Spark 2.0 Python Machine Learning examples

⭐ CLI tool to launch Spark jobs on AWS EMR

An AWS based solution using AWS CloudWatch and AWS Lambda based on Python to automatically terminate AWS EMR clusters that have been idle for a specified period of time.

The goal of this project is to offer an AWS EMR template using Spot Fleet and On-Demand Instances that you can use quickly. Just focus on writing pyspark code.

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):

EMR Cost Calculator

Cloud-based AI / ML workflow and data application development framework

A Grafana-based application to assist Big Data infrastructure optimization initiatives where Spark applications are a dominant cost driver

A collection of airflow sample workflows for data processing on aws

A Spark application, written in Python, to figure out strongly connected components with Bi-directional Label Propagation algorithm. This project implemented an 1.3GB Twitter network dataset on AWS EMR cluster.

Create Data Lake on AWS S3 to store dimensional tables after processing data using Spark on AWS EMR cluster

A cookiecutter template for working with PySpark on AWS EMR

Data Engineering Project with Terraform, Spark, AWS, Docker, Airflow and other tools

My AWS Playground

EMR + Hadoop to Redshift ELT workflow using spark steps API and orchestrated by Apache-Airflow, which ingests disparate datasets focused around 7Gb of I94 arrivals information to produce a simple star schema in Redshift

Daily Incremental load ETL pipeline for Ecommerce company using AWS Lambda and AWS EMR cluster, Deployed using Apache airflow in a docker container.

Lambda to start EMR and run a map reduce job

Generic python library that enables to provision emr clusters with yaml config files (Configuration as Code)

We Build an ETL pipeline using Airflow that accomplishes the following: Downloads data from an AWS S3 bucket, Runs a Spark/Spark SQL job on the downloaded data producing a cleaned-up dataset of delivery deadline missing orders and then Upload the cleaned-up dataset back to the same S3 bucket in a folder primed for higher level analytics

Analysed New York City's Yellow taxi data set with Big Data tools such as Hadoop, HBase, Sqoop, MapReduce and AWS Cloud Infrastructure.