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Wykorzystuj swoje umiejętności w konsoli Google Cloud

Alexandre Janoni

Jest członkiem od 2025

Liga diamentowa

7716 pkt.
Tworzenie siatki danych przy użyciu Dataplex Earned gru 10, 2025 EST
Build a Data Warehouse with BigQuery Earned gru 8, 2025 EST
Serverless Data Processing with Dataflow: Develop Pipelines Earned gru 4, 2025 EST
Serverless Data Processing with Dataflow: Foundations Earned lis 25, 2025 EST
Build Streaming Data Pipelines on Google Cloud Earned lis 25, 2025 EST
Build Batch Data Pipelines on Google Cloud Earned lis 18, 2025 EST
Build Data Lakes and Data Warehouses on Google Cloud Earned lis 13, 2025 EST
Introduction to Data Engineering on Google Cloud Earned lis 12, 2025 EST
Preparing for your Professional Data Engineer Journey Earned lis 8, 2025 EST
Gen AI: Beyond the Chatbot Earned lis 4, 2025 EST

Ukończ szkolenie wprowadzające Tworzenie siatki danych przy użyciu Dataplex, aby zdobyć odznakę potwierdzającą zdobycie następujących umiejętności: tworzenie siatki danych przy użyciu Dataplex w celu ułatwienia zarządzania danymi oraz ich wykrywania i ochrony w Google Cloud. Przećwiczysz i sprawdzisz swoje umiejętności w zakresie tagowania zasobów, przypisywania ról uprawnień i oceny jakości danych w Dataplex.

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Complete the intermediate Build a Data Warehouse with BigQuery skill badge course 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.

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In this second installment of the Dataflow course series, we are going to be diving deeper on developing pipelines using the Beam SDK. We start with a review of Apache Beam concepts. Next, we discuss processing streaming data using windows, watermarks and triggers. We then cover options for sources and sinks in your pipelines, schemas to express your structured data, and how to do stateful transformations using State and Timer APIs. We move onto reviewing best practices that help maximize your pipeline performance. Towards the end of the course, we introduce SQL and Dataframes to represent your business logic in Beam and how to iteratively develop pipelines using Beam notebooks.

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This course is part 1 of a 3-course series on Serverless Data Processing with Dataflow. In this first course, we start with a refresher of what Apache Beam is and its relationship with Dataflow. Next, we talk about the Apache Beam vision and the benefits of the Beam Portability framework. The Beam Portability framework achieves the vision that a developer can use their favorite programming language with their preferred execution backend. We then show you how Dataflow allows you to separate compute and storage while saving money, and how identity, access, and management tools interact with your Dataflow pipelines. Lastly, we look at how to implement the right security model for your use case on Dataflow.

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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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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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Gen AI: Beyond the Chatbot is the first course of the Gen AI Leader learning path and has no prerequisites. This course aims to move beyond the basic understanding of chatbots to explore the true potential of generative AI for your organization. You explore concepts like foundation models and prompt engineering, which are crucial for leveraging the power of gen AI. The course also guides you through important considerations you should make when developing a successful gen AI strategy for your organization.

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