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Terapkan keterampilan Anda di Konsol Google Cloud

Mario Caesar

Menjadi anggota sejak 2022

Diamond League

10710 poin
Membangun Mesh Data dengan Dataplex Earned Mar 17, 2026 EDT
Bekerja dengan Model Gemini di BigQuery Earned Mar 15, 2026 EDT
Serverless Data Processing with Dataflow: Operations Earned Mar 10, 2026 EDT
Meningkatkan Produktivitas dengan Gemini in BigQuery Earned Mar 9, 2026 EDT
Serverless Data Processing with Dataflow: Develop Pipelines Earned Mar 2, 2026 EST
Pengantar Data Engineering di Google Cloud Earned Mar 2, 2026 EST
DEPRECATED Language, Speech, Text, & Translation with Google Cloud APIs Earned Nov 6, 2022 EST
Data Science on Google Cloud Earned Nov 4, 2022 EDT
Data Science on Google Cloud: Machine Learning Earned Nov 2, 2022 EDT
Melakukan Analisis Data Prediktif di BigQuery Earned Okt 30, 2022 EDT
DEPRECATED Applying BigQuery ML's Classification, Regression, and Demand Forecasting for Retail Applications Earned Okt 30, 2022 EDT
Machine Learning APIs Earned Okt 30, 2022 EDT
Dasar pengukuran: Data, ML, AI Earned Okt 28, 2022 EDT
Membuat Model ML dengan BigQuery ML Earned Okt 28, 2022 EDT
BigQuery untuk Machine Learning Earned Okt 28, 2022 EDT
Membangun dan Men-Deploy Solusi Machine Learning di Vertex AI Earned Okt 27, 2022 EDT
Menyiapkan Data untuk Dasbor dan Laporan Looker Earned Okt 26, 2022 EDT
Preparing for your Professional Data Engineer Journey Earned Okt 26, 2022 EDT
DEPRECATED BigQuery Basics for Data Analysts Earned Okt 20, 2022 EDT
Mendapatkan Insight dari Data BigQuery Earned Okt 19, 2022 EDT
[DEPRECATED] Data Engineering Earned Okt 19, 2022 EDT
Rekayasa Data untuk Pembuatan Model Prediktif dengan BigQuery ML Earned Okt 19, 2022 EDT
Membangun Data Warehouse dengan BigQuery Earned Okt 15, 2022 EDT
DEPRECATED BigQuery for Data Warehousing Earned Okt 15, 2022 EDT
Menyiapkan Data untuk ML API di Google Cloud Earned Okt 14, 2022 EDT
Serverless Data Processing with Dataflow: Foundations Earned Okt 13, 2022 EDT
Build Streaming Data Pipelines on Google Cloud Earned Okt 12, 2022 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud Earned Okt 9, 2022 EDT
Build Batch Data Pipelines on Google Cloud Earned Okt 1, 2022 EDT
Build Data Lakes and Data Warehouses on Google Cloud Earned Sep 24, 2022 EDT

Selesaikan badge keahlian pengantar Membangun Mesh Data dengan Dataplex untuk menunjukkan keterampilan dalam hal berikut: membuat mesh data dengan Dataplex untuk memfasilitasi keamanan, tata kelola, dan penemuan data di Google Cloud. Anda akan berlatih dan menguji keterampilan Anda dalam memberikan tag pada aset, menetapkan peran IAM, dan menilai kualitas data di Dataplex.

Pelajari lebih lanjut

Kursus ini menunjukkan cara menggunakan model AI/ML untuk tugas-tugas AI generatif di BigQuery. Melalui kasus penggunaan praktis yang melibatkan pengelolaan hubungan pelanggan (CRM), Anda akan mempelajari alur kerja pemecahan masalah bisnis dengan model Gemini. Untuk memudahkan pemahaman, kursus ini juga menyediakan panduan langkah demi langkah melalui solusi coding menggunakan kueri SQL dan notebook Python.

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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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Kursus ini mengeksplorasi Gemini in BigQuery, yakni paket fitur yang didukung AI untuk membantu alur kerja data ke AI. Paket fitur ini meliputi eksplorasi dan persiapan data, pembuatan kode dan pemecahan masalah, serta penemuan dan visualisasi alur kerja. Melalui penjelasan konseptual, kasus penggunaan praktis, dan lab interaktif, kursus ini akan membantu para praktisi data dalam meningkatkan produktivitas mereka dan mempercepat pipeline pengembangan.

Pelajari lebih lanjut

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.

Pelajari lebih lanjut

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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In this quest you will use a collection of Google APIs that are all related to language, and speech. You will use the Speech-to-Text API to transcribe an audio file into a text file, the Cloud Translation API to translate from one language to another, the Cloud Translation API to detect what language is being used and translate to a different language, the Natural Language API to classify text and analyze sentiment, and create synthetic speech.

