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Category: gcp

  • CPU Market Share in the Cloud (May 2025) – Detailed Analysis

    CPU Market Share in the Cloud (May 2025) – Detailed Analysis The landscape of CPU market share within the cloud computing sector continues to evolve rapidly in May 2025. Driven by the ever-increasing demand for scalable and efficient cloud services, the competition among CPU vendors is intensifying. This analysis delves deeper into the key players… Read more

  • Various MCP Servers and Cloud Availability

    Companies Developing MCP Servers and Cloud Availability A growing number of companies are actively developing and deploying MCP (Model Context Protocol) servers to integrate their services with AI agents. Many of these servers are designed to run in or interact with cloud environments. Companies with Developed MCP Servers (Examples) Technology Platforms Cloudflare: Provides infrastructure for… Read more

  • Detailed Review of GCP Low-Code Platform

    Detailed Review of GCP Low-Code Platform While Google Cloud Platform (GCP) doesn’t market a single, unified “low-code platform” in the same vein as Microsoft Power Apps, it offers a suite of tools and services that empower users with varying technical skills to build applications and automate processes with minimal coding. The primary low-code offering from… Read more

  • AI Agent with Short-Term Memory on Google Cloud

    AI Agent with Short-Term Memory on Google Cloud Creating AI agents capable of handling complex tasks and maintaining context requires implementing short-term memory, often referred to as “scratchpad” or working memory. This allows agents to temporarily store and process information relevant to their immediate goals. Google Cloud Platform (GCP) offers a range of services that… Read more

  • AI Agent with Long-Term Memory on Google Cloud

    AI Agent with Long-Term Memory on Google Cloud Building truly intelligent AI agents requires not only short-term “scratchpad” memory but also robust long-term memory capabilities. Long-term memory allows agents to retain and recall information over extended periods, learn from past experiences, build knowledge, and personalize interactions based on accumulated history. Google Cloud Platform (GCP) offers… Read more

  • Google Bigtable Index Strategies and Code Samples

    Google Bigtable Index Strategies and Code Samples While Bigtable doesn’t have traditional indexes, its row key design and data organization are crucial for achieving index-like query performance. Here’s a breakdown of strategies and code examples to illustrate this. 1. Row Key Design as an “Index” The row key acts as the primary index in Bigtable.… Read more

  • Detailed Airflow Task Types

    Detailed Airflow Task Types Detailed Airflow Task Types for Orchestration Airflow’s strength lies in its ability to orchestrate a wide variety of tasks through its rich set of operators. Operators represent a single task in a workflow. Here are some key categories and examples: Core Task Concepts At its heart, an Airflow task is an… Read more

  • Processing Data Lakehouse Data for Machine Learning

    Processing Data Lakehouse Data for Machine Learning Processing Data Lakehouse Data for Machine Learning Leveraging the vast amounts of data stored in a data lakehouse for Machine Learning (ML) requires a structured approach to ensure data quality, relevance, and efficient processing. Here are the key steps involved: 1. Data Discovery and Selection Details: The initial… Read more

  • Processing Data Lakehouse Data for Agentic AI

    Processing Data Lakehouse Data for Agentic AI Processing Data Lakehouse Data for Agentic AI Agentic AI, characterized by its autonomy, goal-directed behavior, and ability to interact with its environment, relies heavily on data for learning, reasoning, and decision-making. Processing data from a data lakehouse for such AI agents requires careful consideration of data quality, relevance,… Read more

  • Building a GCP Data Lakehouse from Ground Zero

    Building a GCP Data Lakehouse from Ground Zero Building a GCP Data Lakehouse from Ground Zero: Detailed Steps Building a data lakehouse on Google Cloud Platform (GCP) involves leveraging services like Google Cloud Storage (GCS), BigQuery, Dataproc, and potentially Looker. Here are the detailed steps to build one from the ground up: Step 1: Set… Read more

  • Integrating with Google BigQuery: Real-Time and Batch mode

    Integrating with Google BigQuery: Real-Time and Batch Integrating with Google BigQuery: Real-Time and Batch Google BigQuery offers various methods for integrating data in both real-time (streaming) and batch modes, catering to different data ingestion needs. Real-Time (Streaming) Integration Real-time integration focuses on ingesting data as it is generated, making it available for near immediate analysis.… Read more

  • Comparing BI Offerings: AWS, Azure, and GCP

    Comparing BI Offerings: AWS, Azure, and GCP Comparing Business Intelligence (BI) Offerings: AWS, Azure, and GCP Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) are the leading cloud providers, each offering a comprehensive suite of services for Business Intelligence (BI) and data analytics. While there’s feature overlap, they also have distinct strengths.… Read more

  • Moving Data from GCP Data Lake to Salesforce Using Real-Time Events

    Moving Data from GCP Data Lake to Salesforce Using Real-Time Events Moving Data from GCP Data Lake to Salesforce Using Real-Time Events Moving data from a Google Cloud Platform (GCP) data lake into Salesforce in real-time based on events typically involves monitoring events within the GCP data ecosystem and triggering updates or creations of records… Read more

  • Real-Time Ingestion of Salesforce Data into GCP Data Lake

    Real-Time Ingestion of Salesforce Data into GCP Data Lake Real-Time Ingestion of Salesforce Data into GCP Data Lake Ingesting data from Salesforce into Google Cloud Platform (GCP) in real-time for a data lake typically involves leveraging event-driven architectures and GCP’s data streaming and integration services. Here are the primary methods: 1. Salesforce Data Cloud with… Read more

  • Stream Data Processing in GCP

    Stream Data Processing in GCP Google Cloud Platform (GCP) offers a robust set of services designed to handle continuous, real-time data streams for various analytics and event-driven applications. Core GCP Services for Stream Data Processing: 1. Cloud Pub/Sub The foundation for reliable and scalable stream processing pipelines on GCP. It’s a fully managed, real-time messaging… Read more

