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Abhijna D V

Membro dal giorno 2022

Campionato Diamante

48005 punti
Exploring Data Transformation with Google Cloud Earned gen 27, 2025 EST
Microsoft SQL Server to Cloud SQL Earned gen 27, 2025 EST
Creazione di sistemi di analisi dei flussi di dati resilienti su Google Cloud Earned gen 27, 2025 EST
Creazione di pipeline di dati in batch su Google Cloud Earned gen 27, 2025 EST
Oracle to Cloud Spanner Earned dic 10, 2024 EST
MySQL to Cloud Spanner Earned dic 10, 2024 EST
Redshift to BigQuery Earned dic 9, 2024 EST
Snowflake to BigQuery Migration Earned dic 9, 2024 EST
Exploring and Preparing your Data with BigQuery Earned dic 9, 2024 EST
Data Warehousing for Partners: Enable Google Cloud Customers Earned dic 5, 2024 EST
Data Warehousing for Partners: Process Data with Dataflow Earned dic 5, 2024 EST
Data Warehousing for Partners: Stream Data with Pub/Sub Earned dic 5, 2024 EST
Data Warehousing for Partners: Streaming Analytics Earned dic 5, 2024 EST
Data Warehousing for Partners: BigQuery Extended Capabilities Earned dic 4, 2024 EST
Data Warehousing for Partners: Analyze Data with Looker Earned dic 3, 2024 EST
Data Lake Modernization on Google Cloud: Intro to Data Lakes Earned dic 2, 2024 EST
Data Lake Modernization on Google Cloud: Migrate Workflows Earned dic 2, 2024 EST
Data Lake Modernization on Google Cloud: Data Governance Earned dic 2, 2024 EST
Teradata to BigQuery Earned nov 27, 2024 EST
BigQuery Migration Service Earned nov 27, 2024 EST
BigQuery Fundamentals for Snowflake Professionals Earned nov 26, 2024 EST
Data Warehousing for Partners: Data Warehouse Migration with BigQuery Earned nov 26, 2024 EST
Data Warehousing for Partners: Migrate Data to BigQuery Earned nov 25, 2024 EST
Oracle to BigQuery Migration Earned nov 25, 2024 EST
Cloudera to Google Cloud Earned giu 17, 2024 EDT
BigQuery Fundamentals for Redshift Professionals Earned mag 17, 2024 EDT
Machine Learning Operations (MLOps): Getting Started Earned mar 4, 2024 EST
Create Image Captioning Models - Italiano Earned mar 4, 2024 EST
Data Warehousing for Partners: Design in BigQuery Earned feb 28, 2024 EST
BigQuery Fundamentals for Teradata Professionals Earned feb 28, 2024 EST
Document AI Earned feb 27, 2024 EST
Smart Analytics, Machine Learning, and AI on Google Cloud - Italiano Earned feb 27, 2024 EST
Responsible AI: Applying AI Principles with Google Cloud - Italiano Earned feb 27, 2024 EST
Virtual Agent Development in Dialogflow CX for Citizen Devs Earned feb 27, 2024 EST
Virtual Agent Development in Dialogflow CX for Software Devs Earned feb 23, 2024 EST
CCAI Operations and Implementation Earned feb 21, 2024 EST
Virtual Agent Development in Dialogflow ES for Citizen Devs Earned feb 20, 2024 EST
Virtual Agent Development in Dialogflow ES for Software Devs Earned feb 19, 2024 EST
Contact Center AI: Conversational Design Fundamentals Earned feb 19, 2024 EST
BI Reporting: Looker Visualization on BigQuery Earned feb 19, 2024 EST
BigQuery Fundamentals for Oracle Professionals Earned feb 16, 2024 EST
Analyzing and Visualizing Data the Google Way Earned feb 13, 2024 EST
Data Warehousing for Partners: Cloud Data Fusion Pipelines Earned ott 30, 2023 EDT
Data Warehousing for Partners: Process Data with Dataproc Earned set 24, 2023 EDT
Data Warehousing for Partners: Optimize in BigQuery Earned set 9, 2023 EDT
Modernizzazione di data lake e data warehouse con Google Cloud Earned set 20, 2022 EDT
Analyzing and Visualizing Data in Looker Earned ago 28, 2022 EDT
Google Cloud Big Data and Machine Learning Fundamentals - Italiano Earned ago 22, 2022 EDT

Cloud technology can bring great value to an organization, and combining the power of cloud technology with data has the potential to unlock even more value and create new customer experiences. “Exploring Data Transformation with Google Cloud” explores the value data can bring to an organization and ways Google Cloud can make data useful and accessible. Part of the Cloud Digital Leader learning path, this course aims to help individuals grow in their role and build the future of their business.

