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Andrés Salcedo

Member since 2025

Diamond League

8587 points
Serverless Data Processing with Dataflow: Develop Pipelines Earned Nis 1, 2026 EDT
Serverless Data Processing with Dataflow: Foundations Earned Mar 23, 2026 EDT
Build Streaming Data Pipelines on Google Cloud Earned Mar 23, 2026 EDT
Build Batch Data Pipelines on Google Cloud Earned Mar 21, 2026 EDT
Build Data Lakes and Data Warehouses on Google Cloud Earned Mar 7, 2026 EST
Introduction to Data Engineering on Google Cloud Earned Mar 7, 2026 EST
Google Cloud'da Makine Öğrenimi API'leri İçin Veri Hazırlama Earned Şub 27, 2026 EST
Güvenli Bir Google Cloud Ağı Oluşturma Earned Şub 27, 2026 EST
Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud Earned Şub 25, 2026 EST
[DEPRECATED] - Google Cloud Computing Foundations: Networking and Security in Google Cloud - Locales Earned Şub 23, 2026 EST
Google Cloud'da Uygulama Geliştirme Ortamı Oluşturma Earned Şub 19, 2026 EST
[DEPRECATED] Google Cloud Computing Foundations: Infrastructure in Google Cloud - Locales Earned Şub 19, 2026 EST
Compute Engine İçin Cloud Load Balancing'i Uygulama Earned Eyl 4, 2025 EDT
[DEPRECATED]-Google Cloud Computing Foundations: Cloud Computing Fundamentals - Turkish Earned Eyl 2, 2025 EDT

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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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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In this course, you learn about data engineering on Google Cloud, the roles and responsibilities of data engineers, and how those map to offerings provided by Google Cloud. You also learn about ways to address data engineering challenges.

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Giriş düzeyindeki Google Cloud'da Makine Öğrenimi API'leri İçin Veri Hazırlama beceri rozetini tamamlayarak şu konulardaki becerilerinizi gösterin: Dataprep by Trifacta ile veri temizleme, Dataflow'da veri ardışık düzenleri çalıştırma, Dataproc'ta küme oluşturma ve Apache Spark işleri çalıştırma ve makine öğrenimi API'lerini (Cloud Natural Language API, Google Cloud Speech-to-Text API ve Video Intelligence API dahil olmak üzere) çağırma.

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Güvenli Bir Google Cloud Ağı Oluşturma kursunu tamamlayarak beceri rozeti kazanın. Bu kursta, Google Cloud'da uygulamalarınızı derlemek, ölçeklendirmek ve korumak için ağla ilgili birden fazla kaynak hakkında bilgi edineceksiniz.

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The Google Cloud Computing Foundations courses are for individuals with little to no background or experience in cloud computing. They provide an overview of concepts central to cloud basics, big data, and machine learning, and where and how Google Cloud fits in. By the end of the series of courses, learners will be able to articulate these concepts and demonstrate some hands-on skills. The courses should be completed in the following order: 1. Google Cloud Computing Foundations: Cloud Computing Fundamentals 2. Google Cloud Computing Foundations: Infrastructure in Google Cloud 3. Google Cloud Computing Foundations: Networking and Security in Google Cloud 4. Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud This final course in the series reviews managed big data services, machine learning and its value, and how to demonstrate your skill set in Google Cloud further by earning Skill Badges.

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Google Cloud Computing Foundations kursunda, bulut bilişimi alanında daha önce çalışmamış veya bu konuda hiç deneyimi olmayan bireylere; temel bulut kavramları, büyük veri ve makine öğrenimi gibi kavramlar ve Google Cloud’un bu kavramlarla hangi noktada, nasıl birlikte çalıştığı ayrıntılı bir genel bakışla anlatılır. Kursun sonunda öğrenciler bulut bilişimi, büyük veri ve makine öğrenimi konularında fikir yürütüp bazı becerileri pratik olarak sergileyebilecek seviyeye ulaşacaktır. Bu kurs, Google Cloud Computing Foundations adlı kurs serisinin bir parçasıdır. Kurslar aşağıdaki sırayla tamamlanmalıdır: Google Cloud Computing Foundations: Cloud Computing Fundamentals - Locales Google Cloud Computing Foundations: Infrastructure in Google Cloud - Locales Google Cloud Computing Foundations: Networking and Security in Google Cloud - Locales Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud - Locales Bu üçüncü kursta güvenli ağlar oluşturma,…

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Google Cloud'da Uygulama Geliştirme Ortamı Oluşturma kursunu tamamlayarak beceri rozeti kazanın. Bu kursta Cloud Storage, Identity and Access Management, Cloud Functions ve Pub/Sub gibi teknolojilerin temel özelliklerini kullanarak depolama odaklı bulut altyapısı oluşturma ve bu altyapıyla bağlantı kurmayı öğreneceksiniz.

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Google Cloud Computing Foundations kursunda, bulut bilişimi alanında daha önce çalışmamış veya bu konuda hiç deneyimi olmayan bireylere; temel bulut kavramları, büyük veri ve makine öğrenimi gibi kavramlar ve Google Cloud’un bu kavramlarla hangi noktada, nasıl birlikte çalıştığı ayrıntılı bir genel bakışla anlatılır. Kursun sonunda öğrenciler bulut bilişimi, büyük veri ve makine öğrenimi konularında fikir yürütüp bazı becerileri pratik olarak sergileyebilecek seviyeye ulaşacaktır. Bu kurs, Google Cloud Computing Foundations adlı kurs serisinin bir parçasıdır. Kurslar aşağıdaki sırayla tamamlanmalıdır: Google Cloud Computing Foundations: Cloud Computing Fundamentals - Locales Google Cloud Computing Foundations: Infrastructure in Google Cloud - Locales Google Cloud Computing Foundations: Networking and Security in Google Cloud - Locales Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud - Locales Bu ikinci kursta Google Cloud'da depol…

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Giriş düzeyindeki Compute Engine İçin Cloud Load Balancing'i Uygulama beceri rozetini tamamlayarak şu konulardaki becerilerinizi gösterin: Compute Engine'de sanal makineler oluşturma ve dağıtma. Ağ ve uygulama yük dengeleyicileri yapılandırma.

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Google Cloud Computing Foundations kursunda, bulut bilişimi alanında daha önce çalışmamış veya bu konuda hiç deneyimi olmayan bireylere; temel bulut kavramları, büyük veri, makine öğrenimi gibi kavramlar ve Google Cloud'un bu kavramlarla hangi noktada, nasıl birlikte çalıştığı ayrıntılı bir genel bakışla anlatılır. Kursun sonunda öğrenciler bulut bilişimi, büyük veri ve makine öğrenimi konularında fikir yürütüp bazı becerileri pratik olarak sergileyebilecek seviyeye ulaşacaktır. Bu kurs, Google Cloud Computing Foundations adlı kurs serisinin bir parçasıdır. Kurslar aşağıdaki sırayla tamamlanmalıdır: Google Cloud Computing Foundations: Cloud Computing Fundamentals - Locales Google Cloud Computing Foundations: Infrastructure in Google Cloud - Locales Google Cloud Computing Foundations: Networking and Security in Google Cloud - Locales Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud - Locales İlk kursta bulut bilişimi, Google Cloud'u k…

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