Learn the Choices and Strategies for Architectural Patterns in Data Integration with Informatica

Enterprises are embracing modern data integration patterns to fuel digital transformation. Not just cost and performance but also agility, scalability, and productivity are on the list of key requirements for enterprise architectures.

For long-term benefits, your architectural strategy should be fluid and dynamic to support changing business needs. By adopting a comprehensive data platform, you can gain flexibility, build repeatable processes, and future-proof your technology stack—all at once.

DBTA held a webinar with Makesh Renganathan, principal product manager, R&D cloud at Informatica, and John O'Brien, principal advisor and CEO, Radiant Advisors who discussed common architectural patterns in data integration

In terms of modern day architecture and integration there are obvious differences before the pandemic and now during, O’Brien noted.

“All these programs continued to move forward, since they were already budgeted in, but the degree of volatility put everyone at risk,” O’Brien said. “We saw a real move to becoming more agile. They needed to get data right in front of the decision makers because things were changing day by day.”

Companies are reapplying strategies to get access to data and empower everyone to be able to see and use this data to become resilient in the business world, O’Brien said.

Some common data management needs include comprehensive connectivity, support for any pattern, support all user personas, simplicity, productivity, elasticity, and flexible consumption, according to Renganathan. Informatica can help companies get the most value from their cloud data management initiatives.

Informatica offers autonomous enterprise scale data management across multi-cloud/inter-cloud and hybrid for all data consumers, Renganathan said.

DataOps goes hand in hand with MLOps as Informatica provides trusted, high quality data for model building and training/retraining. Users can integrate and operationalize ML models at scale within the data pipelines.

An archived on-demand replay of this webinar is available here.