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.
了解详情
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。
了解详情
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.
運用 BigQuery ML 建立機器學習模型
Earned Dec 29, 2024 EST
在 BigQuery 執行預測資料分析
Earned Dec 25, 2024 EST
透過 BigQuery 建構資料倉儲
Earned Dec 12, 2024 EST
建立及管理 Bigtable 執行個體
Earned Dec 2, 2024 EST
建立及管理 Cloud Spanner 執行個體
Earned Nov 5, 2024 EST
Build Data Lakes and Data Warehouses on Google Cloud
Earned Apr 8, 2024 EDT
使用 BigQuery ML 為預測模型進行資料工程
Earned Apr 3, 2024 EDT
Natural Language Processing on Google Cloud
Earned Mar 30, 2024 EDT