Enterprise Customer Data Platform - Treasure Data Blog - Part 36

February 17, 2016

Elasticsearch, and How to Make it Faster for ALL Queries

With millions of downloads since launch, Elasticsearch has become the open-source tool of choice for full text search. Also, it has become increasing popular for analytics use cases thanks to its ease of setup and JSON-based RESTful API. Like any other technology, however, Elasticsearch excels i...

February 16, 2016

Build a Simple Recommendation Engine with Hivemall and Minhash

This is a translation of this blog post, printed with permission from the author. In this post, I will introduce a technique called Minhash that is bundled in Treasure Data's Hivemall machine learning library. Minhash is not usually thought of as a machine learning technique, but as you will see...

February 11, 2016

Customer Highlight: Ingesting Massive Volumes of Data at Grindr

Grindr was a runaway success. The first ever geo-location based dating app had scaled from a living room project into a thriving community of over 1 million hourly active users in under 3 years. The engineering team, despite having staffed......

February 10, 2016

What’s the difference between Amazon Redshift and Aurora?

As you plan your analytics and data architecture on AWS, you may get confused between Redshift and Aurora. Both are advertised to be scalable and performant. Both are supposedly better than incumbents. Both have optically inspired names. So, what's the difference? In short, Redshift is OLAP where...

February 8, 2016

Raw SQL Access to Segment.com Data in Real-time

What's Segment? Segment is a cloud service that simplifies the process of integrating cloud services into your website and mobile apps. You can say it's a “tag management 2.0” tool to integrate multiple marketing and product management analytics tools with a single SDK. Once the raw data is flo...

February 3, 2016

Bringing Machine Learning to Elasticsearch with Logz.io and Treasure Data

As organizations build up their data infrastructure, we often see different, divergent pipelines sprouting up to accommodate respective needs. This leads to data silos as well as analytics silos: one team builds its own data pipeline for themselves......

February 1, 2016

Graduate from Mixpanel: Tracking Your Funnel Long Term

This post is part two of a two part series. Read part one here. As we saw with Part 1, we were able to create a funnel that allowed us to easily change the period of time and events that defined the funnel, and then visualize that funnel using R. However, like most business driven analytics, we w...

January 28, 2016

Fueling 2016 with Team Growth (Hint: We’re Hiring)

The Muse recently ranked TD as one of the top 15 companies to work for this year. It’s a great honor to be recognized, and we believe our company stands out from other startups based on our team, our products, and our core values......

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