Technology - Treasure Data Blog - Part 16

Technology Category

Data Science 101: Interactive Analysis with Jupyter, Pandas and Treasure Data

In case you were wondering, the next time you overhear a data scientist talking excitedly about “Pandas on Jupyter”, s/he's not citing the latest 2-bit sci-fi from the orthographically challenged! Treasure Data gives you a cloud-based analytics infrastructure accessible via SQL. Our interac...

Apache Flink: General Analytics on a Streaming Dataflow Engine

This is a guest blog from Kostas Tzoumas, of dataArtisans and committer at Apache Flink. Apache Flink® is a new approach to distributed data processing for the Hadoop ecosystem. Flink's approach is to offer familiar programing APIs on top of an engine that has built-in support for: Stream...

The 4 Important Things About Analyzing Data Part 3 & 4: Recognize You Can’t Do It All and Show Your Value

In this blog series highlighting the four important things about analyzing data, so far we've talked about the importance of delivering many obvious results and the need to accurately understand the purpose of the analysis. Throughout the discussions, we've used the word “analysis” to describe t...

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

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. �...

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

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 you...

4 Ways AdTech Uses Treasure Data’s Open Source Software

Like many cloud services, Treasure Data has been a huge beneficiary of open source software. For example, we have adopted and modified Hive and Presto for our distributed processing back end and made several key contributions back to Presto. We run dozens of Chef recipes everyday, and Rails and Angu...

Tableau and Treasure Data: From Logging to Visualization

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...

Python 101 for Aspiring Data Nerds

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...

New UDFs in Presto: currency conversion and geocoding tools

Today, we introduced three new UDFs (user-defined functions) to to Treasure Data's Presto offering. They are: TD_CURRENCY_CONV [crayon-5cb9c76317dde865023405-i/] TD_IP_TO_COUNTRY_CODE [crayon-5cb9c76317de6782027743-i/] TD_IP_TO_COUNTRY_NAME [crayon-5cb9c76317de9769100983-i/] (more&hellip...

Open Source’s Main Advantage is NOT Cost

Let's start with a trick question: How much does MySQL cost Facebook every year? Depending on whom you ask, the answer ranges from “nothing” to “millions of dollars every year.” True, the MySQL software is free; it costs nothing for anyone to download it. What's more, it's free for anyone...

16