Azure Databricks Standard Tier End of Life: What You Need to Know (and Do Next) Azure Databricks Standard Tier is officially reaching end of life, and this change is more than just a pricing update. It marks a strategic shift toward stronger governance, security, and AI-ready capabilities across the Databricks platform. This blog explains what the Standard Tier EOL means, the key timelines to be aware of, and how to prepare for the move to Premium Tier without disruption. It

The Model Context Protocol (MCP) redefines how AI systems connect to tools and data. Learn how MCP simplifies integrations, enhances scalability, and builds a universal standard for AI connectivity.

Dive into our latest insights on AI guardrails and their vital role in transforming your organization’s AI policies into actionable practices. Equip your business with an AI gateway layer for maximum impact!

Learn how to design accurate and efficient incremental data ingestion pipelines with checkpoints, deduplication, and validation best practices.

Databricks recently introduced Agent Bricks, a new framework designed to simplify the creation of enterprise-grade AI agents. In this blog, our CTO Hitesh Sahni shares a hands-on look at how Agent Bricks works, the use cases it supports, and a live demo of building a Knowledge Assistant with real enterprise data. Discover what problems Agent Bricks solves — and where its current limitations lie.

A hands-on guide from EnablerMinds on building intelligent SQL query agents using LangGraph to enable natural language data interaction for non-technical users. Covers architecture, implementation strategies, technical challenges, and business value—showing how AI-powered, multi-agent workflows can democratize data access while ensuring accuracy, governance, and performance in enterprise environments.

Robust data ingestion is the foundation of any modern analytics platform. In this blog, Gauravsingh Pundir shares scalable ingestion techniques, proven patterns, and enterprise-grade best practices to streamline data flow across complex ecosystems.

In high-performing organizations, analytics is expected to inform decisions, create efficiencies, and unlock business value. Yet, many initiatives fall short—not because of technical shortcomings, but due to unclear goals, misaligned expectations, or incomplete data requirements  from the very beginning. 🧩 The quality of your use case outcome depends entirely on the clarity of your inputs—starting with well-defined business and data requirements. Properly scoping analytics u