Ruby 101 and Data Collection with Iron.io and Treasure Data

0
0

Here at Treasure Data, we aim to give you all the tools you need to need to ramp up with data collection  – starting with the basics, using a programming language or environment of your choice – as well as the Treasure Data Service itself, and integration with any number of third-party tools.  (If there’s a technology or topic you’d like us to cover, please leave a note in the comments below.)

This post covers the basics of data collection in Ruby, and how to collect data from multiple Iron.io IronWorker tasks running in parallel. (more…)

Hackers’ Alternative to Marketo: Treasure Data + Heroku + SendGrid

0
0

Emails: The Secret Retention Weapon

It is no secret that email marketing continues to be inbound marketers’ best weapon. (For a good overview of customer lifecycle emails, read this great write-up by Patrick McKenzie.) It’s also no secret that there are many fantastic tools to set up and manage your email engine. From enterprise powerhouse Marketo to Mailchimp of the Email Genome Project fame and startups like Customer.io and Dotmailer, you have a wide range of options.

As a developer, though, I always have an itch to scratch. For example, what if I wanted to send an email to all customers who used a particular feature in our web app? I am sure there is a way to do this in Marketo, but wouldn’t it be great if I could write SQL against my event data, pull up the email list, and email them with a targeted, highly relevant message?

(more…)

Tableau and Treasure Data: From Logging to Visualization

0
0

Previously, we looked at how to log events from our very simple Python “Rock, Paper, Scissors” app. While there are many types of logs (error logs, sensor logs, event logs, payment, and CRM logs, to name a few), the principle is effectively the same for any sort of log, regardless of how the log is invoked or what purpose it actually serves.

To glean insights from our data collection efforts, we’ll need to look at ways to visualize our logs. There are many ways to do this, including R studio, D3.js (topics that merit cover in future posts) as well as out-of-the-box solutions including Chartio and Tableau. (more…)

Python 101 for Aspiring Data Nerds

0
0

As a data scientist, or anyone interested in collecting data for that matter, it’s no doubt helpful to know about how to go about collecting the data in your app – data that you’ll want to later query and analyze.

Here, we’ll build an app in Python from A-Z,  iterate on it to make it more robust, and finally add application event logging with Fluentd and Treasure Data. We chose Python because it’s quickly becoming the language of choice among aspiring data scientists. In our examples, we’ll use Python version 2.7. (more…)

What Our Customers Say: Keeping it Real at Treasure Data

0
0

From the earliest days of Treasure Data, we’ve been fortunate to work with some very innovative companies. Our goal has always been to actively learn from our customers rather than relying 100% on our own vision and perspective. We are pleased to have smart, inventive customers who provide feedback to help us set the direction for Treasure Data Service.

In order to encourage ongoing communication and solicit feedback about the company and service, the Treasure Data team recently conducted a product survey among our customers. Survey respondents were entered in a drawing to win one of three Amazon Gift Cards. We’re passionate about the technology and service we provide and are grateful for the feedback. (more…)