This course explores the benefits of using Vertex AI Feature Store, how to improve the accuracy of ML models, and how to find which data columns make the most useful features. This course also includes content and labs on feature engineering using BigQuery ML, Keras, and TensorFlow.
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As the use of enterprise Artificial Intelligence and Machine Learning continues to grow, so too does the importance of building it responsibly. A challenge for many is that talking about responsible AI can be easier than putting it into practice. If you’re interested in learning how to operationalize responsible AI in your organization, this course is for you.
In this course, you will learn how Google Cloud does this today, together with best practices and lessons learned, to serve as a framework for you to build your own responsible AI approach.
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Dapatkan badge keahlian dengan menyelesaikan kursus Introduction to Generative AI, Introduction to Large Language Models, dan Introduction to Responsible AI. Dengan berhasil menyelesaikan kuis akhir, Anda membuktikan pemahaman Anda tentang konsep dasar AI generatif.
Badge keahlian adalah badge digital yang diberikan oleh Google Cloud sebagai pengakuan atas pengetahuan Anda tentang produk dan layanan Google Cloud. Pamerkan badge keahlian Anda dengan menampilkan profil Anda kepada publik dan menambahkannya ke profil media sosial Anda.
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Ini adalah kursus pengantar pembelajaran mikro yang dimaksudkan untuk menjelaskan responsible AI, alasan pentingnya responsible AI, dan cara Google mengimplementasikan responsible AI dalam produknya. Kursus ini juga memperkenalkan 7 prinsip AI Google.
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Data pipelines typically fall under one of the Extract and Load (EL), Extract, Load and Transform (ELT) or Extract, Transform and Load (ETL) paradigms. This course describes which paradigm should be used and when for batch data. Furthermore, this course covers several technologies on Google Cloud for data transformation including BigQuery, executing Spark on Dataproc, pipeline graphs in Cloud Data Fusion and serverless data processing with Dataflow. Learners get hands-on experience building data pipeline components on Google Cloud using Qwiklabs.
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Complete the introductory Build a Data Mesh with Dataplex skill badge to demonstrate skills in the following: building a data mesh with Dataplex to facilitate data security, governance, and discovery on Google Cloud. You practice and test your skills in tagging assets, assigning IAM roles, and assessing data quality in Dataplex. A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete this Skill Badge, and the final assessment challenge lab, to receive a digital badge that you can share with your network.
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Jika Anda adalah developer cloud pemula yang menginginkan praktik langsung di luar Getting Started - Create and Manage Cloud Resources, artinya quest ini cocok untuk Anda. Anda akan mendapatkan pengalaman praktik melalui lab yang mendalami Cloud Storage dan layanan aplikasi utama lainnya, seperti Stackdriver dan Cloud Functions. Dengan mengambil quest ini, Anda dapat mengembangkan kemampuan berharga yang dapat diterapkan di semua inisiatif Google Cloud. Selesaikan quest ini, termasuk challenge lab di akhir quest, untuk menerima badge digital eksklusif dari Google Cloud. Tersedia video berdurasi 1 menit yang akan membantu Anda memahami konsep utama lab-lab ini.
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Complete the intermediate Build a Data Warehouse with BigQuery skill badge to demonstrate skills in the following: joining data to create new tables, troubleshooting joins, appending data with unions, creating date-partitioned tables, and working with JSON, arrays, and structs in BigQuery. A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete the skill badge course, and final assessment challenge lab, to receive a digital badge that you can share with your network. For practice with BigQuery fundamentals (including working with the console and command line), complete the course titled BigQuery Basics for Data Analysts.
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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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MongoDB Atlas provided customers a fully managed, database-as-a-service on Google’s data cloud that is unmatched in speed, scale, and security—all with AI built in. Modern database systems, including MongoDB, have been a big step forward—giving businesses a more flexible, scalable, and developer-friendly alternative to legacy relational databases. But there is an even bigger payoff with a solution such as MongoDB Atlas a fully managed, database-as-a-service (DBaaS) offering. It is an approach that gives businesses all of the advantages of a modern, scalable, highly available database, while freeing IT to focus on high-value activities.
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Earn a skill badge by completing the Tag and Discover BigLake Data quest, where you use BigQuery, BigLake, and Data Catalog within Dataplex to create, tag, and discover BigLake tables. A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete this Skill Badge, and the final assessment challenge lab, to receive a digital badge that you can share with your network.
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In this course you will learn about Cloud Spanner. You will get an introduction to Cloud Spanner, contrasting it with other Database products to understand when and how to use Spanner to solve your relational database needs at scale. You will learn how to create and manage Spanner databases using various tools on Google Cloud, learn to optimize relational schemas with Spanner’s distributed database model in mind, interact with your Spanner databases using the Spanner APIs, integrate Spanner with your applications, and learn how to use other Google tools for administering Spanner databases and managing your data.
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This learning pack is designed to have hands-on experience on Google Cloud data solutions.
Goals
Plan, execute, test, and monitor simple and complex enterprise database migrations to Google Cloud
Choose an appropriate Google Cloud database, migrate SQL Server databases and run Oracle databases on Google Cloud bare metal
Recognize and overcome the challenges of moving data to prevent data loss, preserve data integrity, and minimize downtime
Evaluate on-premises database architectures and plan migrations to make the business case for moving databases to Google Cloud
You can find all of our technical learning packs on go/techlearningpacks and industry learning packs on go/industrylearningpacks.
Brought to you by the CLS Tech Specialization Team (gcc-enablement-tech@). Share your request/feedback on go/learningpacks-feedback!
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The Google Rapid Migration Program (RaMP) is a holistic, end-to-end migration program that helps customers & partners leverage expertise and best practices, lower risk, control costs, and simplify their path to cloud success. This course will give an overview of the program and some of the tools and best practices available to support customer migrations
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Cloud technology on its own only provides a fraction of the true value to a business; When combined with data–lots and lots of it–it has the power to truly unlock value and create new experiences for customers.
In this course, you'll learn what data is, historical ways companies have used it to make decisions, and why it is so critical for machine learning. This course also introduces learners to technical concepts such as structured and unstructured data. database, data warehouse, and data lakes. It then covers the most common and fastest growing Google Cloud products around data.
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Kursus ini memperkenalkan Generative AI Studio, yaitu produk di Vertex AI yang dapat membantu Anda membuat prototipe dan menyesuaikan model AI generatif, sehingga Anda dapat menggunakan kemampuan model tersebut dalam aplikasi Anda. Dalam kursus ini, Anda akan mempelajari apa itu Generative AI Studio, berbagai fitur dan opsinya, serta cara menggunakannya, dengan mengkaji langkah demi langkah yang ditunjukkan dalam demo produk tersebut. Di bagian akhir, terdapat lab praktik untuk menerapkan apa yang telah Anda pelajari dan kuis untuk menguji pengetahuan Anda.
