Abstract
Big companies that have many branches in different locations often have difficulty with analyzing transaction processes from each branch. The problem experienced by the company management is the rapid delivery of massive data provided by the branch to the head office so that the analysis process of the company's performance becomes slow and inaccurate. The results of this process used as a consideration in decision making which produce the right information if the data is complete and relevant. The right method of massive data collection is using the data warehouse approach. Data warehouse is a relational database designed to optimize queries in Online Analytical Processing (OLAP) from the transaction process of various data sources that can record any changes in data that occur so that the data becomes more structured. In applying the data collection, data warehouse has extracted, transform, and load (ETL) steps to read data from the Online Transaction Processing (OLTP) system, change the form of data through uniform data structures, and save to the final location in the data warehouse. This study provides an overview of the solution for implementing ETL that can work automatically or manually according to needs using the Python programming language so that it can facilitate the ETL process and can adjust to the conditions of the database in the company system.
Highlights
Big companies that have many branches in different locations often have difficulty with analyzing transaction processes from each branch
This study provides an overview of the solution for implementing ETL that can work automatically or manually according to needs using the Python programming language so that it can facilitate the ETL process and can adjust to the conditions of the database in the company system
Gambar 13 bagian (b) menuju database tujuan data warehouse (dwh_toko) merupakan tampilan sebuah diagram pie yang dengan menghasilkan waktu proses yang cepat memberikan informasi mengenai persentase data sehingga ketersediaan data pada data warehouse transaksi penjualan pada toko cabang xxx, seperti pada perusahaan tersebut selalu terjaga
Summary
Engine ini akan membaca isi file dan mempadankan data tersebut ke dalam struktur dimensional pada data warehouse yang sebelumnya sudah dirancang dengan menggunakan pendekatan Kimball (bottom -up). Sistem ETL yang dibangun dalam penelitian ini Pendekatan Kimball dalam pembangunan Data mengambil contoh kasus pada toko elektronik warehouse dimulai dengan membangun tabel yang komputer. Terdapat tahapan-tahapan dalam proses ETL terdiri dari tabel fakta dan dimensi. Tabel fakta mulai dari tahap insialisasi database sumber hingga berisikan metrik yang biasa data recordnya berasal dari menjadi data yang bisa dianalisis sesuai kebutuhan di tabel transaksi di relational database dan tabel dimensi dalam data warehouse. Proses ETL yang dibangun bisa dijalankan dengan 2 menghasilkan informasi dalam bentuk laporan sesuai cara yaitu cara manual, dimana proses ETL akan dengan kebutuhan user pimpinan. Laporan-laporan berjalan jika user administrator database menekan tersebut seperti: tombol proses yang terdapat pada interface engine ETL.
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