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Rolling Retention Done Right in SQL

SaaS’s Most Important Metric: Retention If you are a product manager or in a growth role at a SaaS company, rolling retention (also called cohort analysis) is a key metric to monitor your growth. It’s so important that every analytics service worth their salt (Google Analytics, Mixpanel, Adobe Analytics, Heap Analytics and Amplitude to name few) ... Rolling Retention Done Right in SQL

Routing Data from Docker to Prometheus Server via Fluentd

Prometheus could be similarly configured on Google Cloud Platform, CoreOS or even Kubernetes. Later, we’ll also query Prometheus for that data.

Accurate Sales Forecast for Data Analysts: Building a Random Forest model with Just SQL and Hivemall

Rossman is a pharmacy chain with over 3,000 stores in seven countries within Europe. The manager of each store has been tasked to predict the sales of the store for up to six weeks in advance. Sales of each store depends on various factors such...

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