SMOK.iO

Case Study by Nukomeet · 07/2014

SMOKiO - The First Connected Electronic Cigarette.

Project background

Smokio is a French company that develops and sells connected electronic cigarettes. Smokio's affordable and easy-to-use product helps users track their nicotine consumption, follow their progress, monitor their health and check their financial savings.

Each electronic cigarette communicates via Bluetooth Low Energy with an iOS or Android application and sends data about e-cig usage.

Our involvement

The role of Nukomeet was to automate and optimize ETL (extract, transform, load) flow. Vast amount of data gathered by electronic cigarettes needed to be quickly processed in order to provide near real-time statistics.

Data extraction involved integrating various heterogeneous sources; data transformation processes data by transforming them into a proper storage format/structure for the purposes of querying and analysis; finally, data loading describes the insertion of data into the final target database.

For this project we opted for a micro service architecture, i.e. we built several independent and loosely coupled services, each with a specific goal (using Amazon Web Services).

Services we have provided

Extract, Transform, Load

Technologies

We used Clojure, Amazon Kinesis, Amazon Redshift, Amazon S3.

Clojure

Clojure is a modern programming language which is an extremely effective for building scalable software. The language is a perfect choice in the enterprise environment. It’s built on top of Java Virtual Machine.

Amazon Kinesis

Amazon Kinesis makes it easy to collect, process, and analyze real-time, streaming data so you can get timely insights and react quickly to new information. Amazon Kinesis offers key capabilities to cost-effectively process streaming data at any scale, along with the flexibility to choose the tools that best suit the requirements of your application.

Amazon Redshift

Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud.

Impact

Improved and automated ETL process. The result is reduced required resources and the time needed to conduct the extract, transform, load.