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CHIARELLO DAVIDE

Menjadi anggota sejak 2022

Serverless Data Processing with Dataflow: Operations Earned Des 11, 2023 EST
Rekayasa Data untuk Pembuatan Model Prediktif dengan BigQuery ML Earned Des 6, 2023 EST
Membangun Data Warehouse dengan BigQuery Earned Des 6, 2023 EST
Menyiapkan Data untuk ML API di Google Cloud Earned Des 6, 2023 EST
Serverless Data Processing with Dataflow: Develop Pipelines Earned Des 1, 2023 EST
Serverless Data Processing with Dataflow: Foundations Earned Nov 28, 2023 EST
Smart Analytics, Machine Learning, and AI on Google Cloud Earned Nov 28, 2023 EST
Build Streaming Data Pipelines on Google Cloud Earned Nov 28, 2023 EST
Build Batch Data Pipelines on Google Cloud Earned Nov 27, 2023 EST
Build Data Lakes and Data Warehouses on Google Cloud Earned Nov 24, 2023 EST
[DEPRECATED] Google Cloud Big Data and Machine Learning Fundamentals Earned Nov 23, 2023 EST
Build a Certification Study Guide: PDE Exam Prep Earned Nov 21, 2023 EST
Mengimplementasikan Alur Kerja DevOps di Google Cloud Earned Nov 20, 2023 EST
Membangun Infrastruktur dengan Terraform di Google Cloud Earned Nov 20, 2023 EST
DEPRECATED Cloud Operations and Service Mesh with Anthos Earned Nov 17, 2023 EST
Getting Started with Terraform for Google Cloud Earned Nov 15, 2023 EST
Logging and Monitoring in Google Cloud Earned Nov 13, 2023 EST
Infrastruktur Google Cloud yang Dapat Diandalkan: Desain dan Proses Earned Nov 8, 2023 EST
Developing a Google SRE Culture Earned Nov 3, 2023 EDT
Dasar-Dasar Google Cloud: Infrastruktur Inti Earned Nov 2, 2023 EDT
Memantau dan Membuat Log dengan Google Cloud Observability Earned Des 28, 2022 EST
Menyiapkan Lingkungan Pengembangan Aplikasi di Google Cloud Earned Des 27, 2022 EST

In the last installment of the Dataflow course series, we will introduce the components of the Dataflow operational model. We will examine tools and techniques for troubleshooting and optimizing pipeline performance. We will then review testing, deployment, and reliability best practices for Dataflow pipelines. We will conclude with a review of Templates, which makes it easy to scale Dataflow pipelines to organizations with hundreds of users. These lessons will help ensure that your data platform is stable and resilient to unanticipated circumstances.

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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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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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Selesaikan badge keahlian pengantar Menyiapkan Data untuk ML API di Google Cloud untuk menunjukkan keterampilan Anda dalam hal berikut: menghapus data dengan Dataprep by Trifacta, menjalankan pipeline data di Dataflow, membuat cluster dan menjalankan tugas Apache Spark di Managed Service for Apache Spark, dan memanggil beberapa ML API, termasuk Cloud Natural Language API, Google Cloud Speech-to-Text API, dan Video Intelligence API.

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In this second installment of the Dataflow course series, we are going to be diving deeper on developing pipelines using the Beam SDK. We start with a review of Apache Beam concepts. Next, we discuss processing streaming data using windows, watermarks and triggers. We then cover options for sources and sinks in your pipelines, schemas to express your structured data, and how to do stateful transformations using State and Timer APIs. We move onto reviewing best practices that help maximize your pipeline performance. Towards the end of the course, we introduce SQL and Dataframes to represent your business logic in Beam and how to iteratively develop pipelines using Beam notebooks.

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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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Incorporating machine learning into data pipelines increases the ability to extract insights from data. This course covers ways machine learning can be included in data pipelines on Google Cloud. For little to no customization, this course covers AutoML. For more tailored machine learning capabilities, this course introduces Notebooks and BigQuery machine learning (BigQuery ML). Also, this course covers how to productionalize machine learning solutions by using Vertex AI.

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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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This course introduces the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle. It explores the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud.

