Month: December 2018

Talend Performance Tuning Strategy

As a Customer Success Architect with Talend, I spend a significant amount of my time helping customers with optimizing their data integration tasks – both on the Talend Data Integration Platform and the Big Data Platform. While most of the time the developers have a robust toolkit of solutions to address different performance tuning scenarios, […]


7 Factors for a Successful Deployment

Deploying a successful technology solution, especially in data management, takes more than just installing software and writing a job (or multiple jobs… thousands in some cases), and running those jobs. If you’re taking on a new data management initiative, deploying using containers and serverless technology, migrating from traditional data sources to Hadoop, or from on-premises […]


An Introduction to the Service Mesh

In the last few years, microservices or microservice architecture has become a popular reference in IT due to its benefits and the flexibility this architectural style brings. Before we get into working with microservices and Talend, we should review the basics of microservices or a microservice architecture. In a previous blog from Ravi Chebolu, he provided […]


How to Architect, Engineer and Manage Performance (Part 1)

This is the first of a series of blogs on how to architect, engineer and manage performance.  In it, I’d like to attempt to demystify performance by defining it clearly as well as describing methods and techniques to achieve performance requirements. I’ll also cover how to make sure the requirements are potentially achievable. Why is […]


Talend Cloud: A hybrid-friendly, secure Cloud Integration Platform

As enterprises move towards massively scaled interconnected software systems, they are embracing the cloud like never before. Very few would dispute the notion that the cloud has become one of the biggest drivers of change in the enterprise IT landscape and that the cloud has provided IT a powerful way to deploy services in a […]


Data Matching with Different Regional Data Sets

When it comes to Data Matching, there is no ‘one size fits all menu’. Different matching routines, different algorithms and different tuning parameters will all apply to different datasets. You generally can’t take one matching setup used to match data from one distinct data set and apply it to another. This proves especially true when […]