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Feliciah M A

Учасник із 2025

Діамантова ліга

Кількість балів: 22730
Engineer Data for Predictive Modeling with BigQuery ML Earned лип. 31, 2025 EDT
Підготовка даних для інтерфейсів API машинного навчання в Google Cloud Earned лип. 30, 2025 EDT
Derive Insights from BigQuery Data Earned лип. 24, 2025 EDT
Створення сітки даних за допомогою Dataplex Earned лип. 22, 2025 EDT
Build a Data Warehouse with BigQuery Earned лип. 22, 2025 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud Earned лип. 18, 2025 EDT
Build Streaming Data Pipelines on Google Cloud Earned лип. 16, 2025 EDT
Build Batch Data Pipelines on Google Cloud Earned лип. 14, 2025 EDT
Work with Gemini Models in BigQuery Earned лип. 10, 2025 EDT
Serverless Data Processing with Dataflow: Foundations Earned лип. 8, 2025 EDT
Build Data Lakes and Data Warehouses on Google Cloud Earned лип. 7, 2025 EDT
Introduction to Data Engineering on Google Cloud Earned лип. 7, 2025 EDT

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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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 introductory Derive Insights from BigQuery Data skill badge course 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.Create reports in Looker Studio by connecting to BigQuery data.

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Пройдіть вступний кваліфікаційний курс Створення сітки даних за допомогою Dataplex, щоб продемонструвати свої навички створення такої сітки для покращеної безпеки даних, керування ними й пошуку в Google Cloud. Ви потренуєтеся й перевірите свої навички щодо позначення тегами об’єктів, призначення ролей IAM і перевірки якості даних у 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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Incorporating machine learning into data pipelines increases the ability to extract insights from data. This course covers ways machine learning can be included in data pipelines on Google Cloud. For little to no customization, this course covers AutoML. For more tailored machine learning capabilities, this course introduces Notebooks and BigQuery machine learning (BigQuery ML). Also, this course covers how to productionalize machine learning solutions by using Vertex AI.

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In this course you will get hands-on in order to work through real-world challenges faced when building streaming data pipelines. The primary focus is on managing continuous, unbounded data with Google Cloud products.

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In this intermediate course, you will learn to design, build, and optimize robust batch data pipelines on Google Cloud. Moving beyond fundamental data handling, you will explore large-scale data transformations and efficient workflow orchestration, essential for timely business intelligence and critical reporting. Get hands-on practice using Dataflow for Apache Beam and Serverless for Apache Spark (Dataproc Serverless) for implementation, and tackle crucial considerations for data quality, monitoring, and alerting to ensure pipeline reliability and operational excellence. A basic knowledge of data warehousing, ETL/ELT, SQL, Python, and Google Cloud concepts is recommended.

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This course demonstrates how to use AI/ML models for generative AI tasks in BigQuery. Through a practical use case involving customer relationship management, you learn the workflow of solving a business problem with Gemini models. To facilitate comprehension, the course also provides step-by-step guidance through coding solutions using both SQL queries and Python 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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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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