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This course aims to upskill Google Cloud partners to perform specific tasks of migrating data from Microsoft SQL Server to CloudSQL using the built-in replication capabilities of SQL Server. Sample data will be used during the migration. Learners will complete several labs that focus on the process of transferring schema, data, and related processes to corresponding Google Cloud products. One or more challenge labs will test the learner's understanding of the topics.

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L'elaborazione dei flussi di dati sta diventando sempre più diffusa poiché la modalità flusso consente alle aziende di ottenere parametri in tempo reale sulle operazioni aziendali. Questo corso tratta la creazione di pipeline di dati in modalità flusso su Google Cloud. Pub/Sub viene presentato come strumento per la gestione dei flussi di dati in entrata. Il corso spiega anche come applicare aggregazioni e trasformazioni ai flussi di dati utilizzando Dataflow e come archiviare i record elaborati in BigQuery o Bigtable per l'analisi. Gli studenti acquisiranno esperienza pratica nella creazione di componenti della pipeline di dati in modalità flusso su Google Cloud utilizzando QwikLabs.

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Le pipeline di dati in genere rientrano in uno dei paradigmi EL (Extract, Load), ELT (Extract, Load, Transform) o ETL (Extract, Transform, Load). Questo corso descrive quale paradigma dovrebbe essere utilizzato e quando per i dati in batch. Inoltre, questo corso tratta diverse tecnologie su Google Cloud per la trasformazione dei dati, tra cui BigQuery, l'esecuzione di Spark su Dataproc, i grafici della pipeline in Cloud Data Fusion e trattamento dati serverless con Dataflow. Gli studenti fanno esperienza pratica nella creazione di componenti della pipeline di dati su Google Cloud utilizzando Qwiklabs.

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Migration from Oracle to Cloud Spanner using HarbourBridge. This course describes an example scenario that uses sample data during the migration. This process includes using HarbourBridge for Assessment, Schema Conversion, Schema Transformation, Data Migration, and supporting tools for data validation.

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Migration from MySQL to Cloud Spanner using Dataflow that includes sample mock data and all necessary steps with initial assessment to validation including taking care of migrating users and grants.

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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 for migrating data from AWS Redshift to BigQuery using BigQuery Data Transfer Service, which includes sample mock data. Learners will complete a challenge lab that focuses on the process of transferring both schema and data from a Redshift data warehouse to 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 migrating data from Snowflake to BigQuery. Sample data will be used during the migration. Learners will complete several labs that focus on the process of transferring schema, data and related processes to corresponding Google Cloud products.There will be one or more challenge labs that will test the learners' understanding of the topics. "This learning path aims to upskill Google Cloud partners to perform specific tasks associated with priority workloads. Learners will perform the tasks of migrating data from Snowflake to BigQuery.

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In this course, we see what the common challenges faced by data analysts are and how to solve them with the big data tools on Google Cloud. You’ll pick up some SQL along the way and become very familiar with using BigQuery and Dataprep to analyze and transform your datasets. This is the first course of the From Data to Insights with Google Cloud series. After completing this course, enroll in the Creating New BigQuery Datasets and Visualizing Insights course.

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This course discusses the key elements of Google's Data Warehouse solution portfolio and strategy.

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This course continues to explore the implementation of data load and transformation pipelines for a BigQuery Data Warehouse using Dataflow.

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This course explores how to implement a streaming analytics solution using Pub/Sub.

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This course explores how to implement a streaming analytics solution using Dataflow and BigQuery.

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This course explores the Geographic Information Systems (GIS), GIS Visualization, and machine learning enhancements to BigQuery.

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This course explores how to leverage Looker to create data experiences and gain insights with modern business intelligence (BI) and reporting.

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Welcome to Intro to Data Lakes, where we discuss how to create a scalable and secure data lake on Google Cloud that allows enterprises to ingest, store, process, and analyze any type or volume of full fidelity data.

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Welcome to Migrate Workflows, where we discuss how to migrate Spark and Hadoop tasks and workflows to Google Cloud.

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Welcome to Data Governance, where we discuss how to implement data governance on Google Cloud.

