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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Earn a skill badge by completing the Streaming Analytics into BigQuery skill badge course, where you use Pub/Sub, Dataflow and BigQuery together to stream data for analytics.
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Complete the intermediate Build a Data Warehouse with BigQuery skill badge course to demonstrate skills in the following: joining data to create new tables, troubleshooting joins, appending data with unions, creating date-partitioned tables, and working with JSON, arrays, and structs in BigQuery.
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Learn how to use Gemini Notebook to create a personalized study guide for the Professional Data Engineer certification exam.
You'll review Gemini Notebook features, create a notebook in Gemini Notebook, and learn how to use a study guide to practice for a certification exam.
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Complete the introductory Derive Insights from BigQuery Data skill badge course to demonstrate skills in the following: Write SQL queries.Query public tables.Load sample data into BigQuery.Troubleshoot common syntax errors with the query validator in BigQuery.Create reports in Data Studio by connecting to BigQuery data.
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Complete the introductory Prepare Data for ML APIs on Google Cloud skill badge to demonstrate skills in the following: cleaning data with Dataprep by Trifacta, running data pipelines in Dataflow, creating clusters and running Apache Spark jobs in Managed Service for Apache Spark, and calling ML APIs including the Cloud Natural Language API, Google Cloud Speech-to-Text API, and Video Intelligence API.
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Complete the intermediate Engineer Data for Predictive Modeling with BigQuery ML skill badge to demonstrate skills in the following: building data transformation pipelines to BigQuery using Dataprep by Trifacta; using Cloud Storage, Dataflow, and BigQuery to build extract, transform, and load (ETL) workflows; and building machine learning models using BigQuery ML.
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Welcome to Cloud Composer, where we discuss how to orchestrate data lake workflows with Cloud Composer.
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Build Data Lakes and Data Warehouses on Google Cloud
Earned sept. 10, 2024 EDT
Streaming Analytics into BigQuery
Earned juil. 26, 2024 EDT
Build a Data Warehouse with BigQuery
Earned juil. 12, 2024 EDT
Build a Certification Study Guide: PDE Exam Prep
Earned juil. 12, 2024 EDT
Derive Insights from BigQuery Data
Earned juil. 11, 2024 EDT
Prepare Data for ML APIs on Google Cloud
Earned juin 27, 2024 EDT
Engineer Data for Predictive Modeling with BigQuery ML
Earned juin 10, 2024 EDT
Data Lake Modernization on Google Cloud: Cloud Composer
Earned jan. 4, 2024 EST