Franco Corts-Romeo
Menjadi anggota sejak 2023
Silver League
6105 poin
Menyiapkan Data untuk Dasbor dan Laporan Looker
Earned Apr 7, 2023 EDT
Mengelola Model Data di Looker
Earned Apr 4, 2023 EDT
Membangun Objek LookML di Looker
Earned Apr 3, 2023 EDT
Data Catalog Fundamentals
Earned Apr 3, 2023 EDT
How Google Does Machine Learning
Earned Mar 29, 2023 EDT
Membuat Model ML dengan BigQuery ML
Earned Mar 26, 2023 EDT
Menerapkan Konsep LookML Lanjutan di Looker
Earned Mar 26, 2023 EDT
Mendapatkan Insight dari Data BigQuery
Earned Mar 22, 2023 EDT
Building No-Code Apps with AppSheet: Foundations
Earned Mar 22, 2023 EDT
Dasar-Dasar Google Cloud: Infrastruktur Inti
Earned Mar 18, 2023 EDT
Applying Machine Learning to your Data with Google Cloud
Earned Mar 17, 2023 EDT
Analyzing and Visualizing Data in Looker
Earned Mar 14, 2023 EDT
Creating New BigQuery Datasets and Visualizing Insights
Earned Mar 14, 2023 EDT
Achieving Advanced Insights with BigQuery
Earned Mar 14, 2023 EDT
Developing Data Models with LookML
Earned Mar 13, 2023 EDT
Exploring and Preparing your Data with BigQuery
Earned Mar 11, 2023 EST
Build a Certification Study Guide: PCSE Exam Prep
Earned Mar 10, 2023 EST
Rekayasa Data untuk Pembuatan Model Prediktif dengan BigQuery ML
Earned Feb 27, 2023 EST
Membangun Data Warehouse dengan BigQuery
Earned Feb 26, 2023 EST
Menyiapkan Data untuk ML API di Google Cloud
Earned Feb 25, 2023 EST
Serverless Data Processing with Dataflow: Operations
Earned Feb 23, 2023 EST
Serverless Data Processing with Dataflow: Develop Pipelines
Earned Feb 23, 2023 EST
Serverless Data Processing with Dataflow: Foundations
Earned Feb 14, 2023 EST
Smart Analytics, Machine Learning, and AI on Google Cloud
Earned Feb 13, 2023 EST
Build Streaming Data Pipelines on Google Cloud
Earned Feb 13, 2023 EST
Build Batch Data Pipelines on Google Cloud
Earned Feb 12, 2023 EST
Build Data Lakes and Data Warehouses on Google Cloud
Earned Feb 8, 2023 EST
[DEPRECATED] Google Cloud Big Data and Machine Learning Fundamentals
Earned Feb 8, 2023 EST
Build a Certification Study Guide: PDE Exam Prep
Earned Feb 5, 2023 EST
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.
Pelajari lebih lanjut
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 kursus badge keahlian pengantar Membangun Objek LookML di Looker untuk menunjukkan keterampilan dalam hal berikut: membuat dimensi dan ukuran, tabel turunan, serta tampilan baru; menetapkan filter ukuran dan berdasarkan persyaratan; memperbarui dimensi dan ukuran; membangun dan menyempurnakan Eksplorasi; menggabungkan tabel ke Eksplorasi yang ada; dan memutuskan objek yang akan dibuat berdasarkan persyaratan bisnis.
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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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This course explores what ML is and what problems it can solve. The course also discusses best practices for implementing machine learning. You’re introduced to Vertex AI, a unified platform to quickly build, train, and deploy AutoML machine learning models.
The course discusses the five phases of converting a candidate use case to be driven by machine learning, and why it’s important to not skip them. The course ends with recognizing the biases that ML can amplify and how to recognize them.
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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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Di kursus ini, Anda akan mendapatkan pengalaman langsung dalam menerapkan konsep LookML lanjutan di Looker. Anda akan mempelajari cara menggunakan Liquid untuk menyesuaikan dan membuat dimensi serta ukuran dinamis, membuat tabel turunan SQL dinamis dan tabel turunan native yang disesuaikan, serta menggunakan ekstensi untuk memodularisasi kode LookML Anda.
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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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In this course you will learn the fundamentals of no-code app development and recognize use cases for no-code apps. The course provides an overview of the AppSheet no-code app development platform and its capabilities. You learn how to create an app with data from spreadsheets, create the app’s user experience using AppSheet views and publish the app to end users.
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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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In this course, we define what machine learning is and how it can benefit your business. You'll see a few demos of ML in action and learn key ML terms like instances, features, and labels. In the interactive labs, you will practice invoking the pretrained ML APIs available as well as build your own Machine Learning models using just SQL with BigQuery ML.
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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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This is the second course in the Data to Insights course series. Here we will cover how to ingest new external datasets into BigQuery and visualize them with Looker Studio. We will also cover intermediate SQL concepts like multi-table JOINs and UNIONs which will allow you to analyze data across multiple data sources.
Note: Even if you have a background in SQL, there are BigQuery specifics (like handling query cache and table wildcards) that may be new to you.
After completing this course, enroll in the Achieving Advanced Insights with BigQuery course.
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The third course in this course series is Achieving Advanced Insights with BigQuery. Here we will build on your growing knowledge of SQL as we dive into advanced functions and how to break apart a complex query into manageable steps.
We will cover the internal architecture of BigQuery (column-based sharded storage) and advanced SQL topics like nested and repeated fields through the use of Arrays and Structs. Lastly we will dive into optimizing your queries for performance and how you can secure your data through authorized views.
After completing this course, enroll in the Applying Machine Learning to your Data with Google Cloud course.
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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, we see what the common challenges faced by data analysts are and how to solve them with the big data tools on Google Cloud. You’ll pick up some SQL along the way and become very familiar with using BigQuery and Dataprep to analyze and transform your datasets.
This is the first course of the From Data to Insights with Google Cloud series. After completing this course, enroll in the Creating New BigQuery Datasets and Visualizing Insights course.
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Learn how to use Gemini Notebook to create a personalized study guide for the Professional Cloud Security Engineer certification exam. You'll review Gemini Notebook features, create a notebook, and learn how to use a study guide to practice for the certification exam.
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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 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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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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