Final Project Practicum IV

In this practicum, students are required to perform Extract, Transform & Load (ETL) on the provided dataset as part of the final stages of the Practicum Final Project.

In the initial process of data analysis, data scientists must follow several basic steps to ensure the accuracy of the output. One of the methods is Extract, Transform, and Load, commonly referred to as ETL.

Since its emergence in the 1970s, ETL has drawn the attention of companies for storing their databases. This method is considered a standard approach for obtaining a substantial amount of data as it efficiently sifts through databases, making them more integrated. What is ETL? How does this method become crucial in business? The following provides an overview.

WHAT IS ETL?

ETL stands for Extract, Transform, Load. As the name suggests, Extract, Transform, Load is a process that involves extracting data from various source systems, followed by transforming the data (calculations, rounding, etc.) and ultimately loading it into a data warehouse system. Initially, mixed data is processed into a single, consistent dataset, making it easy for future analysis when needed.

ETL serves as the foundation for data analytics and machine learning. Through various configurations, ETL can clean and organize data as desired. Monthly report data can be easily analyzed.

WHY IS ETL IMPORTANT?

There are several reasons why Extract, Transform, Load is crucial to implement in an organization or company. Some of these reasons include:

  • ETL helps companies analyze business data as a reference for critical decision-making.
  • Transactional databases generally cannot answer business problems that are easily addressed by ETL.
  • ETL provides a method for moving data from various sources into a single data warehouse.
  • When the data source changes, the data warehouse will be automatically updated.
  • A well-designed and documented Extract, Transform, Load system significantly influences the success rate of a data warehouse project.
  • ETL can verify transformation rules, aggregations, and calculations within a dataset.
  • The Extract, Transform, Load process allows for the presence of data comparison examples between the source and target systems.
  • The Extract, Transform, Load process can perform transformations on complex data and may require additional storage space for data.
  • Thanks to the automation of data transfer programming, Extract, Transform, Load can increase the productivity of data professionals without requiring technical expertise to write code and scripts.

PROCESSES IN ETL

To understand how ETL works easily, it’s essential to grasp what happens in each step of the ETL process.

  1. EXTRACT
    During the extraction process, raw data is copied or exported from the source location to the staging area. The data management team can extract data from various sources, both structured and unstructured. These sources include SQL/NoSQL servers, CRM and ERP systems, flat files, emails, and websites.

  2. TRANSFORM
    Data that has entered the staging area then undergoes a series of processes. Here, the data is transformed and consolidated before being analyzed according to the desired output type. This phase includes:

    • Filtering, cleansing, de-duplication, validation, and authentication of data.
    • Calculations, translations, or inferences based on raw data.
    • Data audit execution to ensure quality and relevance.
    • Deletion, encryption, or data protection required by industry or government regulations.
    • Formatting data into tables or combined tables to align with the target data warehouse.

  3. LOAD
    In the final step, the transformed data is moved from the staging area to the target data warehouse. Usually, this step involves the initial loading of the entire data, followed by periodic loading of data changes, refreshing data to delete or replace data in the warehouse.

    Data loading processes like these occur automatically in organizations or companies that use ETL. Because it involves comprehensive data, ETL is typically performed outside of working hours when system traffic and data warehouse usage are at their lowest.

ETL PIPELINE

ETL pipeline refers to the process of extracting, transforming, and loading data into a database warehouse as one type of data pipeline. A data pipeline is a more general term, where data does not necessarily involve data transformation or even loading into the target database.

CONCLUSION

That is the understanding of ETL and its process in implementing data integration in a company. By understanding the concept of ETL, it is hoped that data scientists can utilize it to create more optimal outputs. To delve deeper into ETL and its application, you can join data science classes from Algoritma Data Science School. Various classes are available and can be chosen based on needs and expertise levels.

MODULE

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