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The Paris Apache Airflow Meetup focuses on the technical advancements introduced in Airflow 3.2. Data engineers will explore new orchestration features and real-world use cases for the platform. The event highlights how to use Airflow in enterprise environments to manage complex data pipelines.

dbt integrates with BigQuery, AlloyDB, and BigLake to manage SQL transformations. By utilizing Apache Iceberg, it enables seamless data flow across Spark and BigQuery. The Semantic Layer creates a single source of truth for metrics, ensuring everyone works from the same logic.

AI Shipping Labs is a community for engineers to build and ship AI products through structured accountability circles and building sessions. It focuses on solving real-world problems, offering peer support for overcoming technical blockers and mastering tools like Python for AI engineering.

Databricks event in Paris focusing on data engineering skills, AI, and analytics, featuring technical demos and customer insights to leverage data and streamline governance.

Data teams are transitioning from experimentation to production with agentic analytics, emphasizing the semantic layer as a mandatory foundation. Architectures are shifting toward structured context layers and knowledge graphs rather than simple RAG to reduce hallucinations and improve SQL accuracy.

ClickHouse utilizes LLVM XRay for production hotpatching, allowing engineers to inject logging and profiling via SQL without redeploys. The system also employs specialized inverted indexes to handle massive text processing in observability workloads.

The AI Engineering Buildcamp focuses on constructing production-ready systems through a Documentation Agent project. Engineers learn to index documentation, implement RAG, and transition to tool-using agents. The curriculum covers testing with LLM-based judges, monitoring, and systematic evaluation.

DuckLake aims to simplify data lakehouse architectures by leveraging MotherDuck and DuckDB. Authors Matt Martin and Alex Monahan argue that data lakehouses aren't known for being simple and seek to streamline how engineers manage these complex systems through new patterns and tools.

OpenMetadata meetup in Paris.

Dives are interactive React applications generated through natural language conversations with an AI agent. They support complex data engineering features like drill downs, crossfiltering, and custom animations. If you can describe it, you can build it without writing any manual code.

Analytics agents use iterative loops and chain-of-thought reasoning to navigate complex schemas. By leveraging tools like the MotherDuck MCP Server, agents perform schema exploration and error recovery. Success rates are highest for single-shot queries, while iteration often signals ambiguity.

This meetup focuses on agentic analytics and the technical implementation of AI agents within data stacks. Engineering teams will discuss deploying solutions using Snowflake Cortex, Hex, and open-source tools like nao. The goal is to share real-world feedback on building fully in-house AI agents.

The latest DuckDB release introduces significant updates for data engineers running analytical workloads in production. MotherDuck developers demonstrate key features and architectural changes designed to improve performance. The session focuses on practical demos to help users get up to speed fast.

Data engineers can implement native CI/CD within Snowflake using marketplace applications to replace manual scripts and custom tooling. This approach automates the promotion of database objects while enforcing governance through integrated testing and audit trails.

AI agents often struggle with data persistence, leaving analysis trapped in chat windows. The MotherDuck MCP server introduces the query_rw tool to solve this by enabling agents to save derived tables and store intermediate results.

This masterclass focuses on the engineering of digital visibility within the AI economy. Data professionals learn to navigate algorithmic recommendation layers using a technical optimization checklist.

L'exploitation en temps réel est devenue un standard pour alimenter les architectures data a grande échelle. Ce meetup explore l'usage d'Apache Flink pour construire des systemes Big Data fiables et evolutifs.

ParisDataLadies meetup on integrating AI into existing products, addressing technical challenges such as building variable pricing systems, data engineering for investment decisions, and GenAI in fintech using tools like Tableau and GCP.

Online event on dbt Core 1.11, featuring technical release updates, roadmap insights, and a Q&A session with the team, focusing on reliability and extensibility enhancements for data engineering projects.

Technical meetup focused on open source data infrastructure, featuring practical discussions on Apache Kafka, ClickHouse, PostgreSQL, Apache Iceberg, and OpenSearch to address engineering challenges such as observability and data analysis.

Photoroom employs dbt for rigorous data modeling and Omni for unambiguous exploration, ensuring trustworthy AI analytics by bridging the context gap between data warehouses and business intent.

Workshop on maintaining production data pipelines, focusing on monitoring, automated testing, CI/CD, and failure handling to enhance reliability and scalability.

A technical event covers network security blocks and authentication procedures for developers, including token usage and ticket management.

Modern data engineering is shifting toward user-facing analytics where latency must be measured in milliseconds. Architectures are moving from shared-nothing to compute and storage separation using object storage.

Industry leaders discuss the shift toward framework-defined infrastructure, where code automatically deploys into production-ready environments. Key concepts include BI-as-code, streaming interfaces for AI, and the transition from Cloud 1.0 to AI-native stacks using vector databases and LLMs.