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Kursus ini menjelaskan cara membuat model keterangan gambar menggunakan deep learning. Anda akan belajar tentang berbagai komponen model keterangan gambar, seperti encoder dan decoder, serta cara melatih dan mengevaluasi model. Pada akhir kursus ini, Anda akan dapat membuat model keterangan gambar Anda sendiri dan menggunakannya untuk menghasilkan teks bagi gambar.
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Kursus ini memberi Anda sinopsis tentang arsitektur encoder-decoder, yang merupakan arsitektur machine learning yang canggih dan umum untuk tugas urutan-ke-urutan seperti terjemahan mesin, ringkasan teks, dan tanya jawab. Anda akan belajar tentang komponen utama arsitektur encoder-decoder serta cara melatih dan menyalurkan model ini. Dalam panduan lab yang sesuai, Anda akan membuat kode pada penerapan simpel arsitektur encoder-decoder di TensorFlow untuk pembuatan puisi dari awal.
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Kursus ini memperkenalkan model difusi, yaitu kelompok model machine learning yang belakangan ini menunjukkan potensinya dalam ranah pembuatan gambar. Model difusi mengambil inspirasi dari fisika, khususnya termodinamika. Dalam beberapa tahun terakhir, model difusi menjadi populer baik di dunia industri maupun penelitian. Model difusi mendasari banyak alat dan model pembuatan gambar yang canggih di Google Cloud. Kursus ini memperkenalkan Anda pada teori yang melandasi model difusi dan cara melatih serta men-deploy-nya di Vertex AI.
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Earn a skill badge by completing the Get Started with Dataplex quest, where you use Dataplex to start creating a data mesh on Google Cloud. A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete this Skill Badge, and the final assessment challenge lab, to receive a digital badge that you can share with your network.
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The Generative AI Explorer - Vertex Quest is a collection of labs on how to use Generative AI on Google Cloud. Through the labs, you will learn about how to use the models in the Vertex AI PaLM API family, including text-bison, chat-bison, and textembedding-gecko. You will also learn about prompt design, best practices, and how it can be used for ideation, text classification, text extraction, text summarization, and more. You will also learn how to tune a foundation model by training it via Vertex AI custom training and deploy it to a Vertex AI endpoint.
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Kursus ini memperkenalkan Anda pada arsitektur Transformer dan model Representasi Encoder Dua Arah dari Transformer (Bidirectional Encoder Representations from Transformers atau BERT). Anda akan belajar tentang komponen utama arsitektur Transformer, seperti mekanisme self-attention, dan cara penggunaannya untuk membangun model BERT. Anda juga akan belajar tentang berbagai tugas yang dapat memanfaatkan BERT, seperti klasifikasi teks, menjawab pertanyaan, dan inferensi natural language. Kursus ini diperkirakan memakan waktu sekitar 45 menit untuk menyelesaikannya.
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Dalam kursus ini Anda akan diperkenalkan dengan mekanisme atensi, yakni teknik efektif yang membuat jaringan neural berfokus pada bagian tertentu urutan input. Anda akan mempelajari cara kerja atensi, cara penggunaannya untuk meningkatkan performa berbagai tugas machine learning, termasuk terjemahan mesin, peringkasan teks, dan menjawab pertanyaan.
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Ini adalah kursus pengantar pembelajaran mikro yang membahas definisi model bahasa besar (LLM), kasus penggunaannya, dan cara menggunakan prompt tuning untuk meningkatkan performa LLM. Kursus ini juga membahas beberapa alat Google yang dapat membantu Anda mengembangkan aplikasi Gen AI Anda sendiri.
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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 learner pack 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.
Goals
Identify the purpose and value of Google Cloud Data Platform
Learn about batch and streaming data pipelines
Build data lake and data warehouse
You can find all of our technical learning packs on go/techlearningpacks and industry learning packs on go/industrylearningpacks.
Brought to you by the CLS Tech Specialization Team (gcc-enablement-tech@). Share your request/feedback on go/learningpacks-feedback!
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This learning pack is intended to give architects, engineers, and developers the skills required to help enterprise customers architect, plan, execute, and test database migration projects. This course covers how to move on-premises, enterprise databases like SQL Server to Google Cloud (Compute Engine and Cloud SQL) and Oracle to Google Cloud bare metal.
Goals
Plan, execute, test, and monitor simple and complex enterprise database migrations to Google Cloud
Choose an appropriate Google Cloud database, migrate SQL Server databases and run Oracle databases on Google Cloud bare metal
Recognize and overcome the challenges of moving data to prevent data loss, preserve data integrity, and minimize downtime
Evaluate on-premises database architectures and plan migrations to make the business case for moving databases to Google Cloud
You can find all of our technical learning packs on go/techlearningpacks and industry learning packs on go/industrylearningpacks.
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Complete the introductory Create and Manage Cloud SQL for PostgreSQL Instances skill badge to demonstrate skills in the following: migrating, configuring, and managing Cloud SQL for PostgreSQL instances and databases. A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete this skill badge course, and the final assessment challenge lab, to receive a skill badge that you can share with your network.
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Complete the introductory Create and Manage AlloyDB Instances skill badge to demonstrate skills in the following: performing core AlloyDB operations and tasks, migrating to AlloyDB from PostgreSQL, administering an AlloyDB database, and accelerating analytical queries using the AlloyDB Columnar Engine. A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete this skill badge course, and the final assessment challenge lab, to receive a skill badge that you can share with your network.
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It’s no secret today that data is growing rapidly and considered the most critical asset of any organization. NetApp and Google Cloud play an instrumental role in enabling you to optimally store, protect and govern your data. With NetApp Cloud Manager and NetApp Cloud Volumes ONTAP data storage technology that utilizes Google Cloud compute, storage and networking infrastructure, you can easily manage storage operations and meet the requirements of any workload. This quest is the first quest of its kind, involving NetApp technology on Google Cloud. In this quest you will get hands-on practice on using NetApp Cloud Manager and Cloud Volumes ONTAP and learn about the capabilities delivered such as multi-protocol data access, built-in storage efficiencies and data protection features, remote caching and more.
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Flex your Google Clout! Each week unlocks a new cloud puzzle. How fast can you find the solution? Share your score on your choice of social networks and join the conversation over in the Google Cloud Community.
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In this quest you will get hands-on experience writing infrastructure as code with Terraform.
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Complete the intermediate Create and Manage Bigtable Instances skill badge to demonstrate skills in the following: creating instances, designing schemas, querying data, and performing administrative tasks in Bigtable including monitoring Bigtable performance and configuring node autoscaling and replication. A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete this Skill Badge, and the final assessment challenge lab, to receive a digital badge that you can share with your network.
