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This year, many data teams have added AI agents to their roadmaps. The excitement is real: an agent that turns a two-day analysis into a two-minute conversation can change how analysts and business teams work together. But agents are only as reliable as the data foundation beneath them. Point them at raw tables or outdated
Building Trustworthy Snowflake AI Agents with Semantic Governance Read More »
AWS (Amazon Web Services) has explained how Bluesight developed Prism, an AI layer that connects hospital pharmacy and compliance data across its product suite. Prism Assistant for ControlCheck has reached general availability and operates across 20 health systems, according to AWS vendor statements. Meanwhile, a multi-product agent for 340B Group Purchasing Organisation (GPO) compliance remains
AWS and Bluesight build AI for hospital 340B compliance Read More »
A recent AWS GraphRAG deployment reduced drug research and development cycles in pharmaceutical environments by 87 percent. This acceleration is achieved by integrating previously separated proprietary databases into a unified and queryable knowledge graph. Historically, initial data gathering and screening phases took over six months per iteration, yielding a low five percent success rate. Crucial
AWS GraphRAG deployment cuts drug research cycles by 87% Read More »
SAP aligns fragmented commerce data structures to enable operational AI personalisation at the execution layer. Enterprise leadership routinely establishes objectives to anticipate customer requirements and deliver relevant interactions across digital touchpoints. However, the actual infrastructure running inside these enterprises fails to support systematic execution at the required volume. Recommendation engines display generic product listings because
Suppose you want to locate a particular piece of information in a library that is the size of a city. This is a predicament that businesses have to deal with on a daily basis regarding their electronic data. They contain giant quantities of logs, documents, and user actions. Locating what is important is like trying
Data Engineering is not just about moving data from point A to point B. In 2026, data engineers are expected to design scalable, reliable, cost-efficient, and analytics-ready data systems that support real-time decision making, AI workloads, and business intelligence. Modern data engineers work at the intersection of distributed systems, cloud platforms, big data processing, and
30+ Data Engineer Interview Questions and Answers Read More »
Job descriptions of Data Engineering roles have changed drastically over the years. In 2026, these read less like data plumbing and more like production engineering. You are expected to ship pipelines that don’t break at 2 AM, scale cleanly, and stay compliant while they do it. So, no – “I know Python and Spark” alone
Imagine stepping into your first data science interview—your palms are sweaty, your mind racing, and then… you get a question you actually know the answer to. That’s the power of preparation. With data science reshaping how businesses make decisions, the race to hire skilled data scientists is more intense than ever. For freshers, standing out
Top 100 Data Science Interview Questions and Answers (2026 Edition) Read More »
Data science powers decision-making across modern businesses, from data preparation and automation to advanced analytics and machine learning. Learning it requires a strong foundation in mathematics, statistics, programming, and practical problem-solving. The good news is that data science can be self-learned with the right resources and consistent practice. Books remain one of the most effective
Today, I’ll tell you about our ETL migration from AWS Glue to the Snowflake and dbt modern data stack. My team’s mission is to centralize and ensure data reliability from multiple business domains. We build an analytical foundation to support product and strategic teams’ decision-making. As part of this effort, we migrated 28 gold models
