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Aerupilli Bhaskara Rao

Учасник із 2026

Золота ліга

Кількість балів: 2042
Створення сітки даних за допомогою Knowledge Catalog Earned лип. 20, 2026 EDT
Deploy Multi-Agent Architectures Earned лип. 19, 2026 EDT
Streaming Analytics into BigQuery Earned лип. 17, 2026 EDT
Build Batch Data Pipelines on Google Cloud Earned лип. 17, 2026 EDT
Derive Insights from BigQuery Data Earned лип. 8, 2026 EDT
Build Data Lakes and Data Warehouses on Google Cloud Earned лип. 5, 2026 EDT
Build a Data Warehouse with BigQuery Earned лип. 1, 2026 EDT

Пройдіть вступний кваліфікаційний курс Створення сітки даних за допомогою Knowledge Catalog, щоб продемонструвати свої навички створення такої сітки для покращеної безпеки даних, керування ними й пошуку в Google Cloud. Ви потренуєтеся й перевірите свої навички щодо позначення тегами об’єктів, призначення ролей IAM і перевірки якості даних у Knowledge Catalog.

Докладніше

Complete the advanced Deploy Multi-Agent Architectures skill badge to demonstrate skills in the following: building multi-agent systems with ADK, connecting agents with the Agent-to-Agent (A2A) protocol, integrating external tools using the Model Context Protocol (MCP), and deploying a complete multi-agent solution to Agent Engine. Explore other content in the Gemini Enterprise Agent Ready (GEAR) program.

Докладніше

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.

Докладніше

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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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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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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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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