Data Replication, Batch and Streaming Best Practices to Boost Analytics and AI

According to a survey of data leaders, 55% report more than 1,000 sources of data at their organization. As a result, data engineering has become critical to properly manage high volumes of data and extract value from it.

To keep up with growing demand, data engineering has evolved from manual-intensive hand coding to an AI-powered, automated and easy-to-use, no-code/low-code GUI-based approach.

Get our white paper, “6 Data Replication, Batch and Streaming Best Practices to Boost Analytics and AI,” to learn:

• Data engineering challenges and how to overcome them
• Real-world use cases, including how one company improved developer productivity by 80% with real-time data
• How Informatica helps data engineers build autonomous, robust and AI-enhanced data pipelines