Talk @ TIAD, 04/10/2016, Paris by Roberto Hashioka, Docker. #tiadparis

1. Real-Time Data Processing Pipeline &
Visualization with Docker, Spark, Kafka
and Cassandra
Roberto G. Hashioka – 2016-10-04 – TIAD – Paris

2. Personal Information
• Roberto Gandolfo Hashioka
• @rogaha (Github) e @rhashioka (Twitter)
• Finance -> Software Engineer
• Growth & Data Engineer at Docker

3. Summary
• Background / Motivation
• Project Goals
• How to build it?
• DEMO

4. 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

5. 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.

6. Project / Language Stack

7. How to build it?
• Step 1: Install Docker for Mac/Win and dockerize all the applications
link: https://www.docker.com/products/docker

8. 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

9. How to build it?
• Step 2: Define your services stack with a docker-compose file

10. 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

11. How to build it?
• Step 3: Test the applications locally from your laptop using containers

12. How to build it?

13. How to build it?
• Step 4: Provision your remote servers and deploy your containers

14. How to build it?

15. How to build it?
• Step 5: Scale your services with Docker swarm

16. DEMO
source code: https://github.com/rogaha/data-processing-pipeline

17. 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.

18. Thanks!
Questions?
@rhashioka