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The Fundamentals of Data Governance - Part 2

In part 1 of my post on data governance fundamentals, I introduced the "5 Ws and 1 H" of problem-solving—"What”, “Why”, “Who”, “When”, “Where”, and “How”— and applied the first three to data governance. This part covers how you can apply the last three pieces and suggest some next steps. Let's get started!

Augmented analytics lets smart analysts jump to conclusions

Jumping to conclusions has always been considered a bad way to do business (and most things in life). It implies making a rash, poorly considered decision without the facts and without an understanding of the wider implications associated with that decision. But there’s a new wave of technology that is changing everything.

Why every software application you're building needs embedded analytics

Recently, I read an article by Jill Dyché which was a wrap-up of TDWI Las Vegas. She talked about speaking to an analytics professional who works for a bank and was building analytics on top of their applications. This comment really struck me because it means the bank’s software vendor is missing out on a great opportunity to create an enormous amount of value for their customer and their own business.

Yellowfin Signals: Discovering Critical Changes in Google Analytics data

In October 2018, we launched two new products into the Yellowfin Suite: Signals, an automated discovery product that discovers critical changes in your data as they happen, and Stories, a data storytelling product which enables users to provide better context to the numbers and create a common, consistent understanding across the organization. What did we do next? Drink our own champagne, of course.

How We Moved from Heroku to Google Kubernetes Engine

In my last post I laid out our reasoning for moving from Heroku to Google Kubernetes Engine (GKE) and other GCP services. Now I'll describe the actual migration process in detail. This isn't designed as a how-to guide for migrating from Heroku to GKE—Google has their own excellent tutorial for that—but rather a description of some of the challenges of migrating real-world production applications and how we overcame them.

Introducing Talend Pipeline Designer

Are you looking to get the most from your digital transformation projects? Do you often wish there was an easier way to integrate all your streaming and cloud data without the complexity of traditional ETL tools? Well, now there is a simple cloud solution for all your data integration challenges. Talend Cloud just got even better with Pipeline Designer, a next-generation data integration design environment for modern data engineering.

Pipeline Designer Use Case: Real-time Retail Analytics

Welcome to Talend's Pipeline Designer, a self-service web UI that makes streaming data integration accessible, easy, and fast. Within this video, I’ll construct a pipeline using customer order data streaming from a website selling women’s clothes and shoes. Using some of Pipeline Designer’s most prominent processors and features, I’ll be able to calculate in real-time a customer's total basket price while also gaining early insight into potentially fraudulent activity.

Why every software application you're building needs embedded analytics

Embedding analytics into applications is a great opportunity for software vendors to create an enormous amount of value for their customers and their own business. To do this well, you can partner with an analytics vendor, but there are three things to look out for if you do.

Firebase Crashlytics and Bugfender: a Step-by-Step Integration Guide

Ever since we started logging with Bugfender back in 2015, we’ve been working towards integration with Firebase, the app development platform created by Google. Firebase is famous for the breadth of its integration libraries and millions of people use the product around the world, drawn to its sleek UI and range of features.