Real-Time Data Processing Pipeline & Visualization with Docker, Spark, Kafka

and Cassandra

Roberto G. Hashioka – 2016-10-04 – TIAD – Paris

Personal Information

• Roberto Gandolfo Hashioka

• @rogaha (Github) e @rhashioka (Twitter)

• Finance -> Software Engineer

• Growth & Data Engineer at Docker

Summary

• Background / Motivation

• Project Goals

• How to build it?

• DEMO

Background

• Gather of data from multiple sources and process them in “real-time”

• Transform raw data into meaningful and useful information used to enable more effective

decision-making process

• Provide more visibility into trends on: 1) user behavior 2) feature engagement 3) opportunities

for future investments

• Data transparency and standardization

Project Goals

• Create a data processing pipeline that can handle a huge amount of events per second

• Automate the development environment — Docker compose.

• Automate the remote machines management — Docker for AWS / Machine.

• Reduce the time to market / time to development — New hires / new features.

Project / Language Stack

How to build it?

• Step 1: Install Docker for Mac/Win and dockerize all the applications

link: https://www.docker.com/products/docker

Exemplo de Dockerfile-----------------------------------------------------------------------------------------------------------

FROM ubuntu:14.04

MAINTAINER Roberto Hashioka (roberto@docker.com)

RUN apt-get update && apt-get install -y nginx

RUN echo “Hello World! #TIAD” > /usr/share/nginx/html/index.html

EXPOSE 80

------------------------------------------------------------------------------------------------------------

$ docker build –t rogaha/web_demotiad2016 . $ docker run –d –p 80:80 –-name web_demotiad2016 rogaha/web_demotiad2016

How to build it?

• Step 2: Define your services stack with a docker-compose file

Docker Compose

containers: web: build: . command: python app.py ports: - "5000:5000" volumes: - .:/code links: - redis environment: - PYTHONUNBUFFERED=1 redis: image: redis:latest command: redis-server --appendonly yes

How to build it?

• Step 3: Test the applications locally from your laptop using containers

How to build it?

How to build it?

• Step 4: Provision your remote servers and deploy your containers

How to build it?

How to build it?

• Step 5: Scale your services with Docker swarm

DEMOsource code: https://github.com/rogaha/data-processing-pipeline

Open Source Projects Used• Docker (https://github.com/docker/docker)

• An open platform for distributed applications for developers and sysadmins

• Apache Spark / Spark SQL (https://github.com/apache/spark)

• A fast, in-memory data processing engine. Spark SQL lets you query structured data as a resilient distributed dataset (RDD)

• Apache Kafka (https://github.com/apache/kafka)

• A fast and scalable pub-sub messaging service

• Apache Zookeeper (https://github.com/apache/zookeeper)

• A distributed configuration service, synchronization service, and naming registry for large distributed systems

• Apache Cassandra (https://github.com/apache/cassandra)

• Scalable, high-available and distributed columnar NoSQL database

• D3 (https://github.com/mbostock/d3)

• A JavaScript visualization library for HTML and SVG.

Thanks!Questions?

@rhashioka