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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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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This course introduces the products and solutions to solve NLP problems on Google Cloud. Additionally, it explores the processes, techniques, and tools to develop an NLP project with neural networks by using Vertex AI and TensorFlow.
通过 BigQuery ML 创建机器学习模型
Earned Dec 29, 2024 EST
在 BigQuery 中执行预测性数据分析
Earned Dec 25, 2024 EST
使用 BigQuery 构建数据仓库
Earned Dec 12, 2024 EST
创建和管理 Bigtable 实例
Earned Dec 2, 2024 EST
创建和管理 Cloud Spanner 实例
Earned Nov 5, 2024 EST
Build Data Lakes and Data Warehouses on Google Cloud
Earned Apr 8, 2024 EDT
利用 BigQuery ML 构建预测模型时的数据工程处理
Earned Apr 3, 2024 EDT
Natural Language Processing on Google Cloud
Earned Mar 30, 2024 EDT