Modern enterprises generate enormous amounts of data every day. However, legacy infrastructure—whether on-premise Hadoop clusters, traditional SQL servers, or outdated ETL systems—often prevents organizations from tapping into the full value of their data. In such a landscape, the unified data platform stands out as a solution that streamlines

Feature Traditional RPA Databricks AI Automation Data Handling Structured only Structured, unstructured, real-time Cognitive Ability None Yes (LLMs + AI agents) Learning & Adaptation Static Dynamic, data-driven learning Governance & Compliance Manual Built-in with Unity Catalog Scalability Limited to bot licenses Horizontal scale across cloud Use Case Breadth Task-based workflows End-to-end

Redefining data intelligence with unified, AI-driven capabilities.

1. Introduction Organizations generate vast amounts of unstructured data daily, including emails, PDFs, wikis, logs, and various other forms. Traditional machine learning models (LLMs) often struggle to process these data silos effectively at scale due to several challenges, such as latency, governance issues, and token limit constraints. Retrieval-Augmented Generation (RAG)

Designing Intelligent LLM Systems for Real-Time Decision Making As organizations grapple with ever-growing volumes of data and rising expectations for real-time decision-making, agentic systems powered by large language models (LLMs) are emerging as a transformative solution.  Databricks stands out as a versatile platform, seamlessly integrating data processing, governance, and model