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Flex your Google Clout! Each day unlocks a new cloud puzzle. Complete all five and you’ll earn the inaugural Google Cloud badge! Share your score on your choice of social networks and join the conversation over in the Google Cloud Community.
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Complete the introductory Create and Manage Cloud Spanner Instances skill badge to demonstrate skills in the following: creating and interacting with Cloud Spanner instances and databases; loading Cloud Spanner databases using various techniques; backing up Cloud Spanner databases; defining schemas and understanding query plans; and deploying a Modern Web App connected to a Cloud Spanner instance. A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete this skill badge course, and the final assessment challenge lab, to receive a skill badge that you can share with your network.
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Earn a skill badge by completing the Explore Machine Learning Models with Explainable AI quest, where you will learn how to do the following using Explainable AI: build and deploy a model to an AI platform for serving (prediction), use the What-If Tool with an image recognition model, identify bias in mortgage data using the What-If Tool, and compare models using the What-If Tool to identify potential bias. A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete this skill badge quest and the final assessment challenge lab to receive a skill badge that you can share with your network.
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This is the second Quest in a two-part series on Google Cloud billing and cost management essentials. This Quest is most suitable for those in a Finance and/or IT related role responsible for optimizing their organization’s cloud infrastructure. Here you'll learn several ways to control and optimize your Google Cloud costs, including setting up budgets and alerts, managing quota limits, and taking advantage of committed use discounts. In the hands-on labs, you’ll practice using various tools to control and optimize your Google Cloud costs or to influence your technology teams to apply the cost optimization best practices.
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Complete the introductory Derive Insights from BigQuery Data skill badge to demonstrate skills in the following: write SQL queries, query public tables, load sample data into BigQuery, troubleshoot common syntax errors with the query validator in BigQuery, and create reports in Looker Studio by connecting to BigQuery data. A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete this skill badge course, and the final assessment challenge lab, to receive a skill badge that you can share with your network.
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Many traditional enterprises use legacy systems and applications that can't stay up-to-date with modern customer expectations. Business leaders often have to choose between maintaining their aging IT systems or investing in new products and services. 'Modernize Infrastructure and Applications with Google Cloud' explores these challenges and offers solutions to overcome them by using cloud technology.
Part of the Cloud Digital Leader learning path, this course aims to help individuals grow in their role and build the future of their business.
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Earn a skill badge by completing the Share Data Using Google Data Cloud course, where you will gain practical experience with Google Cloud Data Sharing Partners, which have proprietary datasets that customers can use for their analytics use cases. Customers subscribe to this data, query it within their own platform, then augment it with their own datasets and use their visualization tools for their customer facing dashboards. A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete the skill badge course, and final assessment challenge lab, to receive a digital badge that you can share with your network.
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This quest introduces you to Vault and teaches you how to secure, store, and tightly control access to tokens, passwords, certificates, and encryption keys to protect secrets and other sensitive data.
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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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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 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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Cloud Logging is a fully managed service that performs at scale. It can ingest application and system log data from thousands of VMs and, even better, analyze all that log data in real time. In this fundamental-level Quest, you learn how to store, search, analyze, monitor, and alert on log data and events from Google Cloud. The labs in the Quest give you hands-on practice using Cloud Logging to maximize your learning experience and provide insight on how you can use Cloud Logging to your own Google Cloud environment.
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Earn a skill badge by completing the Integrate with Machine Learning APIs course, where you learn the basic features for the following machine learning and AI technologies: Cloud Vision API, Cloud Cloud Translation API, and Cloud Natural Language API. A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete this skill badge course, and the final assessment challenge lab, to receive a skill badge that you can share with your network.
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Cloud technology can bring great value to an organization, and combining the power of cloud technology with data has the potential to unlock even more value and create new customer experiences. “Exploring Data Transformation with Google Cloud” explores the value data can bring to an organization and ways Google Cloud can make data useful and accessible. Part of the Cloud Digital Leader learning path, this course aims to help individuals grow in their role and build the future of their business.
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Quest level dasar ini berbeda dari penawaran Qwiklabs lainnya. Lab-lab di dalamnya telah diseleksi guna membekali profesional IT dengan praktik langsung tentang berbagai topik dan layanan yang diujikan dalam Sertifikasi Google Cloud Certified Associate Cloud Engineer. Quest ini berisi lab-lab khusus yang akan menguji pengetahuan Anda seputar Google Cloud, mulai dari IAM, networking, hingga deployment Kubernetes Engine. Selesaikan quest ini, termasuk challenge lab di akhir quest, untuk menerima badge digital eksklusif dari Google Cloud. Harap diketahui, meskipun praktik dengan lab ini dapat meningkatkan keterampilan dan kemampuan Anda, sebaiknya pelajari juga panduan ujian serta referensi persiapan lain yang tersedia.
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Machine Learning is one of the most innovative fields in technology, and the Google Cloud Platform has been instrumental in furthering its development. With a host of APIs, GCP has a tool for just about any machine learning job. In this advanced-level quest, you will get hands-on practice with machine learning at scale and how to employ the advanced ML infrastructure available on GCP.
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Workspace (formerly G Suite) is Google's collaborative applications platform, delivered from Google Cloud. In this introductory-level quest, you will get hands-on practice with Workspace’s core applications from a user perspective. Although there are many more applications and tool components to Workspace than are covered here, you will get experience with the primary apps: Gmail, Calendar, Sheets and a handful of others. Each lab can be completed in 10-15 minutes, but extra time is provided to allow self-directed free exploration of the applications.
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This quest of hands-on labs demonstrates the power of integrating Google Cloud services and tools with Workspace applications - like using Node.js to build a survey bot, the Natural Language API to recognize sentiment in a Google Doc, and building a chat bot with Apps Script.
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Twelve years ago Lily started the Pet Theory chain of veterinary clinics, and has been expanding rapidly. Now, Pet Theory is experiencing some growing pains: their appointment scheduling system is not able to handle the increased load, customers aren't receiving lab results reliably through email and text, and veteranerians are spending more time with insurance companies than with their patients. Lily wants to build a cloud-based system that scales better than the legacy solution and doesn't require lots of ongoing maintenance. The team has decided to go with serverless technology. For the labs in the Google Cloud Run Serverless Quest, you will read through a fictitious business scenario in each lab and assist the characters in implementing a serverless solution. 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…
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The hands-on labs in this Quest are structured to give experienced app developers hands-on practice with the state-of-the-art developing applications in Google Cloud. The topics align with the Google Cloud Certified Professional Cloud Developer Certification. These labs follow the sequence of activities needed to create and deploy an app in Google Cloud from beginning to end. Be aware that while practice with these labs will increase your skills and abilities, it is recommended that you also review the exam guide and other available preparation resources.
