Spark 2.0 Python Machine Learning examples

Spark 2.0 Scala Machine Learning examples

⭐ CLI tool to launch Spark jobs on AWS EMR

Bits of code I use during live demos

AWS Auto Terminate Idle AWS EMR Clusters Framework is an AWS based solution using AWS CloudWatch and AWS Lambda using a Python script that is using Boto3 to 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.

Terraform module to create AWS EMR resources 🇺🇦

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

Use aws-emr and aws-redshift to analyse dataset of adult census of USA

Analyzing Big Data with Amazon EMR

Run a Spark job within Amazon EMR

A collection of airflow sample workflows for data processing on aws

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

AWS EMR Docker integration

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