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Nitish Girkar

成为会员时间：2022

利用 BigQuery ML 构建预测模型时的数据工程处理 Earned Jul 16, 2023 EDT
Data Lake Modernization on Google Cloud: Cloud Composer Earned Jul 16, 2023 EDT
PostgreSQL to Cloud SQL Earned Jul 13, 2023 EDT
Data Warehousing for Partners: Stream Data with Pub/Sub Earned Jul 10, 2023 EDT
Build Streaming Data Pipelines on Google Cloud Earned Jul 6, 2023 EDT
Build Batch Data Pipelines on Google Cloud Earned May 23, 2023 EDT
Build Data Lakes and Data Warehouses on Google Cloud Earned May 7, 2023 EDT
Google Cloud Big Data and Machine Learning Fundamentals Earned Apr 20, 2023 EDT

完成中级技能徽章课程利用 BigQuery ML 构建预测模型时的数据工程处理， 展示自己在以下方面的技能：利用 Dataprep by Trifacta 构建 BigQuery 数据转换流水线； 利用 Cloud Storage、Dataflow 和 BigQuery 构建提取、转换和加载 (ETL) 工作流； 以及利用 BigQuery ML 构建机器学习模型。

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Welcome to Cloud Composer, where we discuss how to orchestrate data lake workflows with Cloud Composer.

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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 PostgreSQL to CloudSQL using the Database Migration Service.

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

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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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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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