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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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This course covers BigQuery fundamentals for professionals who are familiar with SQL-based cloud data warehouses in Snowflake 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 Snowflake, you also learn about similarities and differences between Snowflake 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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In this course, you will receive technical training for Enterprise Data Warehouses solutions using BigQuery based on the best practices developed internally by Google’s technical sales and services organizations. The course will also provide guidance and training on key technical challenges that can arise when migrating existing Enterprise Data Warehouses and ETL pipelines to Google Cloud. You will get hands-on experience with real migration tasks, such as data migration, schema optimization, and SQL Query conversion and optimization. The course will also cover key aspects of ETL pipeline migration to Dataproc as well as using Pub/Sub, Dataflow, and Cloud Data Fusion, giving you hands-on experience using all of these tools for Data Warehouse ETL pipelines.

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This course identifies best practices for migrating data warehouses to BigQuery and the key skills required to perform successful migration.

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Perform a migration from Oracle to BigQuery using SQL Translation and DataFlow using Sample Data. Learners will complete a quiz that focuses on the process of transferring both schema and data from an Oracle enterprise data warehouse to 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 migrating data from five products hosted on Cloudera or Hortonworks to corresponding Google Cloud services and hosted products. The migration solutions addressed will be: HDFS data to Google Cloud Dataproc and Cloud Storage Hive data to Cloud Dataproc and the Cloud Dataproc Metastore Hive data to Google Cloud BigQuery Impala data to Google Cloud BigQuery HBase to Google Cloud Bigtable Sample data will be used during all five migrations. Learners will complete several labs that focus on the process of transferring schema, data and related processes to corresponding Google Cloud products.There will be one or more challenge labs that will test the learners understanding of the topics.

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This course covers BigQuery fundamentals for professionals who are familiar with SQL-based cloud data warehouses in Redshift 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 Redshift, you also learn about similarities and differences between Redshift 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 course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Machine Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. They work with (or can be) Data Scientists, who develop models, to enable velocity and rigor in deploying the best performing models.

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Questo corso ti insegna come creare un modello per le didascalie delle immagini utilizzando il deep learning. Scoprirai i diversi componenti di un modello per le didascalie delle immagini, come l'encoder e il decoder, e imparerai ad addestrare e valutare il tuo modello. Alla fine di questo corso, sarai in grado di creare modelli personali per le didascalie delle immagini e utilizzarli per generare didascalie per le immagini.

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Welcome to Design in BigQuery, where we map Enterprise Data Warehouse concepts and components to BigQuery and Google data services with a focus on schema design.

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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 course provides partners the skills required to scope, design and deploy Document AI solutions for enterprise customers utilizing use-cases from both the procurement and lending arenas.

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L'integrazione del machine learning nelle pipeline di dati aumenta la capacità di estrarre insight dai dati. Questo corso illustra i modi in cui il machine learning può essere incluso nelle pipeline di dati su Google Cloud. Per una personalizzazione minima o nulla, il corso tratta di AutoML. Per funzionalità di machine learning più personalizzate, il corso introduce Notebooks e BigQuery Machine Learning (BigQuery ML). Inoltre, il corso spiega come mettere in produzione soluzioni di machine learning utilizzando Vertex AI.

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Dal momento che l'uso dell'intelligenza artificiale e del machine learning nelle aziende continua a crescere, cresce anche l'importanza di realizzarli in modo responsabile. Molti sono scoraggiati dal fatto che parlare di IA responsabile può essere più facile che metterla in pratica. Se vuoi imparare come operativizzare l'IA responsabile nella tua organizzazione, questo corso fa per te. In questo corso scoprirai come Google Cloud ci riesce attualmente, oltre alle best practice e alle lezioni apprese, per fungere da framework per costruire il tuo approccio all'IA responsabile.

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Welcome to "Virtual Agent Development in Dialogflow CX for Citizen Devs", the second course in the "Customer Experiences with Contact Center AI" series. In this course, learn how to develop customer conversational solutions using Contact Center Artificial Intelligence (CCAI). In this course, you'll be introduced to adding voice (telephony) as a communication channel to your virtual agent conversations using Dialogflow CX.

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Welcome to "Virtual Agent Development in Dialogflow CX for Software Devs", the third course in the "Customer Experiences with Contact Center AI" series. In this course, learn how to develop more customized customer conversational solutions using Contact Center Artificial Intelligence (CCAI). In this course, you'll be introduced to more advanced and customized handling for virtual agent conversations that need to look up and convey dynamic data, and methods available to you for testing your virtual agent and logs which can be useful for understanding issues that arise. This is an intermediate course, intended for learners with the following type of role: Software developers: Codes computer software in a programming language (e.g., C++, Python, Javascript) and often using an SDK/API.

