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Florentina Baciu

Date d'abonnement : 2022

Ligue d'Argent

2600 points
Smart Analytics, Machine Learning, and AI on Google Cloud Earned juil. 17, 2023 EDT
Build Batch Data Pipelines on Google Cloud Earned juil. 17, 2023 EDT
Build Streaming Data Pipelines on Google Cloud Earned juil. 14, 2023 EDT
Monitor and Manage Data in BigQuery Earned juil. 5, 2023 EDT
Build Infrastructure with Terraform on Google Cloud Earned juil. 4, 2023 EDT
BigQuery Fundamentals for Teradata Professionals Earned juil. 3, 2023 EDT
Teradata to BigQuery Earned juin 29, 2023 EDT
BigQuery Migration Service Earned juin 28, 2023 EDT
Prepare Data for ML APIs on Google Cloud Earned mars 14, 2023 EDT
Engineer Data for Predictive Modeling with BigQuery ML Earned mars 9, 2023 EST
Implementing Cloud Load Balancing for Compute Engine Earned mars 8, 2023 EST
Build Data Lakes and Data Warehouses on Google Cloud Earned jan. 12, 2023 EST
Google Cloud Big Data and Machine Learning Fundamentals Earned mai 16, 2022 EDT

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 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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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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This skill badge aims to evaluate a partner's ability to utilize BigQuery's features and capabilities to manage and analyze large datasets. Learners will gain hands-on experience through labs and achieve solid understanding of BigQuery's foundational concepts and features.

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Complete the intermediate Build Infrastructure with Terraform on Google Cloud skill badge to demonstrate skills in the following: Infrastructure as Code (IaC) principles using Terraform, provisioning and managing Google Cloud resources with Terraform configurations, effective state management (local and remote), and modularizing Terraform code for reusability and organization.

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This course covers BigQuery fundamentals for professionals who are familiar with SQL-based cloud data warehouses in Teradata and want to begin working in BigQuery. Through interactive lecture content and hands-on labs, you learn how to provision resources, create and share data assets, ingest data, and optimize query performance in BigQuery. Drawing upon your knowledge of Teradata, you also learn about similarities and differences between Teradata and BigQuery to help you get started with data warehouses in BigQuery. After this course, you can continue your BigQuery journey by completing the skill badge quest titled Build and Optimize Data Warehouses with BigQuery.

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This workload aims to upskill Google Cloud partners to perform specific tasks associated with priority workloads. Learners will perform the tasks of Migration from Teradata to BigQuery using the Data Transfer Service and the Teradata TPT Export Utility. Sample Data will be used during both methods. Learners will complete a challenge lab that focuses on the process of transferring both schema, data and SQL from a Teradata data warehouse to BigQuery.

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In this course, you explore the four components that make up the BigQuery Migration Service. They are Migration Assessment, SQL Translation, Data Transfer Service, and Data Validation. You will use each of these tools to perform a migration using to BigQuery.

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Complete the introductory Prepare Data for ML APIs on Google Cloud skill badge to demonstrate skills in the following: cleaning data with Dataprep by Trifacta, running data pipelines in Dataflow, creating clusters and running Apache Spark jobs in Dataproc, and calling ML APIs including the Cloud Natural Language API, Google Cloud Speech-to-Text API, and Video Intelligence API.

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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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Complete the introductory Implementing Cloud Load Balancing for Compute Engine skill badge to demonstrate skills in the following: creating and deploying virtual machines in Compute Engine and configuring network and application load balancers.

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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 introduces the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle. It explores the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud.

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