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Stefan Mettler

Membro dal giorno 2023

Campionato Diamante

24715 punti
Badge per Engineer Data for Predictive Modeling with BigQuery ML Engineer Data for Predictive Modeling with BigQuery ML Earned feb 4, 2024 EST
Badge per Perform Foundational Data, ML, and AI Tasks in Google Cloud Perform Foundational Data, ML, and AI Tasks in Google Cloud Earned feb 4, 2024 EST
Badge per Serverless Data Processing with Dataflow: Operations Serverless Data Processing with Dataflow: Operations Earned nov 25, 2023 EST
Badge per Serverless Data Processing with Dataflow: Develop Pipelines Serverless Data Processing with Dataflow: Develop Pipelines Earned nov 15, 2023 EST
Badge per Serverless Data Processing with Dataflow: Foundations Serverless Data Processing with Dataflow: Foundations Earned nov 10, 2023 EST
Badge per Smart Analytics, Machine Learning, and AI on Google Cloud Smart Analytics, Machine Learning, and AI on Google Cloud Earned nov 6, 2023 EST
Badge per Building Resilient Streaming Analytics Systems on Google Cloud Building Resilient Streaming Analytics Systems on Google Cloud Earned nov 4, 2023 EDT
Badge per Building Batch Data Pipelines on Google Cloud Building Batch Data Pipelines on Google Cloud Earned set 29, 2023 EDT
Badge per Modernizing Data Lakes and Data Warehouses with Google Cloud Modernizing Data Lakes and Data Warehouses with Google Cloud Earned set 27, 2023 EDT
Badge per Google Cloud Big Data and Machine Learning Fundamentals - Italiano Google Cloud Big Data and Machine Learning Fundamentals - Italiano Earned set 27, 2023 EDT
Badge per Preparing for your Professional Data Engineer Journey Preparing for your Professional Data Engineer Journey Earned set 25, 2023 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; 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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Big data, machine learning e intelligenza artificiale sono i temi più attuali nel campo dell'elaborazione dati. Tuttavia, questi settori sono piuttosto specialistici ed è difficile trovare materiale introduttivo. Fortunatamente, Google Cloud fornisce servizi facili da usare in questi ambiti e il Laboratorio Qwiklabs di livello preliminare ti aiuta a muovere i primi passi con strumenti come Big Query, API Cloud Speech e AI Platform. Completa questo Laboratorio, compreso il Challenge Lab finale, per ricevere un esclusivo badge digitale Google Cloud. I video di un minuto ti guideranno attraverso i concetti chiave di ogni laboratorio.

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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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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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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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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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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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Questo corso presenta i prodotti e i servizi per big data e di machine learning di Google Cloud che supportano il ciclo di vita dai dati all'IA. Esplora i processi, le sfide e i vantaggi della creazione di una pipeline di big data e di modelli di machine learning con Vertex AI su 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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