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Terapkan keterampilan Anda di Konsol Google Cloud

Armand Nana Simo

Menjadi anggota sejak 2023

Gold League

24205 poin
Deploying SAP on Google Cloud Earned Feb 1, 2024 EST
Pengantar Analisis Data di Google Cloud Earned Des 22, 2023 EST
Pengantar AI Generatif Earned Des 12, 2023 EST
Preparing for your Professional Data Engineer Journey Earned Okt 31, 2023 EDT
Applying Advanced LookML Concepts in Looker Earned Okt 27, 2023 EDT
Understanding LookML in Looker Earned Okt 27, 2023 EDT
Menyiapkan Data untuk Dasbor dan Laporan Looker Earned Okt 26, 2023 EDT
Analyzing and Visualizing Data in Looker Earned Okt 26, 2023 EDT
Rekayasa Data untuk Pembuatan Model Prediktif dengan BigQuery ML Earned Okt 25, 2023 EDT
Developing Data Models with LookML Earned Okt 24, 2023 EDT
Membangun Data Warehouse dengan BigQuery Earned Okt 22, 2023 EDT
Serverless Data Processing with Dataflow: Foundations Earned Okt 20, 2023 EDT
Build Streaming Data Pipelines on Google Cloud Earned Okt 19, 2023 EDT
Build Batch Data Pipelines on Google Cloud Earned Okt 13, 2023 EDT
Build Data Lakes and Data Warehouses on Google Cloud Earned Sep 29, 2023 EDT

This course provides a holistic experience of optimally configuring SAP on Google Cloud. Participants will learn to configure SAP on Google Cloud, and what best practices are, leaving the course with actionable experience to configure SAP on Google Cloud and run SAP workloads on Google Cloud.

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Dalam kursus tingkat pemula ini, Anda akan mempelajari alur kerja Analisis Data di Google Cloud dan alat yang dapat Anda gunakan untuk mengeksplorasi, menganalisis, dan memvisualisasikan data, serta membagikan temuan Anda dengan para pemangku kepentingan. Dengan menggunakan studi kasus serta lab interaktif, materi, dan kuis/demo, kursus ini akan mendemonstrasikan cara menghasilkan data bersih hingga visualisasi dan dasbor yang menghasilkan dampak dari set data mentah. Entah Anda sudah bekerja dengan data dan ingin mempelajari cara sukses di Google Cloud, atau ingin mengembangkan karier Anda, kursus ini akan membantu Anda memulai. Hampir semua orang yang melakukan atau menggunakan analisis data dalam pekerjaan mereka dapat mengambil manfaat dari kursus ini.

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Ini adalah kursus pengantar pembelajaran mikro yang bertujuan untuk mendefinisikan AI Generatif, cara penggunaannya, dan perbedaannya dari metode machine learning konvensional. Kursus ini juga mencakup Alat-alat Google yang dapat membantu Anda mengembangkan aplikasi AI Generatif Anda sendiri.

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This course helps learners create a study plan for the PDE (Professional Data Engineer) certification exam. Learners explore the breadth and scope of the domains covered in the exam. Learners assess their exam readiness and create their individual study plan.

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In this course, you will get hands-on experience applying advanced LookML concepts in Looker. You will learn how to use Liquid to customize and create dynamic dimensions and measures, create dynamic SQL derived tables and customized native derived tables, and use extends to modularize your LookML code.

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In this quest, you will get hands-on experience with LookML in Looker. You will learn how to write LookML code to create new dimensions and measures, create derived tables and join them to Explores, filter Explores, and define caching policies in LookML.

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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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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 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 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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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. Badge keahlian adalah badge digital eksklusif yang diberikan oleh Google Cloud sebagai pengakuan atas kemahiran Anda dalam menggunakan produk dan layanan Google Cloud serta menguji kemampuan Anda dalam menerapkan pengetahuan di lingkungan yang interaktif. Selesaikan kursus badge keahlian ini dan challenge lab penilaian akhir, untuk menerima badge keahlian yang dapat Anda bagikan dengan jaringan Anda.

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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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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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