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Ihre Kompetenzen in der Google Cloud Console anwenden
Joseph Hernandez
Mitglied seit 2022
Bronze League
1600 Punkte
Daten für ML-APIs in Google Cloud vorbereiten
Earned Dez 21, 2022 EST
Data Warehouse mit BigQuery erstellen
Earned Dez 13, 2022 EST
Build Batch Data Pipelines on Google Cloud
Earned Dez 12, 2022 EST
Build Data Lakes and Data Warehouses on Google Cloud
Earned Nov 2, 2022 EDT
Google Cloud Big Data and Machine Learning Fundamentals
Earned Nov 1, 2022 EDT
Preparing for your Professional Data Engineer Journey
Earned Okt 24, 2022 EDT
Mit dem Skill-Logo zum Kurs Daten für ML-APIs in Google Cloud vorbereiten weisen Sie Grundkenntnisse in folgenden Bereichen nach: Bereinigen von Daten mit Dataprep von Trifacta, Ausführen von Datenpipelines in Dataflow, Erstellen von Clustern und Ausführen von Apache Spark-Jobs in Dataproc sowie Aufrufen von ML-APIs, einschließlich der Cloud Natural Language API, Cloud Speech-to-Text API und Video Intelligence API.
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Mit dem Skill-Logo zum Kurs Data Warehouse mit BigQuery erstellen weisen Sie fortgeschrittene Kenntnisse in folgenden Bereichen nach: Daten zusammenführen, um neue Tabellen zu erstellen, Probleme mit Joins lösen, Daten mit Unions anhängen, nach Daten partitionierte Tabellen erstellen und JSON, Arrays sowie Strukturen in BigQuery nutzen.
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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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This course helps learners create a study plan for the PDE (Professional Data Engineer) certification exam. Learners explore the breadth and scope of the domains covered in the exam. Learners assess their exam readiness and create their individual study plan.
Daten für ML-APIs in Google Cloud vorbereiten
Earned Dez 21, 2022 EST
Data Warehouse mit BigQuery erstellen
Earned Dez 13, 2022 EST
Build Batch Data Pipelines on Google Cloud
Earned Dez 12, 2022 EST
Build Data Lakes and Data Warehouses on Google Cloud
Earned Nov 2, 2022 EDT
Google Cloud Big Data and Machine Learning Fundamentals
Earned Nov 1, 2022 EDT
Preparing for your Professional Data Engineer Journey
Earned Okt 24, 2022 EDT