GCP Data Engineering

  In the realm of data engineering, particularly when working on  Google Cloud Platform (GCP) , the terms EL, ELT, and ETL refer to key processes that facilitate the flow and transformation of data from various sources to a destination, usually a data warehouse or data lake. For a GCP Data Engineer to understand the differences between these processes and how to implement them efficiently using  GCP services .  GCP Data Engineering Training 1. Extract, Load (EL) In EL (Extract, Load), data is extracted from various sources and then directly loaded into a target system, typically a data lake like Google Cloud Storage (GCS) or  BigQuery in GCP .  No transformations occur during this process. EL is commonly used when: The priority is to ingest raw data quickly. Data needs to be stored for later processing. There is a need for data backup, archiving, or unprocessed analytics. GCP Services for EL: Cloud Dataflow:  A fully managed streaming analytics service used to extract data from sources

AI on Google Cloud Platform (GCP) Artificial Intelligence (AI)  on Google Cloud Platform (GCP) refers to a suite of tools and services designed to help businesses and developers build, deploy, and scale AI-powered applications. GCP offers comprehensive AI and machine learning (ML) solutions that cater to various industries, from healthcare and finance to retail and manufacturing. The platform enables businesses to leverage AI to automate processes, gain insights fromfully managed data, and enhance customer experiences.  GCP Data Engineering Training Key Components of AI on GCP 1.       Google Cloud AI Platform The AI Platform is a fully-managed service that allows developers and data scientists to build, deploy, and scale machine learning models. It provides infrastructure and tools for every stage of the machine learning lifecycle, from data preparation and training to deployment and management. The AI Platform supports popular frameworks like TensorFlow, PyTorch, and Scikit-learn, al

  Introduction: Google Cloud Platform (GCP)  offers a powerful suite of tools to build and manage cloud infrastructure, with Virtual Machines (VMs) and Networking being two of its core components. This guide provides an overview of effectively using these features, focusing on creating scalable and secure environments for your applications.  GCP Data Engineering Training Virtual Machines in GCP What Are Virtual Machines? Virtual Machines (VMs) are virtualised computing resources that emulate physical computers. In GCP, VMs are provided through  Google Compute Engine  (GCE),  allowing users to run workloads on Google's infrastructure. VMs offer flexibility and scalability, making them suitable for various use cases, from simple applications to complex, distributed systems. Key Features of GCP VMs Custom Machine Types:  GCP allows you to create VMs with custom configurations, tailoring CPU, memory, and storage to your specific needs. Preemptible VMs:  These are cost-effective, short-