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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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Obtain a competitive advantage through DevOps. DevOps is an organizational and cultural movement that aims to increase software delivery velocity, improve service reliability, and build shared ownership among software stakeholders. In this quest you will learn how to use Google Cloud to improve the speed, stability, availability, and security of your software delivery capability. DevOps Research and Assessment has joined Google Cloud. How does your team measure up? Take this five question multiple-choice quiz and find out! 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.
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The Data Lake Modernization course aims to prepare you to lead a Data Lake Modernization engagement through discovery & qualification through the technical considerations & cost modelling. The training is designed to educate on the Migration Journey, Data Lifecycle, Costing & Hands on Technical execution. At the end of the training you will have a deeper understanding of the Data Lake ecosystem, modernizing and migrating to GCP and hands-on experience of building data ingestion, processing & analytics pipelines on GCP.
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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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In this course, you will receive technical training for Enterprise Data Warehouses solutions using BigQuery based on the best practices developed internally by Google’s technical sales and services organizations. The course will also provide guidance and training on key technical challenges that can arise when migrating existing Enterprise Data Warehouses and ETL pipelines to Google Cloud. You will get hands-on experience with real migration tasks, such as data migration, schema optimization, and SQL Query conversion and optimization. The course will also cover key aspects of ETL pipeline migration to Dataproc as well as using Pub/Sub, Dataflow, and Cloud Data Fusion, giving you hands-on experience using all of these tools for Data Warehouse ETL pipelines.
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This content is deprecated. Please see the latest version of the course, here.
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This course focuses on how you can bring your on-premises data lakes and workloads to Google Cloud to unlock cost savings and scale.
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This course further explores SQL Server on Google Cloud.
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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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Processing streaming data is becoming increasingly popular as streaming enables businesses to get real-time metrics on business operations. This course covers how to build streaming data pipelines on Google Cloud. Pub/Sub is described for handling incoming streaming data. The course also covers how to apply aggregations and transformations to streaming data using Dataflow, and how to store processed records to BigQuery or Cloud Bigtable for analysis. Learners get hands-on experience building streaming data pipeline components on Google Cloud by using QwikLabs.
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The two key components of any data pipeline are data lakes and warehouses. This course highlights use-cases for each type of storage and dives into the available data lake and warehouse solutions on Google Cloud in technical detail. Also, this course describes the role of a data engineer, the benefits of a successful data pipeline to business operations, and examines why data engineering should be done in a cloud environment.
This is the first course of the Data Engineering on Google Cloud series. After completing this course, enroll in the Building Batch Data Pipelines on Google Cloud course.
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Google Cloud Application Programming Interfaces are the mechanism to interact with Google Cloud Services programmatically. This quest will give you hands-on practice with a variety of GCP APIs, which you will learn through working with Google’s APIs Explorer, a tool that allows you to browse APIs and run their methods interactively. By learning how to transfer data between Cloud Storage buckets, deploy Compute Engine instances, configure Dataproc clusters and much more, Exploring APIs will show you how powerful APIs are and why they are used almost exclusively by proficient GCP users. Enroll in this quest today.
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Complete the introductory Build LookML Objects in Looker skill badge to demonstrate skills in the following: building new dimensions and measures, views, and derived tables; setting measure filters and types based on requirements; updating dimensions and measures; building and refining Explores; joining views to existing Explores; and deciding which LookML objects to create based on business requirements. A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete this skill badge course and the final assessment challenge lab to receive a skill badge that you can share with your network.
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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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Complete the introductory Prepare Data for Looker Dashboards and Reports skill badge to demonstrate skills in the following: filtering, sorting, and pivoting data, merging results from different Looker Explores, and using functions and operators to build Looker dashboards and reports for data analysis and visualization. A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete this skill badge course and the final assessment challenge lab to receive a skill badge that you can share with your network.
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Complete the intermediate Create ML Models with BigQuery ML skill badge to demonstrate skills in the following: creating and evaluating machine learning models with BigQuery ML to make data predictions. A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete this skill badge course, and the final assessment challenge lab, to receive a skill badge that you can share with your network.
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Security is an uncompromising feature of Google Cloud services, and Google Cloud has developed specific tools for ensuring safety and identity across your projects. In this fundamental-level quest, you will get hands-on practice with Google Cloud’s Identity and Access Management (IAM) service, which is the go-to for managing user and virtual machine accounts. You will get experience with network security by provisioning VPCs and VPNs, and learn what tools are available for security threat and data loss protections. 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.
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Earn a skill badge by completing the Build and Secure Networks in Google Cloud course, where you will learn about multiple networking-related resources to build, scale, and secure your applications on Google Cloud. A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete the skill badge, and final assessment challenge lab, to receive a digital badge that you can share with your network.
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Earn a skill badge by completing the Monitor Environments with Managed Service for Prometheus quest, where you learn Kubernetes Monitoring with Google Cloud Managed Service for Prometheus. A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete this Skill Badge, and the final assessment challenge lab, to receive a digital badge that you can share with your network.
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This course is intended to give architects, engineers, and developers the skills required to help enterprise customers architect, plan, execute, and test database migration projects. Through a combination of presentations, demos, and hands-on labs participants move databases to Google Cloud while taking advantage of various services.
This course covers how to move on-premises, enterprise databases like SQL Server to Google Cloud (Compute Engine and Cloud SQL) and Oracle to Google Cloud bare metal.
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This advanced-level Quest builds on its predecessor Quest, and offers hands-on practice on the more advanced data integration features available in Cloud Data Fusion, while sharing best practices to build more robust, reusable, dynamic pipelines. Learners get to try out the data lineage feature as well to derive interesting insights into their data’s history.
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Quest level dasar ini berbeda dari penawaran Qwiklabs lainnya. Lab-lab di dalamnya telah diseleksi guna membekali profesional IT dengan praktik langsung tentang berbagai topik dan layanan yang diujikan dalam Sertifikasi Cloud Certified Professional Cloud Architect. Quest ini berisi lab-lab khusus yang akan menguji pengetahuan Anda seputar Google Cloud, mulai dari IAM, networking, hingga deployment Kubernetes Engine. Selesaikan quest ini, termasuk challenge lab di akhir quest, untuk menerima badge digital eksklusif dari Google Cloud. Harap diketahui, meskipun praktik dengan lab ini dapat meningkatkan keterampilan dan kemampuan Anda, sebaiknya pelajari juga panduan ujian serta referensi persiapan lain yang tersedia.
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This introductory-level quest shows application developers how the Google Cloud ecosystem could help them build secure, scalable, and intelligent cloud native applications. You learn how to develop and scale applications without setting up infrastructure, run data analytics, gain insights from data, and develop with pre-trained ML APIs to leverage machine learning even if you are not a Machine Learning expert. You will also experience seamless integration between various Google services and APIs to create intelligent apps.
