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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.

Making API development faster with new Apigee Extensions

As API programs gain traction, we know many companies want to empower developers to quickly build and deliver their API products. To aid them in this effort, we recently announced the availability of new capabilities in Apigee, the enterprise API management platform of Google Cloud Platform (GCP), to help enterprise IT teams speed up their API development. With faster API development within GCP, you can innovate faster and create connected customer experiences, plus increase developer productivity.

6 Strategy Elements for Building Cloud Native Applications

The cloud native paradigm for application development has come to consist of microservices architecture, containerized services, orchestration, and distributed management. Many companies are already on this journey, with varying degrees of success. To be successful in developing cloud native applications, it’s important to craft and implement the right strategy. Let’s examine a number of important elements that must be part of a viable cloud native development strategy.

Why We Moved from Heroku to Google Kubernetes Engine

Until late last year, Rainforest ran most of our production applications on Heroku. Heroku was a terrific platform for Rainforest in many ways: it allowed us to scale and remain agile without hiring a large Ops team, and the overall developer experience is unparalleled. But in 2018 it became clear that we were beginning to outgrow Heroku.

Edge2AI using Cloudera's Enterprise Data Cloud

IoT has been transforming industries like fleet management. Cloudera enables enterprises to gain actionable intelligence in real-time with data captured from the edge using Machine Learning models refined actively over time. This video shows how Cloudera Edge Management and Cloudera Data Science Workbench work seamlessly with Cloudera Flow Management to realize the Edge2AI vision for enterprises.

Microsoft Azure & Talend : 3 Real-World Architectures

We know that data is a key driver of success in today data-driven world. Often, companies struggle to efficiently integrate and process enterprise data for fast and reliable analytics, due to reliance on legacy ETL solutions and data silos. To solve this problem, companies are adopting cloud platforms like Microsoft Azure to modernize their IT infrastructure.

6 Ways to Start Utilizing Machine Learning with Amazon Web Services and Talend

A common perspective that I see amongst software designers and developers is that Machine Learning and Artificial Intelligence (AI) are technologies which are only meant for an elite group. However, if a particular technology is to truly succeed and scale, it should be friendly with the common man (in this case a normal software developer).

All the Ways to Connect Your Azure SQL Data Warehouse with Talend

Azure SQL Data Warehouse (DW) has quickly become one of the most important elements of the Azure Data Services landscape. Customers are flocking to Azure SQL DW to take advantage of its rich functionality, broad availability and ease-of-use. As a result, Talend’s world-class capabilities in data integration, data quality and preparation, and data governance are a natural fit with Azure SQL DW.