So far, our journey on using Apache Spark with Talend has been a fun and exciting one. The first three posts on my series provided an overview of how Talend works with Apache Spark, some similarities between Talend and Spark Submit, the configuration options available for Spark jobs in Talend and how to tune Spark jobs for performance. If you haven’t already read them you should do so before getting started here.
For the past two years, Snowflake and Talend have joined forces developing deep integration capabilities and high-performance connectors so that companies can easily move legacy on-premises data to a built-for-the-cloud data warehouse. Snowflake, which runs on Amazon Web Services (AWS), is a modern data-warehouse-as-a-service built from the ground up for the cloud, for all an enterprise’s data, and all their users.
Today on July 19, we released Talend Summer ’18, which is jam-packed with cloud features and capabilities.
Talend Data Integration is an enterprise data integration platform that provides visual design while generating simple Java. This lightweight, modular design approach is a great fit for containers. In this blog post, we’ll walk you through how to containerize your Talend job with a single click. All of the code examples in this post can be found on our Talend Job2Docker Git repository. The git readme also includes step-by-step instructions.
Talend Spring ’18 (Talend 7.0) was Talend’s biggest release yet. With improvements to Cloud, big data, governance and developer productivity, Talend Spring ’18 is everything you need to manage your company’s data lifecycle. For those that missed the live webinar, you can view it here.
A common practice in any development team is to conduct code reviews, or at least it should be. This is a process where multiple developers inspect written code and discuss its design, implementation, and structure to increase quality and accuracy. Whether you subscribe to the notion of formal reviews or a more lightweight method (such as pair programming), code reviews have proven to be effective at finding defects and/or insufficiencies before they hit production.