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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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Selesaikan badge keahlian Mengimplementasikan Alur Kerja DevOps di Google Cloud tingkat menengah untuk menunjukkan keterampilan dalam hal berikut: membuat dan mengelola repositori git, meluncurkan, mengelola, dan menskalakan deployment di Google Kubernetes Engine (GKE), serta merancang pipeline CI/CD yang mengotomatiskan pembangunan dan deployment image container ke GKE.

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Selesaikan badge keahlian Membangun Infrastruktur dengan Terraform di Google Cloud tingkat menengah untuk menunjukkan keterampilan dalam hal berikut: Prinsip Infrastruktur sebagai Kode (IaC) menggunakan Terraform, penyediaan dan pengelolaan resource Google Cloud dengan konfigurasi Terraform, pengelolaan status yang efektif (lokal dan jarak jauh), serta modularisasi kode Terraform agar dapat digunakan kembali dan diatur.

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Course two of the Architecting Hybrid Cloud with Anthos series prepares students to operate and observe Anthos environments. Through presentations and hands-on labs, participants explore adjusting existing clusters, setting up advanced traffic routing policies, securing communication across workloads, and observing clusters in Anthos. This course is a continuation of course one, Multi-Cluster, Multi-Cloud with Anthos, and assumes direct experience with the topics covered in that course.

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This course provides an introduction to using Terraform for Google Cloud. It enables learners to describe how Terraform can be used to implement infrastructure as code and to apply some of its key features and functionalities to create and manage Google Cloud infrastructure. Learners will get hands-on practice building and managing Google Cloud resources using Terraform.

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Welcome to the two-part course on Logging, Monitoring, and Observability in Google Cloud. The core operations tools in Google Cloud break down into two major categories. The operations-focused components and the application performance management tools. This course, Logging and Monitoring in Google Cloud, covers the operations-focused components including Logging, Monitoring, and Service Monitoring. After taking this course, it is suggested that you complete part 2, Observability in Google Cloud, to learn about the available application performance management tools.

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Kursus ini membekali peserta dengan keterampilan untuk membangun solusi yang sangat andal dan efisien di Google Cloud menggunakan pola desain yang telah terbukti. Kursus ini merupakan kelanjutan dari kursus Membangun dengan Google Compute Engine atau Membangun dengan Google Kubernetes Engine dan memberikan pengalaman interaktif dengan teknologi yang dibahas dalam kursus tersebut. Melalui kombinasi presentasi, aktivitas desain, dan lab interaktif, peserta akan mempelajari cara menentukan serta menyeimbangkan kebutuhan bisnis dan teknis untuk merancang deployment Google Cloud yang sangat andal, sangat tersedia, aman, dan hemat biaya.

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In many IT organizations, incentives are not aligned between developers, who strive for agility, and operators, who focus on stability. Site reliability engineering, or SRE, is how Google aligns incentives between development and operations and does mission-critical production support. Adoption of SRE cultural and technical practices can help improve collaboration between the business and IT. This course introduces key practices of Google SRE and the important role IT and business leaders play in the success of SRE organizational adoption.

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Dasar-Dasar Google Cloud: Infrastruktur Inti memperkenalkan konsep dan terminologi penting untuk bekerja dengan Google Cloud. Melalui video dan lab interaktif, kursus ini menyajikan dan membandingkan banyak layanan komputasi dan penyimpanan Google Cloud, bersama dengan resource penting dan alat pengelolaan kebijakan.

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Selesaikan badge keahlian pengantar Memantau dan Membuat Log dengan Google Cloud Observability untuk menunjukkan kemahiran dalam hal berikut: memantau virtual machine di Compute Engine, menggunakan Cloud Monitoring untuk pengawasan multi-project, memperluas kemampuan pemantauan dan logging ke Cloud Functions, membuat dan mengirimkan metrik aplikasi kustom, serta mengonfigurasi pemberitahuan Cloud Monitoring berdasarkan metrik kustom.

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Dapatkan badge keahlian dengan menyelesaikan kursus Menyiapkan Lingkungan Pengembangan Aplikasi di Google Cloud, yang memungkinkan Anda mempelajari cara membangun dan menghubungkan infrastruktur cloud yang berpusat pada penyimpanan menggunakan kemampuan dasar teknologi berikut: Cloud Storage, Identity and Access Management, Cloud Functions, dan Pub/Sub.

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