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In this quest, you will learn about Google Cloud’s IoT Core service and its integration with other services like GCS, Dataprep, Stackdriver and Firestore. The labs in this quest use simulator code to mimic IOT devices and the learning here should empower you to implement the same streaming pipeline with real world IoT devices.
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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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Earn a skill badge by completing the Automate Data Capture at Scale with Document AI quest, where you learn how to create a document processing pipeline that will automatically process documents. You will learn how to use the Document AI form processor to extract data from the documents and save the data. A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete this skill badge quest, and the final assessment challenge lab, to receive a skill badge that you can share with your network.
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This intermediate-level quest is unique among Qwiklabs quests. These labs have been curated to give operators hands-on practice with Anthos—a new, open application modernization platform on Google Cloud. Anthos enables you to build and manage modern hybrid applications. Tasks include: installing service mesh, collecting telemetry, and securing your microservices with service mesh policies. This quest is composed of labs targeted to teach you everything you need to know to introduce service mesh, and Anthos, into your next hybrid cloud project.
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Google Cloud’s four step structured Cloud Migration Path Methodology provides a defined and repeatable path for users to follow when migrating and modernizing Virtual Machines. In this quest, you will get hands-on practice with Google’s current solution set for VM assessment, planning, migration, and modernization. You will start by analyzing your lab environment and building assessment reports with CloudPhysics and StratoZone, then build a landing zone within Google Cloud leveraging Terraform’s infrastructure-as-code templates, next you will manually transform a two-tier application into a cloud-native workload running on Kubernetes, and finally, transform a VM workload into Kubernetes with Migrate for Anthos and migrate a VM between cloud environments.
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If you want to take your Google Cloud networking skills to the next level, look no further. This course is composed of labs that cover real-life use cases and it will teach you best practices for overcoming common networking bottlenecks. From getting hands-on practice with testing and improving network performance, to integrating high-throughput VPNs and networking tiers, Network Performance and Optimization is an essential course for Google Cloud developers who are looking to double down on application speed and robustness.
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In this introductory-level quest, you will learn the fundamentals of developing and deploying applications on the Google Cloud Platform. You will get hands-on experience with the Google App Engine framework by launching applications written in languages like Python, Ruby, and Java (just to name a few). You will see first-hand how straightforward and powerful GCP application frameworks are, and how easily they integrate with GCP database, data-loss prevention, and security services.
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Quest level dasar ini berbeda dengan penawaran Qwiklabs lainnya. Semua lab yang termasuk dalam level ini telah diseleksi untuk membekali profesional IT dengan praktik langsung tentang berbagai topik dan layanan yang diujikan dalam Sertifikasi Google Cloud Certified Associate Cloud Engineer. Dari IAM, hingga jaringan, dan penerapan Kubernetes Engine, quest ini tersusun atas sejumlah lab spesifik yang akan menguji pengetahuan Anda tentang GCP. Harap diketahui bahwa, meskipun praktik dengan lab ini akan meningkatkan keterampilan dan kemampuan Anda, sebaiknya Anda juga mempelajari panduan ujian serta referensi persiapan lain yang tersedia.
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The Google Cloud Platform provides many different frameworks and options to fit your application’s needs. In this introductory-level quest, you will get plenty of hands-on practice deploying sample applications on Google App Engine. You will also dive into other web application frameworks like Firebase, Wordpress, and Node.js and see firsthand how they can be integrated with Google Cloud.
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TensorFlow is an open source software library for high performance numerical computation that's great for writing models that can train and run on platforms ranging from your laptop to a fleet of servers in the Cloud to an edge device. This quest takes you beyond the basics of using predefined models and teaches you how to build, train and deploy your own on Google Cloud.
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Want to build ML models in minutes instead of hours using just SQL? BigQuery ML democratizes machine learning by letting data analysts create, train, evaluate, and predict with machine learning models using existing SQL tools and skills. In this series of labs, you will experiment with different model types and learn what makes a good model. 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.
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In this Quest, the experienced user of Google Cloud will learn how to describe and launch cloud resources with Terraform, an open source tool that codifies APIs into declarative configuration files that can be shared amongst team members, treated as code, edited, reviewed, and versioned. In these nine hands-on labs, you will work with example templates and understand how to launch a range of configurations, from simple servers, through full load-balanced applications.
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If you are a novice cloud developer looking for hands-on practice beyond Google Cloud Essentials, this quest is for you. You will get practical experience through labs that dive into Cloud Storage and other key application services like Stackdriver and Cloud Functions. By taking this quest, you will develop valuable skills that are applicable to any Google Cloud initiative. 1-minute videos walk you through key concepts for these labs.
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Networking is a principle theme of cloud computing. It’s the underlying structure of Google Cloud, and it’s what connects all your resources and services to one another. This quest will cover essential Google Cloud networking services and will give you hands-on practice with specialized tools for developing mature networks. From learning the ins-and-outs of VPCs, to creating enterprise-grade load balancers, Networking Fundamentals in the Google Cloud will give you the practical experience needed so you can start building robust networks right away.
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Learn the ins and outs of Google Cloud's operations suite, an important service for generating insights into the health of your applications. It provides a wealth of information in application monitoring, report logging, and diagnoses. These labs will give you hands-on practice with and will teach you how to monitor virtual machines, generate logs and alerts, and create custom metrics for application data. It is recommended that the students have at least earned a Badge by completing the Google Cloud Essentials. Looking for a hands on challenge lab to demonstrate your skills and validate your knowledge? On completing this course, enroll in and finish the challenge lab at the end of the Monitor and Log with Google Cloud Operations Suite to receive an exclusive Google Cloud digital badge.
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Kubernetes is the most popular container orchestration system, and Google Kubernetes Engine was designed specifically to support managed Kubernetes deployments in Google Cloud. In this advanced-level quest, you will get hands-on practice configuring Docker images, containers, and deploying fully-fledged Kubernetes Engine applications. This quest will teach you the practical skills needed for integrating container orchestration into your own workflow. Looking for a hands-on challenge lab to demonstrate your skills and validate your knowledge? On completing this quest, finish the additional Challenge Lab at the end of the Deploy to Kubernetes in Google Cloud Quest to receive an exclusive Google Cloud digital badge.
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Quest level dasar ini berbeda dengan penawaran Qwiklabs lainnya. Semua lab yang termasuk dalam level ini telah diseleksi untuk membekali profesional IT dengan praktik langsung tentang berbagai topik dan layanan yang diujikan dalam Sertifikasi Google Cloud Certified Professional Cloud Architect . Dari IAM, hingga jaringan, dan penerapan Kubernetes Engine, quest ini tersusun atas sejumlah lab spesifik yang akan menguji pengetahuan Anda tentang GCP. Harap diketahui bahwa, meskipun praktik dengan lab ini akan meningkatkan keterampilan dan kemampuan Anda, sebaiknya Anda juga mempelajari panduan ujian serta referensi persiapan lain yang tersedia.
