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Nitish Girkar

成为会员时间：2022

使用 BigQuery ML 為預測模型進行資料工程 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

完成使用 BigQuery ML 為預測模型進行資料工程技能徽章中階課程， 即可證明自己具備下列知識與技能：運用 Dataprep by Trifacta 建構連至 BigQuery 的資料轉換 pipeline； 使用 Cloud Storage、Dataflow 和 BigQuery 建構「擷取、轉換及載入」(ETL) 工作負載， 以及使用 BigQuery ML 建構機器學習模型。

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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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