. As well as improve the health outcomes by changing specific diagnostics.
The application of data from sales can help companies better forecast customer demand. Likewise, putting patient data into a system can allow doctors to make better decisions.
Integrating the data can provide a report that gives the user a clearer big picture of a problem. ETL can also give companies better ideas of their processes.
What Is Data Lake vs. Data Warehouse?
The difference is that the data lake is raw data and the data warehouse has structured data for a purpose.
The data in a data lake still needs processing. In contrast, the data from a data warehouse comes after refining and filtering.
Also, the processed data in a data warehouse requires little storage space. As a result, it is easy for users to analyze the information and provides business insights. And generally, the information is there to answer a question or for a specific purpose.
Data and business analysts often work with the information from a data warehouse. The warehouse contains processed information that fits their exact needs.
The unrefined raw data from the data lake would need more extensive storage space. Data lake stores a large amount of structured, semi-structured, or unstructured data.
What Is Data Pipeline in ETL?
ETL pipeline and data pipeline are different, and ETL pipeline is a type of data pipeline.
A data pipeline is a set of actions to move data from one source to a destination. But data pipeline doesn’t always transform the data. Moreover, there are times that the data is not changed or done after loading to a destination.
Sometimes, the process continues after loading the information. And there may still be new processes triggered after loading the data.
A data pipeline is a broad process of transporting data from one location to another. Automating many manual steps involving transformation and optimization of data loads.
In contrast, an ETL pipeline has the transformation step as part of its process. And it ends when the transformed data loads into a data warehouse. Then, the data in the data warehouse is ready for analyzing and driving business strategy.
Often, the ETL pipeline loads information in batches on a regular schedule. Or it could be during a set time of the day when traffic in the system is low or loading a specific amount of time in a day.
Meanwhile, it is a continuous updating process for data pipelines. It moves data from a source system to a target repository as a real-time process.
What Are ETL and SQL?
The difference between ETL and SQL is that ETL is a process for preparing data for a company. Meanwhile, SQL or Structured Query Language is a computer programming language. SQL helps with working with parts of the data, and ETL gets the data ready for interpretation.
ETL is a process for shaping raw data from sources for an organization’s needs. And then put into a destination, like a data warehouse, for reporting or exploration.
Meanwhile, SQL is a language to perform operations on the data in database systems. It is a standard language that can help programmers access and manipulate data.
You can input commands to manipulate specific rows or tables of data or extract parts of a database. With the use of SQL, it is possible to communicate with database systems.
What Is an ETL Job?
An ETL job is to be an ETL developer, which has many responsibilities that deal with a data system.
One of the main priorities for an ETL developer is the extract, transform, and load process.
The company may also task the developer with creating a data warehouse. And developer needs to make the data warehouse to the needs of the enterprise for storage.
Other tasks include ensuring that the system is running. And developers are responsible for fixing any problems that come up. Developers will also need to be able to build a process to connect and transfer data between systems.
What Are ETL Tools?
The purpose of ETL tools is to help make data management easier by streamlining the ETL process. It helps simplify the process and make insightful information for organizations.
They can extract from sources, transform data, and load it into a data warehouse. ETL tools help replace manual manipulation of the process. With ETL tools, developers save time by removing the need to use SQL for every task.
Doing everything by hand can lead to huge errors when not careful. A slight mistake can lead to significant error probabilities in a calculation.
By providing a system approach, the tools can help improve the data quality. Streamline the task of cleansing, removing duplicates, and other labor.