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Welcome to "CCAI Operations and Implementation", the fourth course in the "Customer Experiences with Contact Center AI" series. In this course, learn some best practices for integrating conversational solutions with your existing contact center software, establishing a framework for human agent assistance, and implementing solutions securely and at scale. In this course, you'll be introduced to Agent Assist and the technology it uses so you can delight your customers with the efficiencies and accuracy of services provided when customers require human agents, connectivity protocols, APIs, and platforms which you can use to create an integration between your virtual agent and the services already established for your business, Dialogflow's Environment Management tool for deployment of different versions of your virtual agent for various purposes, compliance measures and regulations you should be aware of when bringing your virtual agent to production, and you'll be given tips from virtua…

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Welcome to "Virtual Agent Development in Dialogflow ES for Citizen Devs", the second course in the "Customer Experiences with Contact Center AI" series. In this course, learn how to develop customer conversational solutions using Contact Center Artificial Intelligence (CCAI). You will use Dialogflow ES to create virtual agents and test them using the Dialogflow ES simulator. This course also provides best practices on developing virtual agents. You will also be introduced to adding voice (telephony) as a communication channel to your virtual agent conversations. Through a combination of presentations, demos, and hands-on labs, participants learn how to create virtual agents. This is an intermediate course, intended for learners with the following types of roles: Conversational designers: Designs the user experience of a virtual assistant. Translates the brand's business requirements into natural dialog flows. Citizen developers: Creates new business applications fo…

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Welcome to "CCAI Virtual Agent Development in Dialogflow ES for Software Developers", the third course in the "Customer Experiences with Contact Center AI" series. In this course, learn to use additional features of Dialogflow ES for your virtual agent, create a Firestore instance to store customer data, and implement cloud functions that access the data. With the ability to read and write customer data, learner’s virtual agents are conversationally dynamic and able to defer contact center volume from human agents. You'll be introduced to methods for testing your virtual agent and logs which can be useful for understanding issues that arise. Lastly, learn about connectivity protocols, APIs, and platforms for integrating your virtual agent with services already established for your business.

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Welcome to "CCAI Conversational Design Fundamentals", the first course in the "Customer Experiences with Contact Center AI" series. In this course, learn how to design customer conversational solutions using Contact Center Artificial Intelligence (CCAI). You will be introduced to CCAI and its three pillars (Dialogflow, Agent Assist, and Insights), and the concepts behind conversational experiences and how the study of them influences the design of your virtual agent. After taking this course you will be prepared to take your virtual agent design to the next level of intelligent conversation.

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This workload aims to upskill Google Cloud partners to perform specific tasks for modernization using LookML on BigQuery. A proof-of-concept will take learners through the process of creating LookML visualizations on BigQuery. During this course, learners will be guided specifically on how to write Looker modeling language, also known as LookML and create semantic data models, and learn how LookML constructs SQL queries against BigQuery. At a high level, this course will focus on basic LookML to create and access BigQuery objects, and optimize BigQuery objects with LookML.

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This course covers BigQuery fundamentals for professionals who are familiar with SQL-based cloud data warehouses in Oracle 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 Oracle, you also learn about similarities and differences between Oracle 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 learning experience guides you through the process of utilizing various data sources and multiple Google Cloud products (including BigQuery and Google Sheets using Connected Sheets) to analyze, visualize, and interpret data to answer specific questions and share insights with key decision makers.

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This course continues to explore the implementation of data load and transformation pipelines for a BigQuery Data Warehouse using Cloud Data Fusion.

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This course explores the implementation of data load and transformation pipelines for a BigQuery Data Warehouse using Dataproc.

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Welcome to Optimize in BigQuery, where we map Enterprise Data Warehouse concepts and components to BigQuery and Google data services with a focus on optimization.

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I due componenti chiave di qualsiasi pipeline di dati sono costituiti dai data lake e dai data warehouse. In questo corso evidenzieremo i casi d'uso per ogni tipo di spazio di archiviazione e approfondiremo i dettagli tecnici delle soluzioni di data lake e data warehouse disponibili su Google Cloud. Inoltre, descriveremo il ruolo di un data engineer, illustreremo i vantaggi di una pipeline di dati di successo per le operazioni aziendali ed esamineremo i motivi per cui il data engineering dovrebbe essere eseguito in un ambiente cloud. Questo è il primo corso della serie Data engineering su Google Cloud. Dopo il completamento di questo corso, iscriviti al corso Creazione di pipeline di dati in batch su Google Cloud.

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In this course, you learn how to do the kind of data exploration and analysis in Looker that would formerly be done primarily by SQL developers or analysts. Upon completion of this course, you will be able to leverage Looker's modern analytics platform to find and explore relevant content in your organization’s Looker instance, ask questions of your data, create new metrics as needed, and build and share visualizations and dashboards to facilitate data-driven decision making.

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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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