  • GCP Specific Tech Stacks for AI Context Management

    GCP Specific Tech Stacks for AI Context Management Sample Tech Stack 1: For a Large-Scale NLP Application with Knowledge Graph Integration on GCP Knowledge Graph: Google Cloud Knowledge Graph Vector Embeddings: Vertex AI Feature Store Consider Compute Engine or Vertex AI Workbench for open-source libraries (FAISS, Annoy, ChromaDB). Explore Vertex AI Matching Engine for managed… Read more

  • Advanced Java Garbage Collection Tuning

    Advanced Java Garbage Collection Tuning Optimizing the JVM’s garbage collection (GC) is a critical aspect of ensuring high performance, low latency, and stability for Java applications, especially those handling significant loads or requiring stringent response times. 1. Understanding Garbage Collection Goals Before tuning, you need to define your application’s performance goals. The primary goals of… Read more

  • GCP AI Offerings – Details & Use Cases

    GCP AI Offerings – Details and Use Cases GCP AI Offerings – Details and Use Cases Google Cloud Platform (GCP) offers a comprehensive suite of AI and Machine Learning services, ranging from pre-trained APIs to platforms for building and deploying custom models, including cutting-edge Generative AI capabilities. Generative AI Services: Vertex AI Gemini Models Access… Read more

  • AWS DynamoDB vs Azure CosmosDB vs GCP Bigtable & Firestore

    AWS NoSQL vs Azure NoSQL vs GCP NoSQL AWS NoSQL vs Azure NoSQL vs GCP NoSQL Feature Amazon DynamoDB Azure Cosmos DB Google Cloud Firestore Google Cloud Bigtable Data Model Primarily Key-Value and Document Multi-model: Document, Key-Value, Wide-Column (Cassandra API), Graph (Gremlin API), Table (Table API) Document-oriented Wide-column (Column-family) Scalability Highly scalable, automatic partitioning (Partitioning)… Read more

  • Top 20 GCP Cloud Interview Questions and Detailed Answers

    Top 20 GCP Cloud Interview Questions and Detailed Answers 1. Explain Google Cloud Platform (GCP) in your own words. What are its key differentiators compared to AWS and Azure? GCP is Google’s suite of cloud computing services, built on their global infrastructure. Key differentiators include its high-performance global network, strengths in data analytics and machine… Read more

  • Extending Redis Caching Cluster Across Multiple Clouds

    Extending Redis Caching Cluster Across Multiple Clouds Yes, a Redis caching cluster can be extended across multiple cloud providers, but it comes with complexities and trade-offs. Here’s a breakdown of the approaches and considerations for 2025: Methods for Extending Redis Clusters Across Multiple Clouds: Redis Cloud Multi-Cloud: Managed Service: Redis offers a fully managed multi-cloud… Read more

  • The Costs and Benefits of a Multi-Cloud Strategy

    The Costs and Benefits of a Multi-Cloud Strategy (April 2025) Are the Costs of a Multi-Cloud Strategy Worthwhile? (April 2025) Adopting a multi-cloud strategy, which involves using services from two or more cloud providers (like AWS, Azure, and GCP), presents both compelling benefits and potential cost implications. Determining if the costs are “worthwhile” depends heavily… Read more

  • Developing Generative AI Applications with Microservices

    Microservices architecture, with its focus on building applications as a suite of small, independent services, offers a compelling approach to developing complex Generative AI applications. By breaking down the intricate workflows of GenAI into manageable components, microservices can enhance scalability, flexibility, and maintainability. 1. Why Microservices for Generative AI? 2. Potential Microservices for a Generative… Read more

  • Event-Driven Microservices Overview

    Event-driven microservices represent an architectural pattern where independent services communicate with each other through asynchronous events. Instead of direct, synchronous calls, a service publishes an event when a significant state change occurs, and other interested services subscribe to and react to these events. This decoupling offers several advantages in building scalable and resilient systems. 1.… Read more

  • Comparative Analysis: Cost Saving Strategies in AWS, GCP, and Azure

    Optimizing cloud costs is a continuous effort for any organization leveraging AWS, Google Cloud Platform (GCP), or Microsoft Azure. While all three providers offer a pay-as-you-go model, significant savings can be achieved through strategic planning and utilizing platform-specific cost optimization features. This analysis compares the key cost-saving strategies across these cloud giants. 1. Discount Programs… Read more

  • Developing Aptitude and Skills for an AI-Focused Tech Career

    A career in Artificial Intelligence is dynamic and rewarding, but requires a specific blend of aptitude and learned skills. This guide outlines key areas to focus on to develop the necessary foundation for success in the AI-driven tech landscape. 1. Strengthen Your Foundational Aptitude While skills can be learned, certain inherent aptitudes can significantly accelerate… Read more

  • Building a Hilariously Insightful Image Recognition Chatbot with Spring AI

    Building a Hilariously Insightful Image Recognition Chatbot with Spring AI (and a Touch of Sass)While Spring AI’s current spotlight shines on language models, the underlying principles of integration and modularity allow us to construct fascinating applications that extend beyond text. In this article, we’ll embark on a whimsical journey to build an image recognition chatbot… Read more

  • Deploying a PyTorch model on Vertex AI

    Deploying a PyTorch model on Vertex AI involves several steps. Here’s a breakdown: 1. Prerequisites: 2. Steps Here’s a conceptual outline with code snippets using the Vertex AI Python SDK: 2.1 Upload Model Artifacts First, upload your trained model (house_price_model.pth) and preprocessor to your GCS bucket. 2.2 Create a Serving Container Since you’re using PyTorch,… Read more