The Evolution Of Data Analytics ⁘ Data Engineering With AI Agents

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Suri Nuthalapati , Technical Leader - Data ⁘ AI at Cloudera | Founder Trida Labs | Founder Farmioc.

The rise of artificial intelligence(AI) is fundamentally changing the world of data analytics and data engineering. Advanced AI systems—AI agents that autonomously act, starting to change how organizations collect, process and gain insight from data. These AI-driven tools can sift through massive datasets faster and more intelligently than ever before, which enables businesses to unlock value in new ways.

Currently, data analytics and engineering teams rely on traditional and manual workflows. Data engineers build and maintain data pipelines using extract, transform, load (ETL) processes to synthesize data from various sources into data warehouses or data lakes. Data analysts and data scientists then query these data systems with SQL or use business intelligence(BI) tools and machine learning models to find insights.

While this traditional "modern data stack" has enabled tremendous progress, it also has clear limitations. Data preparation tasks—from cleaning messy data to integrating disparate sources—consume a lot of time and effort for data teams. In fact, most data professionals often spend the majority of their time just wrangling data rather than analyzing it—one report found they devote as much as 80% of their time to data preparation​. It's no surprise, then, that between 60% and 73% of all enterprise data goes unused for analytics​. That means so much information is gathered but never transformed into insight that could drive decisions.

Furthermore, many traditional analytics workflows currently operate in batches, creating scheduled reports or regular predictions. Consequently, insights may turn out to be extremely late for fast business decisions. Current developments around AI can alleviate and improve the above-identified data processes.