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This quest offers hands-on practice with Cloud Data Fusion, a cloud-native, code-free, data integration platform. ETL Developers, Data Engineers and Analysts can greatly benefit from the pre-built transformations and connectors to build and deploy their pipelines without worrying about writing code. This Quest starts with a quickstart lab that familiarises learners with the Cloud Data Fusion UI. Learners then get to try running batch and realtime pipelines as well as using the built-in Wrangler plugin to perform some interesting transformations on data.
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Organizations around the world rely on Apache Kafka to integrate existing systems in real time and build a new class of event streaming applications that unlock new business opportunities. Google and Confluent are in a partnership to deliver the best event streaming service based on Apache Kafka and to build event driven applications and big data pipelines on Google Cloud Platform. In this Quest, you will first learn how to deploy and create a streaming data pipeline with Apache Kafka. You will then perform hand-on labs on the different functionalities of the Confluent Platform such as deploying and running Apache Kafka on GKE, building a Cloud Run Streaming Application with Apache Kafka on Anthos or developing a Streaming Microservices Application.
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Containerized applications have changed the game and are here to stay. With Kubernetes, you can orchestrate containers with ease, and integration with the Google Cloud Platform is seamless. In this advanced-level quest, you will be exposed to a wide range of Kubernetes use cases and will get hands-on practice architecting solutions over the course of 8 labs. From building Slackbots with NodeJS, to deploying game servers on clusters, to running the Cloud Vision API, Kubernetes Solutions will show you first-hand how agile and powerful this container orchestration system is.
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Google Cloud is committed to supporting Windows workloads in its frameworks and services. In this advanced-level quest, you will get hands-on practice running many of the popular Windows services on Google Cloud. For example, you will learn how to instantiate Microsoft SQL databases, cloud tools for Powershell on Google Cloud Platform frameworks.
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Big data, machine learning, and artificial intelligence are today’s hot computing topics, but these fields are quite specialized and introductory material is hard to come by. Fortunately, Google Cloud provides user-friendly services in these areas, and with this introductory-level quest, so you can take your first steps with tools like Big Query, Cloud Speech API, and AI Platform. Want extra help? 1-minute videos walk you through key concepts for each lab.
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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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In this advanced-level quest, you will learn how to harness serious Google Cloud computing power to run big data and machine learning jobs. The hands-on labs will give you use cases, and you will be tasked with implementing big data and machine learning practices utilized by Google’s very own Solutions Architecture team. From running Big Query analytics on tens of thousands of basketball games, to training TensorFlow image classifiers, you will quickly see why Google Cloud is the go-to platform for running big data and machine learning jobs.
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In this introductory level Quest you will gain practical experience on the fundamentals of sports data science using BigQuery. Start your journey by creating a soccer dataset in BigQuery by importing CSV and JSON files. Harness the power of BigQuery with sophisticated SQL analytical concepts, including using BigQuery ML to train an expected goals model on the soccer event data and evaluate the impressiveness of World Cup goals.
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Earn a skill badge by completing the Migrate MySQL data to Cloud SQL using Database Migration Services quest, where you will learn how to migrate MySQL data to Cloud SQL using Database Migration Services, including how to: connect to Cloud SQL instances using the Cloud Console and with different job types and connectivity options available in Database Migration Service, and migrate MySQL user data when running Database Migration Service jobs. A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete this skill badge quest, and the final assessment challenge lab, to receive a skill badge that you can share with your network.
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This advanced-level quest offers hands-on practice with migrating MySQL data to Cloud SQL using Database Migration Service. You will start with an introductory lab that briefly reviews how to get started with Cloud SQL for MySQL, including how to connect to Cloud SQL instances using the Cloud Console, then continue with two labs focused on migrating MySQL databases to Cloud SQL using different job types and connectivity options available in Database Migration Service. The quest ends with migrating MySQL user data when running Database Migration Service jobs.
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Google Cloud Fundamentals: Core Infrastructure 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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Big data, machine learning, and scientific data? It sounds like the perfect match. In this advanced-level quest, you will get hands-on practice with GCP services like Big Query, Dataproc, and Tensorflow by applying them to use cases that employ real-life, scientific data sets. By getting experience with tasks like earthquake data analysis and satellite image aggregation, Scientific Data Processing will expand your skill set in big data and machine learning so you can start tackling your own problems across a spectrum of scientific disciplines.
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Blockchain and related technologies, such as distributed ledger and distributed apps, are becoming new value drivers and solution priorities in many industries. In this Quest you will gain hands-on experience with distributed ledger and the exploration of blockchain datasets in Google Cloud. This Quest brings the research and solution work of Google's Allen Day into self-paced labs for you to run and learn directly. Since this Quest utilizes advanced SQL in BigQuery, a SQL-in-BigQuery refresher lab is at the start. The final lab is an advanced challenge-style lab in which there are elements where you are not provided the answer but must solve it for yourself.
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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.
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Want to turn your marketing data into insights and build dashboards? Bring all of your data into one place for large-scale analysis and model building. Get repeatable, scalable, and valuable insights into your data by learning how to query it and using BigQuery. 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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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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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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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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Earn a skill badge by completing the Create Conversational AI Agents with Dialogflow CX quest, where you will learn how to create a conversational virtual agent, including how to: define intents and entities, use versions and environments, create conversational branching, and use IVR features. A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete this skill badge quest, and the final assessment challenge lab, to receive a skill badge that you can share with your network.
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In this advanced-level quest, you will learn the ins and outs of developing GCP applications in Python. The first labs will walk you through the basics of environment setup and application data storage with Cloud Datastore. Once you have a handle on the fundamentals, you will get hands-on practice deploying Python applications on Kubernetes and App Engine (the latter is the same framework that powers Snapchat!) With specialized bonus labs that teach user authentication and backend service development, this quest will give you practical experience so you can start developing robust Python applications straight away.
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In this series of labs you will learn how to use BigQuery to analyze NCAA basketball data with SQL. Build a Machine Learning Model to predict the outcomes of NCAA March Madness basketball tournament games.
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Want to learn the core SQL and visualization skills of a Data Analyst? Interested in how to write queries that scale to petabyte-size datasets? Take the BigQuery for Analyst Quest and learn how to query, ingest, optimize, visualize, and even build machine learning models in SQL inside of BigQuery.
