Found 27 repositories(showing 27)
This project leverages GCS, Composer, Dataflow, BigQuery, and Looker on Google Cloud Platform (GCP) to build a robust data engineering solution for processing, storing, and reporting daily transaction data in the online food delivery industry.
Developed a modern data engineering project on the Uber dataset using Google Cloud Platform (GCP). Built a data model in fact and dimension format, transformed the data using Python, deployed the code on a compute instance, loaded the data onto BigQuery, and created a final dashboard for data analysis and visualization.
jeantardelli
This repo contains code for the exercises and practical examples from the book Data Engineering with Google Cloud Platform
Lakshay-sethi
Uber End-To-End Data Engineering Project on Google Cloud Platform
Explore and create ML datasets. Sample the dataset and create training, validation, and testing datasets for local development of TensorFlow models. Create a benchmark to evaluate the performance of ML. TensorFlow is used for numerical computations, using directed graphs. Getting started with TensorFlow. Explore the TensorFlow python API, build a graph, run a graph, feed values into a graph. Find areas of a triangle using TensorFlow. Learning from tf.estimator. Read from python’s pandas dataframe into tf.constant, create feature columns for estimator, perform linear regression with tf.Estimator framework. Execute Deep Neural Network regression. Use benchmark dataset. Refactoring to add batching and feature creation. Refactor the input. Refactor the way the features are created. Create and train the model, Evaluate the model. Distributed training and monitoring. Create features out of input data. Train and evaluate. Monitor with Tensorboard. To run TensorFlow at scale, use Cloud ML Engine. Package up the code. Find absolute paths to data. Run the python module from the command line. Run locally using GCloud. Submit training job using GCloud. Deploy model. Make predictions. Train on a 1-million row dataset. Feature Engineering. Working with feature columns. Adding feature crosses in TensorFlow. Reading data from BigQuery. Creating datasets using Dataflow. Using a wide-and-deep model.
aniketandhale08
Data engineering project using Uber dataset, involving fact-dimension data modeling and pipeline development. Leveraged Python, Pandas, and GCP tools like BigQuery and Looker Studio for data transformation and visualization.
napoleonka123
Uber data engineering with GCP
No description available
pana-miguel
No description available
legrandtefa53
No description available
mxn170019
No description available
Mihir241000
No description available
bpbpublications
Data Engineering with GCP, By BPB Publications
Mohamed-attia98
No description available
soumya-bhadani
GCP Data Engineering Project: Building and Orchestrating an ETL Pipeline for Online Food Delivery Industry with Apache Beam and Apache Airflow
NishchayAgrawal
No description available
This project leverages GCS, Composer, Dataflow, BigQuery, and Looker on Google Cloud Platform (GCP) to build a robust data engineering solution for processing, storing, and reporting daily transaction data in the online food delivery industry.
swatishilpaherts-ctrl
GCP Data Engineering Project: Building and Orchestrating an ETL Pipeline for Online Food Delivery Industry with Apache Beam and Apache Airflow
No description available
jmartinseller
Using Terraform to create infraestructure on GCP
No description available
dushantpatil407
No description available
anishgillella
No description available
No description available
No description available
No description available
No description available
All 27 repositories loaded