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Google Data Engineer: Practice 500+ Imp. Interview Questions
Crack Google Data Engineer Interview : Practice 500+ Most asked Questions with Answers to gain Confidence in Interviews
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english
Online - Self Paced
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Udemy
About Google Data Engineer: Practice 500+ Imp. Interview Questions
Master the essential concepts and tools needed to excel as a Google Data Engineer with this comprehensive course. This course is designed to guide you through 500+ carefully curated interview questions and answers covering the core topics and skills required for a successful data engineering career on Google Cloud Platform (GCP). Whether you're preparing for interviews or strengthening your knowledge, this course provides in-depth insights into data engineering best practices and real-world applications.
Topics Covered:
Cloud Data Storage:
Dive into Google Cloud Storage (GCS), Cloud SQL, Cloud Spanner, and Cloud Bigtable, exploring various storage options, encryption, access control, and data lifecycle management.
Data Processing and Pipelines:
Understand the key principles of data processing using Cloud Dataflow, Cloud Dataproc, and Cloud Composer for managing data pipelines, scheduling jobs, and orchestrating workflows.
Data Ingestion and Streaming:
Learn about Cloud Pub/Sub, a reliable messaging system for data ingestion, along with Data Transfer Services for batch and real-time data migration.
Data Warehousing and Analytics:
Cover BigQuery's features for data storage, analytics, and machine learning (BigQuery ML), along with techniques for query optimization and performance.
Data Orchestration and Workflow Management:
Discover how to automate data workflows with Cloud Composer (Apache Airflow) and Dataflow templates, supporting efficient data orchestration.
Data Migration and Integration:
Explore Google’s Database Migration Service, BigQuery Data Transfer Service, and Cloud Functions to seamlessly transfer and integrate data.
Data Security and Compliance:
Learn how to secure data with IAM, encryption, and VPC Service Controls, ensuring compliance with industry regulations through audit logging.
Machine Learning Integration:
Delve into the integration of machine learning with AI Platform and TensorFlow on GCP, supporting scalable, data-driven model development.
Data Visualization and Business Intelligence:
Master data visualization and reporting with Looker Studio and Looker BI platform, essential for business intelligence and decision-making.
Monitoring and Optimization:
Use Stackdriver Monitoring and Logging to track, analyze, and optimize pipeline performance, ensuring efficiency across your data workflows.
Cost Management and Optimization:
Gain insights into managing costs effectively with strategies for BigQuery, Dataflow, and storage services.
Best Practices and Real-World Scenarios:
Equip yourself with best practices for scalable data pipelines, disaster recovery, high availability, and data governance to handle real-world challenges effectively.
By completing this course, you’ll gain the confidence to tackle Google Data Engineer interviews and be equipped with practical knowledge to excel in data engineering roles on GCP.