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We take security very seriously. The trust our customers place in Exasol depends on the consistency and safety of our product, and we treat every critical vulnerability disclosure accordingly. On …

Answers widely differ when organizations get asked about the meaning of „Sovereign AI”. For some it means a German or European model. For others, it means a European cloud, or …

How Exasol brings database access, external data, JSON, model inference, and dbt into one workflow A support lead asks a straightforward question: which customers are likely to churn this month, …

How dash-server takes an agent from SQL to a running Dash application From SQL to Something People Can Use An engineer wants to see query volume, execution time, and failed …

Learn how to build a Mojo Script Language Container for Exasol, run faster UDFs, and integrate Python with setup steps and performance benchmarks.

Running classification over thousands of records requires balancing accuracy, latency, and cost. Using an LLM to ask a yes/no question (e.g. is this email spam?) over 10,000 records is incredibly …

How the Lakehouse Query Engine, native IMPORT, and Virtual Schemas fit together Start with the architectural decision Analytical data rarely lives in one place. Organizations store some of it in …

Why connectivity, semantic context, access controls, and auditability matter for agentic analytics? The Challenge An AI agent can query enterprise data. The harder question is whether the result can be …

An e-commerce platform logs order events from its checkout API. Each event arrives as JSON: an order ID, customer details, a list of line items, sometimes optional fields depending on …

A retailer wants to train a model to identify potentially fraudulent transactions. The data already lives in Exasol: transaction history, customer attributes, and account activity. A conventional workflow may export …