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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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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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Complete the intermediate Engineer Data for Predictive Modeling with BigQuery ML skill badge to demonstrate skills in the following: building data transformation pipelines to BigQuery using Dataprep by Trifacta; using Cloud Storage, Dataflow, and BigQuery to build extract, transform, and load (ETL) workflows; building machine learning models using BigQuery ML; and using Cloud Composer to copy data across multiple locations. A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete the skill badge course, and final assessment challenge lab, to receive a digital badge that you can share with your network.
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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.
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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.
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Cloud SQL is a fully managed database service that stands out from its peers due to high performance, seamless integration, and impressive scalability. In this quest you will receive hands-on practice with the basics of Cloud SQL and quickly progress to advanced features, which you will apply to production frameworks and application environments. From creating instances and querying data with SQL, to building Deployment Manager scripts and connecting Cloud SQL instances with applications run on GKE containers, this quest will give you the knowledge and experience needed so you can start integrating this service right away.
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Complete the introductory Implement Load Balancing on Compute Engine skill badge to demonstrate skills in the following: writing gcloud commands and using Cloud Shell, creating and deploying virtual machines in Compute Engine, and configuring network and HTTP load balancers. A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete this skill badge, and the final assessment challenge lab, to receive a skill badge that you can share with your network.
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Dalam quest level pendahuluan ini, Anda akan mendapatkan praktik langsung dengan aneka fitur dan layanan dasar Google Cloud Platform. Dasar-Dasar GCP adalah Quest pertama yang direkomendasikan bagi peserta kursus Google Cloud—Anda dapat memulai dengan pengetahuan yang minim atau tanpa pengetahuan sama sekali tentang cloud, dan selesai dengan pengalaman praktis yang dapat diterapkan pada project GCP pertama Anda. Mulai dari menulis perintah Cloud Shell dan menerapkan mesin virtual pertama Anda, hingga menjalankan aplikasi di Kubernetes Engine atau dengan load balancing, Dasar-Dasar GCP merupakan pengenalan terbaik pada fitur-fitur dasar platform cloud. Setiap lab disertai video berdurasi 1 menit yang akan memandu Anda memahami berbagai konsep penting.
Feature Engineering
Earned Apr 16, 2024 EDT
Responsible AI: Applying AI Principles with Google Cloud
Earned Mar 5, 2024 EST
Generative AI Fundamentals - Bahasa Indonesia
Earned Mar 5, 2024 EST
Introduction to Responsible AI - Bahasa Indonesia
Earned Mar 5, 2024 EST
Building Batch Data Pipelines on Google Cloud
Earned Feb 29, 2024 EST
Build a Data Mesh with Dataplex
Earned Feb 28, 2024 EST
Perform Foundational Data, ML, and AI Tasks in Google Cloud
Earned Feb 27, 2024 EST
Build a Data Warehouse with BigQuery
Earned Feb 22, 2024 EST
Serverless Data Processing with Dataflow: Operations
Earned Feb 16, 2024 EST
Getting Started with MongoDB Atlas on Google Cloud
Earned Des 12, 2023 EST
Tag and Discover BigLake Data
Earned Des 11, 2023 EST
Understanding Cloud Spanner
Earned Nov 27, 2023 EST
GCC Technical Specialty Learning Packs - Data Management - Specialty
Earned Nov 21, 2023 EST
Rapid Migration Program
Earned Nov 21, 2023 EST
Innovating with Data and Google Cloud
Earned Okt 24, 2023 EDT
Introduction to Generative AI Studio - Bahasa Indonesia
Earned Agu 23, 2023 EDT
Create Image Captioning Models - Bahasa Indonesia
Earned Agu 23, 2023 EDT
Encoder-Decoder Architecture - Bahasa Indonesia
Earned Agu 23, 2023 EDT
Introduction to Image Generation - Bahasa Indonesia
Earned Agu 23, 2023 EDT
Get Started with Dataplex
Earned Jun 13, 2023 EDT
Generative AI Explorer - Vertex AI
Earned Mei 17, 2023 EDT
Transformer Models and BERT Model - Bahasa Indonesia
Earned Mei 11, 2023 EDT
Attention Mechanism - Bahasa Indonesia
Earned Mei 11, 2023 EDT
Introduction to Large Language Models - Bahasa Indonesia
Earned Mei 11, 2023 EDT
Introduction to Generative AI - Bahasa Indonesia
Earned Mei 11, 2023 EDT
GCC Technical Specialty Learning Packs - Data Analytics - Prerequisites
Earned Jan 4, 2023 EST
GCC Technical Specialty Learning Packs - Data Management - Prerequisites
Earned Jan 4, 2023 EST
Create and Manage Cloud SQL for PostgreSQL Instances
Earned Jan 4, 2023 EST
Create and Manage AlloyDB Databases
Earned Okt 17, 2022 EDT
NetApp: Build, Protect and Govern your Data Infrastructure On Google Cloud
Earned Sep 8, 2022 EDT
#GoogleClout Set 5 (4/10)
Earned Sep 2, 2022 EDT
Creating Infrastructure on Google Cloud with Terraform
Earned Agu 18, 2022 EDT
Create and Manage Bigtable Instances
Earned Agu 18, 2022 EDT
#GoogleClout Set 2, 1/10
Earned Agu 12, 2022 EDT
Create and Manage Cloud Spanner Instances
Earned Agu 12, 2022 EDT
DEPRECATED Explore Machine Learning Models with Explainable AI
Earned Agu 11, 2022 EDT
Optimize Your Google Cloud Costs
Earned Agu 9, 2022 EDT
Derive Insights from BigQuery Data
Earned Jun 29, 2022 EDT
Modernize Infrastructure and Applications with Google Cloud
