Building Batch Data Pipelines on GCP 日本語版

Google Cloud via Coursera

Go to Course: https://www.coursera.org/learn/batch-data-pipelines-gcp-jp

Introduction

### Course Review: Building Batch Data Pipelines on GCP 日本語版 **Overview:** The course "Building Batch Data Pipelines on GCP 日本語版" provides an extensive exploration of batch data pipelines within the Google Cloud Platform (GCP). It's designed to help learners understand the various frameworks of data extraction, loading, and transformation—commonly referred to as EL, ELT, and ETL—and when to implement each effectively. The course doesn't just focus on theoretical insights; it also dives deep into practical applications using GCP technologies like BigQuery, Dataproc, Cloud Data Fusion, and Dataflow. Through guided hands-on exercises using Qwiklabs, participants will engage in building components of data pipelines on Google Cloud, giving them a robust grounding in both the concepts and the practical skills necessary for data management. **Syllabus Breakdown:** 1. **Introduction:** The course kicks off with an introduction to the content and agenda, setting the stage for what learners can expect. This module effectively lays the groundwork for understanding the importance of data pipelines in today’s data-driven environments. 2. **Overview of Building Batch Data Pipelines:** In this section, the course examines various methods for data ingestion. Participants will explore the nuances of EL, ELT, and ETL, learning about the appropriate contexts for using each approach. This critical foundation helps in making informed decisions about data management strategies. 3. **Running Spark on Dataproc:** This module delves into the operational side of data processing by introducing Dataproc. It explains how to execute Hadoop jobs and leverage Cloud Storage for efficient data handling. Furthermore, optimization techniques for Dataproc jobs are also discussed, enhancing the learner's ability to maximize performance. 4. **Serverless Data Processing with Dataflow:** Learners are introduced to Dataflow, a serverless approach to data processing. This module guides participants on constructing data processing pipelines, which is essential for scalable and flexible data workflows. Understanding serverless architecture is increasingly important in modern data engineering. 5. **Managing Data Pipelines with Cloud Data Fusion and Cloud Composer:** The course also covers management techniques for data pipelines utilizing Cloud Data Fusion and Cloud Composer. This section is crucial for those looking to orchestrate complex data workflows across different services seamlessly. 6. **Course Summary:** The final module consolidates all that has been learned throughout the course, reinforcing key concepts and ensuring participants leave with a strong understanding of batch data pipelines on GCP. **Recommendation:** I highly recommend the "Building Batch Data Pipelines on GCP 日本語版" course for anyone interested in advancing their skills in data engineering and management. Whether you're a beginner aiming to grasp the fundamental concepts or an experienced professional looking to refine your skills in GCP, this course caters to a wide range of expertise levels. The hands-on approach through Qwiklabs is particularly beneficial, enabling learners to apply what they learn in a real-world context. Moreover, the delivery in Japanese makes it accessible to native speakers, which can enhance the learning experience significantly. If you aim to work with data at scale and utilize cloud technology, this course is an excellent investment in your professional development.

Syllabus

はじめに

このモジュールでは、コースおよびアジェンダについて紹介します

バッチデータ パイプラインの構築の概要

このモジュールでは、データ読み込みに関するさまざまな方法を確認します。EL、ELT、ETL について、また何をどのタイミングで使用するか

Dataproc での Spark の実行

このモジュールでは、Dataproc での Hadoop の実行、Cloud Storage の活用、Dataproc ジョブの最適化の方法を示します。

Dataflow を使用したサーバーレスのデータ処理

このモジュールでは、Dataflow を使用してデータ処理パイプラインを構築する方法について説明します。

Cloud Data Fusion と Cloud Composer を使用したデータ パイプラインの管理

このモジュールでは、Cloud Data Fusion と Cloud Composer を使用したデータ パイプラインの管理方法について説明します。

コースのまとめ

コースのまとめ