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Implementing best practices for PySpark ETL jobs and applications.
An end-to-end GoodReads Data Pipeline for Building Data Lake, Data Warehouse and Analytics Platform.
Mass processing data with a complete ETL for .net developers
Provides guidance for fast ETL jobs, an IDataReader implementation for SqlBulkCopy (or the MySql or Oracle equivalents) that wraps an IEnumerable, and libraries for mapping entites to table columns.
A comprehensive guide to building a modern data warehouse with SQL Server, including ETL processes, data modeling, and analytics.
Terraform modules for provisioning and managing AWS Glue resources
This code creates a Kinesis Firehose in AWS to send CloudWatch log data to S3.
This repo will guide you step-by-step method to create star schema dimensional model.
A Python PySpark Projet with Poetry
A declarative, SQL-like DSL for data integration tasks.
An end-to-end Twitter Data Pipeline that extracts data from Twitter and loads it into AWS S3.
Airflow POC demo : 1) env set up 2) airflow DAG 3) Spark/ML pipeline | #DE
Built a Data Pipeline for a Retail store using AWS services that collects data from its transactional database (OLTP) in Snowflake and transforms the raw data (ETL process) using Apache spark to meet business requirements and also enables Data Analyst create Data Visualization using Superset. Airflow is used to orchestrate the pipeline
This is a PHP project which combines ETL with different strategies to extract data from multiple databases, files, and services, transform it and load it into multiple destinations.
A simple in-memory, configuration driven, data processing pipeline for Apache Spark.
Sentiment Analysis of Tweets Using ETL process and Elastic Search
Comms processing (ETL) with Apache Flink.
A data pipeline from source to data warehouse using Taipei Metro Hourly Traffic data
An ETL pipeline where data is captured from REST API (Remotive, Adzuna & GitHub) and RSS feeds (StackOverflow). The data collected from the API is stored on local disk. The files are preprocessed and ETL jobs are written in spark and scheduled in Prefect to run every week. Transformed data is moved to PostgreSQL.