Scan databases and data warehouses for PII data. Tag tables and columns in data catalogs like Amundsen and Datahub

Redshift Python Connector. It supports Python Database API Specification v2.0.

Data pipeline performing ETL to AWS Redshift using Spark, orchestrated with Apache Airflow

Developed a data pipeline to automate data warehouse ETL by building custom airflow operators that handle the extraction, transformation, validation and loading of data from S3 -> Redshift -> S3

🔄 🏃 EtLT of my own Strava data using the Strava API, MySQL, Python, S3, Redshift, and Airflow

Project was based on an interest in Data Engineering, ETL pipeline. It also provided a good opportunity to develop skills and experience in a range of tools. As such, project is more complex than required, utilising dbt, airflow, docker and cloud based storage.

A batch processing data pipeline, using AWS resources (S3, EMR, Redshift, EC2, IAM), provisioned via Terraform, and orchestrated from locally hosted Airflow containers. The end product is a Superset dashboard and a Postgres database, hosted on an EC2 instance at this address (powered down):

A Covid-19 data pipeline on AWS featuring PySpark/Glue, Docker, Great Expectations, Airflow, and Redshift, templated in CloudFormation and CDK, deployable via Github Actions.

This project provides valuable customer sentiment insights for Zomato by tracking and analyzing tweets related to their brand and services.

Example project for consuming AWS Kinesis streamming and save data on Amazon Redshift using Apache Spark

The goal of this repository is to provide good and clear examples of Amazon CLI commands together with Amazon CDK to easily create any AWS services and resources

Zero-ETL integrations - Enable near real-time analytics on petabytes of transactional data

Smart City Realtime Data Engineering Project

Project 3 - Data Engineering Nanodegree

Project 5 - Data Engineering Nanodegree

This project designs and implements an ETL pipeline using Apache Airflow (Docker Compose) to ingest, process, and store retail data. AWS S3 acts as the data lake, AWS Redshift as the data warehouse, and Looker Studio for visualization. [Data Engineer]

building etl pipelines to migrate music json data/ metadata files (semi-structured data) into a relational database stored in AWS Redshift cluster

Redshift script to create a MANIFEST file recursively

Remove duplicates entries from a Redshift cluster

This project provides a comprehensive data pipeline solution to extract, transform, and load (ETL) Reddit data into a Redshift data warehouse. The pipeline leverages a combination of tools and services including Apache Airflow, Celery, PostgreSQL, Amazon S3, AWS Glue, Amazon Athena, and Amazon Redshift.