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This is the first of two Quests of hands-on labs is derived from the exercises from the book Data Science on Google Cloud Platform, 2nd Edition by Valliappa Lakshmanan, published by O'Reilly Media, Inc. In this first Quest, covering up through chapter 8, you are given the opportunity to practice all aspects of ingestion, preparation, processing, querying, exploring and visualizing data sets using Google Cloud tools and services.

Pelajari lebih lanjut

This is the second of two Quests of hands-on labs derived from the exercises from the book Data Science on Google Cloud Platform, 2nd Edition by Valliappa Lakshmanan, published by O'Reilly Media, Inc. In this second Quest, covering chapter 9 through the end of the book, you extend the skills practiced in the first Quest, and run full-fledged machine learning jobs with state-of-the-art tools and real-world data sets, all using Google Cloud tools and services.

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Selesaikan kursus badge keahlian tingkat menengah Melakukan Analisis Data Prediktif di BigQuery untuk mendemonstrasikan keterampilan dalam hal berikut: membuat set data di BigQuery dengan mengimpor file CSV dan JSON; memanfaatkan kemampuan BigQuery dengan konsep analitik SQL yang canggih, termasuk menggunakan BigQuery ML untuk melatih model prediksi gol pada data peristiwa sepak bola dan mengevaluasi kehebatan gol Piala Dunia.

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In this course you will learn how to use several BigQuery ML features to improve retail use cases. Predict the demand for bike rentals in NYC with demand forecasting, and see how to use BigQuery ML for a classification task that predicts the likelihood of a website visitor making a purchase.

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It's no secret that machine learning is one of the fastest growing fields in tech, and Google Cloud has been instrumental in furthering its development. With a host of APIs, Google Cloud has a tool for just about any machine learning job. In this advanced-level course, you will get hands-on practice with machine learning APIs by taking labs like Detect Labels, Faces, and Landmarks in Images with the Cloud Vision API. Looking for a hands-on challenge lab to demonstrate your skills and validate your knowledge? Enroll in and finish the additional challenge lab at the end of this quest to receive an exclusive Google Cloud digital badge.

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Big data, machine learning, dan kecerdasan buatan menjadi topik komputasi yang populer saat ini, tetapi bidang tersebut sangat terspesialisasi dan materi pengantarnya sulit diperoleh. Untungnya, Google Cloud menyediakan layanan yang mudah digunakan dalam bidang tersebut, dan melalui kursus tingkat pengantar ini, Anda dapat mengambil langkah pertama dengan alat seperti BigQuery, Cloud Speech API, dan Video Intelligence.

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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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Ingin membangun model ML dalam hitungan menit, bukan jam, hanya dengan menggunakan SQL? BigQuery ML memperluas akses machine learning dengan memungkinkan analis data membuat, melatih, mengevaluasi, dan memprediksi sesuatu dengan model machine learning menggunakan alat serta keterampilan SQL yang ada. Dalam rangkaian lab ini, Anda akan bereksperimen dengan beragam jenis model dan mempelajari ciri-ciri model yang baik.

Pelajari lebih lanjut

Dapatkan badge keahlian tingkat menengah dengan menyelesaikan kursus Membangun dan Men-Deploy Solusi Machine Learning di Vertex AI, tempat Anda akan belajar cara menggunakan platform Vertex AI Google Cloud, AutoML, dan layanan pelatihan kustom untuk melatih, mengevaluasi, menyesuaikan, menjelaskan, serta men-deploy model machine learning. Kursus badge keahlian ini diperuntukkan bagi Data Scientist dan Engineer Machine Learning profesional. 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 praktis yang interaktif. Selesaikan Badge keahlian ini, dan challenge lab penilaian akhir, untuk menerima badge digital yang dapat Anda bagikan ke jaringan Anda.

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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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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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Want to scale your data analysis efforts without managing database hardware? Learn the best practices for querying and getting insights from your data warehouse with this interactive series of BigQuery labs. BigQuery is Google's fully managed, NoOps, low cost analytics database. With BigQuery you can query terabytes and terabytes of data without having any infrastructure to manage or needing a database administrator. BigQuery uses SQL and can take advantage of the pay-as-you-go model. BigQuery allows you to focus on analyzing data to find meaningful insights.

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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 Looker Studio dengan menghubungkannya ke data BigQuery.

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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.

Pelajari lebih lanjut

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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Looking to build or optimize your data warehouse? Learn best practices to Extract, Transform, and Load your data into Google Cloud with BigQuery. In this series of interactive labs you will create and optimize your own data warehouse using a variety of large-scale BigQuery public datasets. BigQuery is Google's fully managed, NoOps, low cost analytics database. With BigQuery you can query terabytes and terabytes of data without having any infrastructure to manage or needing a database administrator. BigQuery uses SQL and can take advantage of the pay-as-you-go model. BigQuery allows you to focus on analyzing data to find meaningful insights. 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 this quest to receive an exclusive Google Cloud digital badge.

Pelajari lebih lanjut

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 Dataproc, dan memanggil beberapa ML API, termasuk Cloud Natural Language API, Google Cloud Speech-to-Text API, dan Video Intelligence API.

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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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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 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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