Introducing 
Express — Go from prompt to pipeline in minutes.
Governed self-service data embeds metadata controls, quality guardrails, and access policies. This enables business users to explore and transform data in no-code while preventing metric drift.
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.
Customer API and CSV feeds create engineering bottlenecks. Learn how to standardize raw customer data into governed, reusable data products using Common Data Models—eliminating custom integrations and scaling onboarding.
Raw feeds without context create endless rework. This metadata-first blueprint shows how to turn changing source feeds into governed, reusable data products with automated validation, lineage, and GenAI-ready contracts.
Essential checklist for validating AI-ready data before building LLM pipelines. Learn the 10 critical steps ML teams must follow to ensure quality, freshness, and compliance.
Context engineering is the systematic practice of designing and controlling the information AI models consume at runtime, ensuring outputs are accurate, auditable, and compliant.
Nexla brings 500+ pre-built data connectors to Microsoft 365 Copilot, enabling organizations to easily integrate internal and third-party data for smarter AI workflows.
Learn how to choose the right automated ETL tools for your startup. Discover key selection factors, compare top platforms, and find solutions that scale with your business growth.
Poor data management can cost organizations 15–20% of revenue. Reusable, scalable data products help—but only if they’re consistent and reliable. A Common Data Model (CDM) standardizes and structures data, ensuring accuracy, scalability, and long-term value.
Fivetran and Nexla are leading data integration platforms, but they take different approaches. Learn how they compare on features, deployment, and governance to find the right fit for your data strategy.
Dive into Apache Iceberg’s benefits. Reliable pipelines need strong operations, from catalog options to lakehouses. Learn about time travel, schema evolution, and best practices for scalable maintenance with Nexla.
Discover how Apache Iceberg separates storage from compute to improve modern data lake performance. See how Nexla supports CDC into Iceberg and also uses Iceberg as a source to streamline data integration and analytics.