Earned Jun 27, 2022 EDT
Share Data Using Google Data Cloud
Earned Jun 27, 2022 EDT
Using Vault on Google Cloud
Earned Jun 24, 2022 EDT
Exploring and Preparing your Data with BigQuery
Earned Jun 23, 2022 EDT
Achieving Advanced Insights with BigQuery
Earned Jun 23, 2022 EDT
Creating New BigQuery Datasets and Visualizing Insights
Earned Jun 23, 2022 EDT
Cloud Logging
Earned Jun 17, 2022 EDT
Use Machine Learning APIs on Google Cloud
Earned Jun 14, 2022 EDT
Exploring Data Transformation with Google Cloud
Earned Jun 12, 2022 EDT
Set up and Configure a Cloud Environment in Google Cloud
Earned Jun 10, 2022 EDT
Advanced ML: ML Infrastructure
Earned Jun 9, 2022 EDT
Workspace Essentials
Earned Jun 9, 2022 EDT
Workspace: Add-ons
Earned Jun 8, 2022 EDT
Google Cloud Run Serverless Workshop
Earned Jun 8, 2022 EDT
Cloud Development
Earned Jun 8, 2022 EDT
Data Science on Google Cloud: Machine Learning
Earned Jun 7, 2022 EDT
DevOps Essentials
Earned Jun 7, 2022 EDT
Data Lake Modernization (Hadoop) on GCP
Earned Mei 27, 2022 EDT
Serverless Data Processing with Dataflow: Develop Pipelines
Earned Mei 27, 2022 EDT
Data Warehousing for Partners: Data Warehouse Migration with BigQuery
Earned Mei 20, 2022 EDT
Google Cloud Platform Fundamentals: Core Infrastructure
Earned Mei 19, 2022 EDT
Data Lake Modernization on Google Cloud
Earned Mei 18, 2022 EDT
Inside Track: SQL Server - Advanced
Earned Mei 17, 2022 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud
Earned Mei 17, 2022 EDT
Building Resilient Streaming Analytics Systems on Google Cloud
Earned Mei 17, 2022 EDT
Modernizing Data Lakes and Data Warehouses with Google Cloud
Earned Mei 17, 2022 EDT
Exploring APIs
Earned Mei 13, 2022 EDT
Build LookML Objects in Looker
Earned Mei 11, 2022 EDT
Applying Advanced LookML Concepts in Looker
Earned Mei 10, 2022 EDT
Prepare Data for Looker Dashboards and Reports
Earned Mei 9, 2022 EDT
Create ML Models with BigQuery ML
Earned Apr 22, 2022 EDT
Security & Identity Fundamentals
Earned Apr 12, 2022 EDT
Build a Secure Google Cloud Network
Earned Apr 11, 2022 EDT
Monitor Environments with Google Cloud Managed Service for Prometheus
Earned Apr 11, 2022 EDT
Enterprise Database Migration
Earned Apr 9, 2022 EDT
![Badge untuk [DEPRECATED] Building Advanced Codeless Pipelines on Cloud Data Fusion](https://cdn.qwiklabs.com/TllGMNYfLB0m5ViqTXDtw38UVQ38DyKKVPlrBbrUwY8%3D)
[DEPRECATED] Building Advanced Codeless Pipelines on Cloud Data Fusion
Earned Apr 6, 2022 EDT
Deploy and Manage Cloud Environments with Google Cloud
Earned Apr 4, 2022 EDT
Google Developer Essentials
Earned Apr 4, 2022 EDT
DEPRECATED IoT in the Google Cloud
Earned Apr 3, 2022 EDT
Applying BigQuery ML's Classification, Regression, and Demand Forecasting for Retail Applications
Earned Apr 2, 2022 EDT
Automate Data Capture at Scale with Document AI
Earned Apr 2, 2022 EDT
Anthos Service Mesh
Earned Apr 2, 2022 EDT
VM Migration
Earned Apr 1, 2022 EDT
Network Performance and Optimization
Earned Mar 30, 2022 EDT
Baseline: Deploy & Develop
Earned Mar 30, 2022 EDT
Cloud Engineering
Earned Mar 29, 2022 EDT
![Badge untuk [DEPRECATED] Deploying Applications](https://cdn.qwiklabs.com/1r8IzkgZ2ozf6jLcsKK81SdLwBzAL1f1vyuNmbXyhjE%3D)
[DEPRECATED] Deploying Applications
Earned Mar 29, 2022 EDT
Intermediate ML: TensorFlow on Google Cloud
Earned Mar 28, 2022 EDT
BigQuery for Machine Learning
Earned Mar 28, 2022 EDT
Managing Cloud Infrastructure with Terraform
Earned Mar 28, 2022 EDT
Baseline: Infrastructure
Earned Mar 26, 2022 EDT
Networking Fundamentals in Google Cloud
Earned Mar 26, 2022 EDT
DEPRECATED Google Cloud's Operations Suite
Earned Mar 25, 2022 EDT
Kubernetes in Google Cloud
Earned Mar 25, 2022 EDT
Cloud Architecture
Earned Mar 23, 2022 EDT
Building Codeless Pipelines on Cloud Data Fusion
Earned Feb 24, 2022 EST
Getting Started with Apache Kafka and Confluent Platform on Google Cloud
Earned Jan 29, 2022 EST
Deprecated Kubernetes Solutions
Earned Jan 19, 2022 EST
DEPRECATED Windows on Google Cloud
Earned Jan 17, 2022 EST
Baseline: Data, ML, AI
Earned Jan 17, 2022 EST
Understanding LookML in Looker
Earned Jan 16, 2022 EST
Google Cloud Solutions II: Data and Machine Learning
Earned Jan 16, 2022 EST
Sports Analytics: Pitch Perfect BigQuery
Earned Jan 15, 2022 EST
Migrate MySQL data to Cloud SQL using Database Migration Service
Earned Jan 14, 2022 EST
Migrating MySQL data to Cloud SQL using Database Migration Service
Earned Jan 14, 2022 EST
Google Cloud Fundamentals: Core Infrastructure - Bahasa Indonesia
Earned Jan 13, 2022 EST
Scientific Data Processing
Earned Jan 13, 2022 EST
Applied Data: Blockchain
Earned Jan 12, 2022 EST
Data Science on Google Cloud
Earned Jan 11, 2022 EST
BigQuery for Marketing Analysts
Earned Jan 10, 2022 EST
Serverless Data Processing with Dataflow: Foundations
Earned Jan 10, 2022 EST
Google Cloud Big Data and Machine Learning Fundamentals
Earned Jan 10, 2022 EST
Preparing for your Professional Data Engineer Journey
Earned Jan 8, 2022 EST
DEPRECATED Create Conversational AI Agents with Dialogflow CX
Earned Des 11, 2021 EST
Application Development - Python
Earned Jul 27, 2020 EDT
NCAA® March Madness®: Bracketology with Google Cloud
Earned Jul 24, 2020 EDT
DEPRECATED BigQuery for Data Analysis
Earned Jul 24, 2020 EDT
BigQuery Basics for Data Analysts
Earned Jul 24, 2020 EDT
Data Catalog Fundamentals
Earned Jul 24, 2020 EDT
Engineer Data for Predictive Modeling with BigQuery ML
Earned Jul 24, 2020 EDT
![Badge untuk [DEPRECATED] Data Engineering](https://cdn.qwiklabs.com/%2BCsWnL49dO1EiQScypoKfAJdI8oWEgDWqu5PBt%2FWepc%3D)
[DEPRECATED] Data Engineering
Earned Jul 24, 2020 EDT
BigQuery for Data Warehousing
Earned Jul 23, 2020 EDT
Cloud SQL
Earned Jul 22, 2020 EDT
Implement Load Balancing on Compute Engine
Earned Jul 22, 2020 EDT
Google Cloud Essentials
Earned Jul 22, 2020 EDT