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Apply your skills in Google Cloud console

Maneesh Reddy Duddukunta

Учасник із 2025

Діамантова ліга

Кількість балів: 25455
Підготовка даних для інтерфейсів API машинного навчання в Google Cloud Earned бер. 10, 2026 EDT
Build a Data Warehouse with BigQuery Earned бер. 9, 2026 EDT
Engineer Data for Predictive Modeling with BigQuery ML Earned бер. 9, 2026 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud Earned січ. 10, 2026 EST
Build Data Lakes and Data Warehouses on Google Cloud Earned груд. 9, 2025 EST
Serverless Data Processing with Dataflow: Foundations Earned груд. 9, 2025 EST
Preparing for your Professional Data Engineer Journey Earned груд. 8, 2025 EST
Introduction to Data Engineering on Google Cloud Earned лист. 6, 2025 EST

Пройдіть вступний кваліфікаційний курс Підготовка даних для інтерфейсів API машинного навчання в Google Cloud, щоб продемонструвати свої навички щодо очистки даних за допомогою сервісу Dataprep by Trifacta, запуску конвеєрів даних у Dataflow, створення кластерів і запуску завдань Apache Spark у Dataproc, а також виклику API машинного навчання, зокрема Cloud Natural Language API, Google Cloud Speech-to-Text API і Video Intelligence API.

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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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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; and building machine learning models using BigQuery ML.

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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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While the traditional approaches of using data lakes and data warehouses can be effective, they have shortcomings, particularly in large enterprise environments. This course introduces the concept of a data lakehouse and the Google Cloud products used to create one. A lakehouse architecture uses open-standard data sources and combines the best features of data lakes and data warehouses, which addresses many of their shortcomings.

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This course 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 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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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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