So far, in our blog series Data Integration Best practices, we have covered the different types of high-level and low-level problems occurring in data integration projects. We have also addressed the different types of integration, the systems that move data and even the pricing aspect of such a project. Ten articles later, we arrived at best practices moving forward. In this last chapter, we are going to talk about some tips that revolve around preparing for and running an integration project.

Talend Cloud is an Integration platform as a service (iPaaS) offering by Talend. It is a fully managed cloud option which has the capabilities of data integration, data stewardship, data preparation, API designer and Tester and Pipeline designer. These tools can be used for lightweight ETL and detecting the schema on the fly. One of the unique features of Talend Cloud is it provides both on premise and cloud execution environments.

Freemium and free trials are common acquisition models in SaaS––but which is the right model for you? Here, 70+ experts share the best way to decide.

In today’s rapidly changing world, software products need to be upgraded frequently and quickly to bring value to customers and users. Software features are continuously developed, tested, deployed, and operated in the production environment. New features are not only developed and tested successfully, but they also deployed and operated without much chaos and disruption.

We know that data is a key driver of success in today data-driven world. In fact, according to Forrester, data and insight-driven businesses are growing at an average of more than 30% annually. However, becoming a data driven organization is not easy. Companies often struggle with speed in accessing and analyzing their data, as well with ensuring delivery of trustworthy data that is free of critical errors.

One of the things I'm really passionate about is data and how it can be used to influence decision-making. But everyone has their own personal biases and opinions that they use to create a narrative and this can affect how they look at data. So even when the data tells us one thing, we may see something completely different.