6 Data Replication, Batch and Streaming Strategies to Boost Analytics and AI

data engineering has evolved from manual-intensive hand coding to an AI-powered

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

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