Google Cloud Data Engineering Course

Real-time data processing refers to the ability to acquire, process, and interpret data as it is produced, while batch data refers to a static set of data that is processed as a whole. Real-time data comprises two types: event data, which is a record of a change of state, and streaming data, which is a continuous flow of data. Batch processing on GCP leverages its infrastructure to automatically scale the compute resources based on the data volume and processing requirements. By leveraging Dataflow, data pipelines can be built by processing and transforming data in real-time and batch modes.

Evaluating query performance in BigQuery involves several factors, such as input/output sources, communication between nodes (shuffling), computation, outputs (materialisation), capacity and concurrency, and query patterns. Data warehouse optimisation in BigQuery covers critical aspects from fundamental schema, migrating from traditional data warehouses, and building a new analytics platform. Optimising Data warehouses is necessary for managing large datasets, providing timely data access for analytics and reporting, and maintaining high performance in business intelligence activities. BigQuery offers two pricing models for queries: on-demand pricing and capacity-based pricing. The on-demand model provides a shared pool of capacity, while the capacity-based model is recommended when you need more capacity than is available with the on-demand model.

Publish-Subscribe (Pub/Sub) messaging is an asynchronous communication model that helps developers build highly functional and architecturally complex applications in the cloud. This system has four key components: messages, topics, subscribers, and publishers. This training course provides the concepts you need to develop applications that leverage PubSub + Event Brokers. This course will provide a deep dive into PubSub + API (Java/C/. NET), and provide an overview of open APIs and protocols such as MQTT, AMQP, REST, and JMS. All our training is in both online and offline modes, with hands-on sessions.

Industries were modified through the integration of Machine Learning (ML) with Google Cloud Platform (GCP). This dynamic integration not only performs efficient operations but also explores various innovative operations, utilising ML’s potential with GCP’s robust framework to drive businesses forward. It will empower beginners to create sophisticated models efficiently by selecting and tuning automation algorithms through Google’s AutoML simplification. The model development cycle will be collaborated effectively by sharing insights, enabling teams to track changes, and facilitating GCP’s ML platform. A successful business aimed at embracing ML on GCP capabilities, which is not just a strategic move but a necessary step.