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.

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.

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.

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.

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).

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.

Since the release of Talend 7.1 users can build Talend jobs as Docker images and publish them to Docker registries. In this blog post, I am going to run through the steps to publish to the major cloud provider container registries (AWS, Azure and Google Cloud). Before I dig into publishing container images to registries, I am going to remind you the basics of building Talend Jobs in Docker images from Talend Studio as well as point out the difference between a local build and a remote build.

To say that organizations today are facing a complex data landscape is really an understatement. Data exists in on-premises systems and in the cloud; data is used across applications and accessed across departments. Information is being exchanged in ever-growing volumes with customers and business partners. Websites and social media platforms are constantly adding data to the mix.