Enterprise Customer Data Platform - Treasure Data Blog - Part 33

Customer Highlight: Bringing Data Science to Mobile Shopping at Wish

Wish needed scalable data infrastructure that worked right out of the box. Back in early 2012, the recommendations based shopping app had grown from a data science experiment at ContextLogic into a full web and mobile e-commerce platform......

Wrangling Huge .CSVs Made Simple (with a little help from SQL)

Let's say you have a very large .csv (containing more than 80k rows) that you want to analyze and visualize. Usually, given only Excel and Tableau (or Looker, Chartio, etc.), you would have one of two options: Put it into Excel and run pivot tables, charts, and whatnot, OR Ingest the data di...

Dimensionality Reduction Techniques: Where to Begin

When dealing with huge volumes of data, a problem naturally arises. How do you whittle down a dataset of hundreds or even thousands of variables into an optimal model? How do you visualize data through countless dimensions? Fortunately, a series of techniques called dimensionality reduction aim to...

Importing and Exporting Google Spreadsheets with Treasure Data

Despite various business intelligence vendors’ campaigns against Excel and similar programs, spreadsheets continue to be the most popular business intelligence tool. In fact, they are more than a BI tool: Bec...

Opening up the Great Wide Open, Atlanta, March 16-17, 2016

It's not often the case that you get to combine both a cutting-edge technology conference with a market that, in many ways, is only just waking up to the possibilities of open source analytics. But that's exactly what the Great Wide Open Conference (GWOC) is and I'm glad I got to attend this year!...

The Analytics Hierarchy of Needs

Inspired by Maslow's Hierarchy of Needs, I wanted to create a similar idea around data science and analytics. Too many times do I see individuals putting the cart before the horse and trying to build a complex predictive analytics pipeline when they don't even know the basics of their customer's usa...

A Self-Study List for Data Engineers and Aspiring Data Architects

With the explosion of “Big Data” over the last few years, the need for people who know how to build and manage data-pipelines has grown. Unfortunately, supply has not kept up with demand and there seems t...

SQL is Not Dead and How to Make it Part of Your Toolbox

Even as SQL draws ever closer to being half a century old, it continues to be relevant for the majority of the business world. One reason for SQL's continued reign as the main data query language is its ease of use. Back when SQL was being built at IBM, their goal was to create a language that mirro...

Evaluating Analytics SaaS’s Raw Data Access Capabilities

Why Raw Data Access for Analytics SaaS? If you are a data analyst or data-driven product manager, you must have hit the limits of your analytics tools at one point. For example: You noticed that a particular customer's product usage spiked, and you wanted to track them event by event but your t...

Microsoft Offers a Faster, More Efficient R, But Is it Right for You?

In early 2015, Microsoft announced its successful acquisition of Revolution Analytics, which made R available as an enterprise ready statistical and data science solution. Initially, Microsoft stated that they would be using this acquisition to seamlessly integrate the power of the R language into M...

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Forrester Study: The Total Economic Impact™ of Arm Treasure Data Download Report