BRSASGOGoogleGO-BRSAS1.0<ul> <li>Interpret use-cases for real-time streaming analytics</li><li>Manage data events by using the Pub/Sub asynchronous messaging service</li><li>Write streaming pipelines and run transformations where necessary</li><li>Interoperate Dataflow, BigQuery and Pub/Sub for real-time streaming and analysis</li></ul><ul> <li>Experience analyzing and visualizing big data, implementing cloud-based big data solutions, and transforming/processing datasets.</li><li>Google Cloud Big Data and Machine Learning Fundamentals (or equivalent experience).</li><li>Some knowledge of Java</li></ul><p>This class is intended for data analysts, data scientists and programmers who want to build for extraordinary scenarios such as high availability, resiliency, high-throughput, real-time streaming analytics on Google Cloud.</p><h4>Module 1 - Introduction to Processing Streaming Data</h4><p> <strong>Topics:</strong> </p> <ul> <li>Introduction to processing streaming data</li></ul><p><strong>Objectives:</strong> </p> <ul> <li>Explain streaming data processing.</li><li>Describe the challenges with streaming data.</li><li>Identify the Google Cloud products and tools that can help address streaming data challenges.</li></ul><h4>Module 2 - Serverless Messaging with Pub/Sub</h4><p> <strong>Topics:</strong> </p> <ul> <li>Introduction to Pub/Sub</li><li>Pub/Sub push versus pull</li><li>Publishing with Pub/Sub code</li></ul><p><strong>Objectives:</strong> </p> <ul> <li>Describe the Pub/Sub service.</li><li>Explain how Pub/Sub works.</li><li>Simulate real-time streaming sensor data using Pub/Sub</li></ul><h4>Module 3 - Dataflow Streaming Features</h4><p> <strong>Topics:</strong> </p> <ul> <li>Steaming data challenges</li><li>Dataflow windowing</li></ul><p><strong>Objectives:</strong> </p> <ul> <li>Describe the Dataflow service.</li><li>Build a stream processing pipeline for live traffic data.</li><li>Demonstrate how to handle late data by using watermarks, triggers, and accumulation.</li></ul><h4>Module 4 - High-Throughput BigQuery and Bigtable Streaming Features</h4><p> <strong>Topics:</strong> </p> <ul> <li>Streaming into BigQuery and visualizing results</li><li>High-throughput streaming with Bigtable</li><li>Optimizing Bigtable performance</li></ul><p><strong>Objectives:</strong> </p> <ul> <li>Describe how to perform ad hoc analysis on streaming data using BigQuery and dashboards.</li><li>Discuss Cloud Bigtable as a low-latency solution.</li><li>Describe how to architect for Bigtable and how to ingest data into Bigtable.</li><li>Highlight performance considerations for the relevant services.</li></ul><h4>Module 5 - Advanced BigQuery Functionality and Performance</h4><p> <strong>Topics:</strong> </p> <ul> <li>Analytic window functions</li><li>Geographic Information System (GIS) functions</li><li>Performance considerations</li></ul><p><strong>Objectives:</strong> </p> <ul> <li>Review some of BigQuery&rsquo;s advanced analysis capabilities.</li><li>Discuss ways to improve query performance.</li></ul>- Interpret use-cases for real-time streaming analytics - Manage data events by using the Pub/Sub asynchronous messaging service - Write streaming pipelines and run transformations where necessary - Interoperate Dataflow, BigQuery and Pub/Sub for real-time streaming and analysis- Experience analyzing and visualizing big data, implementing cloud-based big data solutions, and transforming/processing datasets. - Google Cloud Big Data and Machine Learning Fundamentals (or equivalent experience). - Some knowledge of JavaThis class is intended for data analysts, data scientists and programmers who want to build for extraordinary scenarios such as high availability, resiliency, high-throughput, real-time streaming analytics on Google Cloud.Module 1 - Introduction to Processing Streaming Data Topics: - Introduction to processing streaming data Objectives: - Explain streaming data processing. - Describe the challenges with streaming data. - Identify the Google Cloud products and tools that can help address streaming data challenges. Module 2 - Serverless Messaging with Pub/Sub Topics: - Introduction to Pub/Sub - Pub/Sub push versus pull - Publishing with Pub/Sub code Objectives: - Describe the Pub/Sub service. - Explain how Pub/Sub works. - Simulate real-time streaming sensor data using Pub/Sub Module 3 - Dataflow Streaming Features Topics: - Steaming data challenges - Dataflow windowing Objectives: - Describe the Dataflow service. - Build a stream processing pipeline for live traffic data. - Demonstrate how to handle late data by using watermarks, triggers, and accumulation. Module 4 - High-Throughput BigQuery and Bigtable Streaming Features Topics: - Streaming into BigQuery and visualizing results - High-throughput streaming with Bigtable - Optimizing Bigtable performance Objectives: - Describe how to perform ad hoc analysis on streaming data using BigQuery and dashboards. - Discuss Cloud Bigtable as a low-latency solution. - Describe how to architect for Bigtable and how to ingest data into Bigtable. - Highlight performance considerations for the relevant services. Module 5 - Advanced BigQuery Functionality and Performance Topics: - Analytic window functions - Geographic Information System (GIS) functions - Performance considerations Objectives: - Review some of BigQuery’s advanced analysis capabilities. - Discuss ways to improve query performance.1 day595.00650.00660.00820.00950.00950.00950.00790.00950.00