Introducing 
Express — Go from prompt to pipeline in minutes.
In the rapidly evolving world of data engineering, Nexla stands out in the latest Gartner Emerging Tech Impact Radar on Artificial Intelligence, recognized for its pivotal role in empowering “citizen data engineers.”
Citizen data engineers represent a new class of data users who bring domain expertise from various business sectors to working with data. While they may lack deep technical backgrounds, they excel in understanding the nuances of their respective domains’ data. Nexla steps in to fill the gap, providing intuitive solutions that simplify the intricacies of data integration and pipeline management, bringing together domain experts with the technological context required for effective delivery and utilization of their data assets.
Nexla was highlighted as an emerging vendor in this report because of its ability to streamline the creation and maintenance of any data pipeline with minimal technical overhead. By offering user-friendly interfaces, universal connectors to every data system, and powerful no/low-code transformation tools, Nexla empowers citizen data engineers to leverage data effectively for informed decision-making within their domains.
As organizations increasingly recognize the value of citizen data engineers, Nexla’s role becomes more pronounced. By focusing on simplicity and accessibility, Nexla ensures that data engineering tasks can be efficiently carried out by professionals across various business domains. Gartner predicts major adoption of the idea of the role of citizen data engineers will occur in the next three to five years, but early adopters will reap even greater benefits.
Inclusion in the Gartner report underscores Nexla’s mission to democratize data engineering and empower data-driven decision-making. As organizations embrace the value of data, Nexla is already empowering not just citizen data engineers, but data engineers, business users, data leaders, and more to participate in the data process.
Gartner Subscribers can read the full report here: https://www.gartner.com/document/5124832
Agentic RAG systems fail when data is fragmented, stale, or inconsistent. Learn how AI-ready data products with standardized schemas, governance, and retrieval metadata enable reliable, scalable RAG applications.
In episode five of DatAInnovators & Builders, GrowthX founder Marcel Santilli explains the delegation test for AI and why poor context, not weak models, is the real reason AI initiatives fail to scale.
AI systems fail when context doesn’t scale. This article explains the limits of context graphs, why static relationships break for enterprise AI, and what’s needed to deliver accurate, trustworthy AI outputs at scale.
