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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 help alleviate these issues. These techniques help to ... Dimensionality Reduction Techniques: Where to Begin

The Analytics & Data Science 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 usage and behavior. Just like ... The Analytics & Data Science Hierarchy of Needs

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 to be a shortage of engineers focused on the ingestion and management of data at scale.  Part of the ... A Self-Study List for Data Engineers and Aspiring Data Architects

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 in this post, it’s quite ... Build a Simple Recommendation Engine with Hivemall and Minhash

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 whereas Aurora is OLTP. In this blog post, we’ll ... What’s the difference between Amazon Redshift and Aurora?

Graduate from Mixpanel: Funnel Analysis with SQL and R

This post is part one of a two part series. See part two here. What is Funnel Analysis? In a nutshell, funnel analysis allows you to follow a user through a series of self-defined events as well as, allowing you to calculate the given conversion rates between event to event. There are multiple ways and ... Graduate from Mixpanel: Funnel Analysis with SQL and R

Redshift is 400x Bigger than MySQL Yet MySQL is More Popular

The Amazon Redshift COPY Command Guide is now available! There are good reasons for the hype around Amazon Redshift. Redshift is blazing fast and not that much more expensive than MySQL or PostgreSQL, the traditional mainstay of data engineers. But is Amazon Redshift really becoming predominant in the world of analytic databases, taking over its ... Redshift is 400x Bigger than MySQL Yet MySQL is More Popular

Move your data – from MySQL to Amazon Redshift (in less time than it takes to ignore an index!)

Redshift, as you may already know, is quickly gaining broad acceptance, especially among consumers of free software like MySQL and PostgreSQL, for its “pay as you go” pricing model. However, the same pricing model can still make it a very expensive one. Not all queries need to be done against the Redshift instance itself, as ... Move your data – from MySQL to Amazon Redshift (in less time than it takes to ignore an index!)

Elasticsearch vs. Hadoop For Advanced Analytics

A Tale of Two Platforms Elasticsearch is a great tool for document indexing and powerful full text search. Its JSON based Domain Specific query Language (DSL) is simple and powerful, making it the defacto standard for search integration in any web app. But is it good as an analytics backend? Are we looking at a ... Elasticsearch vs. Hadoop For Advanced Analytics

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