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Henry Rodriguez

Menjadi anggota sejak 2021

Silver League

6800 poin
Serverless Data Processing with Dataflow: Foundations Earned Jul 15, 2022 EDT
Data Catalog Fundamentals Earned Jul 14, 2022 EDT
Membangun Data Warehouse dengan BigQuery Earned Jul 14, 2022 EDT
Build Data Lakes and Data Warehouses on Google Cloud Earned Jul 9, 2022 EDT
Rekayasa Data untuk Pembuatan Model Prediktif dengan BigQuery ML Earned Jul 5, 2022 EDT
[DEPRECATED] Data Engineering Earned Jul 5, 2022 EDT
Build a Certification Study Guide: PDE Exam Prep Earned Jul 2, 2022 EDT
Dasar-Dasar Google Cloud Earned Mei 2, 2021 EDT

This course is part 1 of a 3-course series on Serverless Data Processing with Dataflow. In this first course, we start with a refresher of what Apache Beam is and its relationship with Dataflow. Next, we talk about the Apache Beam vision and the benefits of the Beam Portability framework. The Beam Portability framework achieves the vision that a developer can use their favorite programming language with their preferred execution backend. We then show you how Dataflow allows you to separate compute and storage while saving money, and how identity, access, and management tools interact with your Dataflow pipelines. Lastly, we look at how to implement the right security model for your use case on Dataflow.

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Data Catalog is deprecated and will be discontinued on January 30, 2026. You can still complete this course if you want to. For steps to transition your Data Catalog users, workloads, and content to Dataplex Catalog, see Transition from Data Catalog to Dataplex Catalog (https://cloud.google.com/dataplex/docs/transition-to-dataplex-catalog). Data Catalog is a fully managed and scalable metadata management service that empowers organizations to quickly discover, understand, and manage all of their data. In this quest you will start small by learning how to search and tag data assets and metadata with Data Catalog. After learning how to build your own tag templates that map to BigQuery table data, you will learn how to build MySQL, PostgreSQL, and SQLServer to Data Catalog Connectors.

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Selesaikan badge keahlian tingkat menengah Membangun Data Warehouse dengan BigQuery untuk menunjukkan keterampilan Anda dalam hal berikut: menggabungkan data untuk membuat tabel baru, memecahkan masalah penggabungan, menambahkan data dengan union, membuat tabel berpartisi tanggal, serta menggunakan JSON, array, dan struct di BigQuery.

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While the traditional approaches of using data lakes and data warehouses can be effective, they have shortcomings, particularly in large enterprise environments. This course introduces the concept of a data lakehouse and the Google Cloud products used to create one. A lakehouse architecture uses open-standard data sources and combines the best features of data lakes and data warehouses, which addresses many of their shortcomings.

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Selesaikan badge keahlian tingkat menengah Rekayasa Data untuk Pembuatan Model Prediktif dengan BigQuery ML untuk menunjukkan keterampilan Anda dalam hal berikut: membangun pipeline transformasi data ke BigQuery dengan Dataprep by Trifacta; menggunakan Cloud Storage, Dataflow, dan BigQuery untuk membangun alur kerja ekstrak, transformasi, dan pemuatan (ETL); serta membangun model machine learning menggunakan BigQuery ML.

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This advanced-level quest is unique amongst the other catalog offerings. The labs have been curated to give IT professionals hands-on practice with topics and services that appear in the Google Cloud Certified Professional Data Engineer Certification. From Big Query, to Dataprep, to Cloud Composer, this quest is composed of specific labs that will put your Google Cloud data engineering knowledge to the test. Be aware that while practice with these labs will increase your skills and abilities, you will need other preparation, too. The exam is quite challenging and external studying, experience, and/or background in cloud data engineering is recommended. Looking for a hands on challenge lab to demonstrate your skills and validate your knowledge? On completing this quest, enroll in and finish the additional challenge lab at the end of the Engineer Data in the Google Cloud to receive an exclusive Google Cloud digital badge.

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Learn how to use Gemini Notebook to create a personalized study guide for the Professional Data Engineer certification exam. You'll review Gemini Notebook features, create a notebook in Gemini Notebook, and learn how to use a study guide to practice for a certification exam.

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Dalam kursus tingkat pemula ini, Anda akan mendapatkan praktik langsung dengan alat dan layanan dasar Google Cloud. Video opsional disediakan untuk memberikan konteks dan ulasan lebih lanjut mengenai konsep-konsep yang dibahas dalam lab ini. Dasar-Dasar Google Cloud adalah kursus pertama yang direkomendasikan bagi peserta kursus Google Cloud— Anda bisa mengikutinya dengan pengetahuan yang minim atau tanpa pengetahuan sama sekali tentang cloud, dan mendapatkan pengalaman praktis yang dapat diterapkan pada project Google Cloud pertama Anda setelah menyelesaikan kursus ini. Mulai dari menulis perintah Cloud Shell dan men-deploy virtual machine pertama Anda, hingga menjalankan aplikasi di Kubernetes Engine atau dengan load balancing, Dasar-Dasar Google Cloud merupakan pengantar utama untuk fitur dasar platform ini.

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