Elides Viloria
Menjadi anggota sejak 2025
Serverless Data Processing with Dataflow: Foundations
Earned Jul 18, 2025 EDT
Build Streaming Data Pipelines on Google Cloud
Earned Jul 16, 2025 EDT
Build Batch Data Pipelines on Google Cloud
Earned Jul 10, 2025 EDT
Build Data Lakes and Data Warehouses on Google Cloud
Earned Jun 30, 2025 EDT
Pengantar Data Engineering di Google Cloud
Earned Jun 17, 2025 EDT
Mengelola Model Data di Looker
Earned Jun 11, 2025 EDT
Membuat Model ML dengan BigQuery ML
Earned Jun 9, 2025 EDT
Mendapatkan Insight dari Data BigQuery
Earned Jun 4, 2025 EDT
Developing Data Models with LookML
Earned Jun 3, 2025 EDT
Analyzing and Visualizing Data in Looker
Earned Mei 28, 2025 EDT
Menyiapkan Data untuk Dasbor dan Laporan Looker
Earned Mei 23, 2025 EDT
Dasar-Dasar Analisis Data Google Cloud
Earned Mei 8, 2025 EDT
Build a Certification Study Guide: PDE Exam Prep
Earned Mei 7, 2025 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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In this course you will get hands-on in order to work through real-world challenges faced when building streaming data pipelines. The primary focus is on managing continuous, unbounded data with Google Cloud products.
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In this intermediate course, you will learn to design, build, and optimize robust batch data pipelines on Google Cloud. Moving beyond fundamental data handling, you will explore large-scale data transformations and efficient workflow orchestration, essential for timely business intelligence and critical reporting.
Get hands-on practice using Dataflow for Apache Beam and Serverless for Apache Spark (Dataproc Serverless) for implementation, and tackle crucial considerations for data quality, monitoring, and alerting to ensure pipeline reliability and operational excellence. A basic knowledge of data warehousing, ETL/ELT, SQL, Python, and Google Cloud concepts is recommended.
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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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Dalam kursus ini, Anda akan belajar tentang data engineering on Google Cloud, peran dan tanggung jawab data engineer, dan bagaimana hal tersebut terhubung dengan penawaran yang disediakan oleh Google Cloud. Anda juga akan mempelajari cara untuk mengatasi tantangan terkait data engineering.
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Selesaikan badge keahlian tingkat menengah Mengelola Model Data di Looker untuk menunjukkan keterampilan dalam hal berikut: mempertahankan performa project LookML; memanfaatkan SQL runner untuk validasi data; menerapkan praktik terbaik LookML; mengoptimalkan kueri dan laporan untuk performa; serta menerapkan tabel turunan persisten dan kebijakan penyimpanan ke dalam cache.
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Selesaikan badge keahlian tingkat menengah Membuat Model ML dengan BigQuery ML untuk menunjukkan keterampilan dalam hal berikut: membuat dan mengevaluasi model machine learning dengan BigQuery ML untuk membuat prediksi data.
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Selesaikan badge keahlian pengantar Mendapatkan Insight dari Data BigQuery untuk menunjukkan keterampilan dalam hal berikut: menulis kueri SQL, membuat kueri tabel publik, memuat sampel data ke dalam BigQuery, memecahkan masalah error sintaksis umum dengan validator kueri di BigQuery, dan membuat laporan di Data Studio dengan menghubungkannya ke data BigQuery.
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This course empowers you to develop scalable, performant LookML (Looker Modeling Language) models that provide your business users with the standardized, ready-to-use data that they need to answer their questions. Upon completing this course, you will be able to start building and maintaining LookML models to curate and manage data in your organization’s Looker instance.
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In this course, you learn how to do the kind of data exploration and analysis in Looker that would formerly be done primarily by SQL developers or analysts. Upon completion of this course, you will be able to leverage Looker's modern analytics platform to find and explore relevant content in your organization’s Looker instance, ask questions of your data, create new metrics as needed, and build and share visualizations and dashboards to facilitate data-driven decision making.
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Selesaikan badge keahlian pengantar Menyiapkan Data untuk Dasbor dan Laporan Looker untuk menunjukkan keterampilan dalam hal berikut: memfilter, mengurutkan, dan melakukan pivot pada data; menggabungkan hasil dari sejumlah Eksplorasi Looker; serta menggunakan fungsi dan operator untuk membangun dasbor dan laporan Looker untuk analisis dan visualisasi data.
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Pelajari cara mengubah angka mentah menjadi keputusan yang jelas dan berbasis data di Google Cloud. Melalui lab interaktif dan skenario praktis di dunia nyata, kursus tingkat pemula ini memandu Anda membuat kueri data di BigQuery, mengeksplorasi tren di Looker, dan membuat laporan interaktif yang dapat dibagikan di Data Studio.
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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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