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Setting Up a Machine Learning Pipeline on Google Cloud Platform

KDnuggets

There are many ways to set up a machine learning pipeline system to help a business, and one option is to host it with a cloud provider. There are many advantages to developing and deploying machine learning models in the cloud, including scalability, cost-efficiency, and simplified processes compared to building the entire pipeline in-house.

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Google Cloud Pub/Sub: Messaging on The Cloud

ProjectPro

With over 10 million active subscriptions, 50 million active topics, and a trillion messages processed per day, Google Cloud Pub/Sub makes it easy to build and manage complex event-driven systems. Google Pub/Sub provides global distribution of messages making it possible to send and receive messages from across the globe.

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Build ETL Pipelines for Data Science Workflows in About 30 Lines of Python

KDnuggets

Step 3: Load In a real project, you might be loading into a database, sending to an API, or pushing to cloud storage. Now instead of just having transaction amounts, we have meaningful business segments. Here, were loading our clean data into a proper SQLite database. conn = sqlite3.connect(db_name)

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End-to-End Data Pipeline on GCP with Airflow: A Social Media Case Study

RandomTrees

Blog Part 1: Social Media Data Pipeline – GCP Setup and Modeling Introduction In this blog series, I will walk you through a real-world case study I personally worked on, where we built an end-to-end social media data pipeline using Google Cloud Platform (GCP) and Apache Airflow. social-media-env), region (e.g.,

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Data Engineering Roadmap, Learning Path,& Career Track 2025

ProjectPro

Leverage various big data engineering tools and cloud service providing platforms to create data extractions and storage pipelines. Experience with using cloud services providing platforms like AWS/GCP/Azure. The three most popular cloud service providing platforms are Google Cloud Platform, Amazon Web Services, and Microsoft Azure.

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Building End-to-End Data Pipelines: From Data Ingestion to Analysis

KDnuggets

Image by Author Let’s break down each step: Component 1: Data Ingestion (or Extract) The pipeline begins by gathering raw data from multiple data sources like databases, APIs, cloud storage, IoT devices, CRMs, flat files, and more. Data can arrive in batches (hourly reports) or as real-time streams (live web traffic).

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Snowflake vs. BigQuery- Head-to-Head Comparison of Cloud Data Warehouses

ProjectPro

Snowflake vs BigQuery, both cloud data warehouses undoubtedly have unique capabilities, but deciding which is the best will depend on the user's requirements and interests. This blog will present a detailed comparison of Snowflake vs. BigQuery to help you select the best data warehouse solution for your next data engineering project.