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DuckCon in Amsterdam focuses on the technical evolution of DuckDB, DuckLake, and Quack. Engineers from organizations like Snowflake and Spotify discuss open-source analytics and data engineering patterns. The event highlights how to learn the latest developments about DuckDB, DuckLake and Quack.

This workshop demonstrates building a reporting layer for AI agents using dltHub Pro to ingest and normalize agent traces. Engineers learn to capture tool calls and token usage into structured schemas.

Data engineering shifts toward open standards like ODCS for data contracts and ODPS for data products to enable automation. By defining output ports and semantic layers in standard-conformant files, engineers can use coding agents to build architectural units. A data contract exists to build trust.

Flights enables data engineers to build and schedule Python pipelines using AI agents. It supports ingestion via MCP servers, SQL table functions, and a dedicated UI. The tool uses Flight Plan templates for production deployment and integrates with Dives to transform raw data into dashboards.

Experts analyze modern data architectures by comparing theoretical models with field realities. Key technical focuses include the maturity of lakehouses, the adoption of Apache Iceberg and open table formats, and the balance between batch and streaming.

Data engineering remains the foundation of effective AI development. Research involving hundreds of controlled experiments shows that well-modeled schemas and clear naming conventions outperform complex RAG pipelines and agent architectures.

The DiveMaxxing contest focuses on interactive data visualizations built using AI agents integrated with MotherDuck. Engineers use Claude or ChatGPT to perform live queries against datasets, moving beyond static charts. One example includes a star map built on ESA Gaia data.

The MotherDuck MCP server enables AI agents to execute SQL queries and build data artifacts directly through conversational interfaces. This shift toward agentic analytics allows for automated data exploration while maintaining human oversight.

Context engineering for data agents is most effective when focusing on well-modeled schemas rather than complex RAG pipelines. Experiments show that clear naming and simple architectures outperform expensive retrieval systems.

The ClickHouse France User Group focuses on high-performance analytical database management. Technical sessions explore real-time data processing and architectural patterns for large-scale telemetry. Attendees discuss optimizing query performance and managing distributed clusters.

The latest dbt updates introduce Fusion to streamline workloads and a Developer Agent to automate tasks. Engineers can leverage new Cost Insights for spend visibility and OAuth for connecting AI tools to governed data. As the team notes, Fusion is ready for your workloads and dbt is getting smarter.

Modern data engineering leverages AI copilots to accelerate the creation of robust pipelines using dltHub. Engineers can automate ingestion from sources like GitHub while maintaining control over production readiness. Elvis Kahoro demonstrates that speed matters, but so does getting it right.

DuckCon Amsterdam features technical sessions and lightning talks focused on the DuckDB ecosystem. The event highlights advancements in the database engine and its integration with modern data stacks. As noted, the program includes a borrel for networking among data engineers and developers.

The Looker France User Group launch in Paris focuses on building a technical community for data professionals to exchange feedback and explore business intelligence trends.

Modern data stacks are shifting from human-centric semantic layers to agent-native context layers. While vendors promote YAML-based standards, an alternative approach uses Python code to define schema contracts and logic.

Modern database connectivity often relies on inefficient row-based protocols. This workshop explores Arrow Database Connectivity (ADBC) to transport data in the Apache Arrow columnar format. By using a single API, engineers can eliminate row-based overhead and switch backends with minimal code.

DuckLake integrates open lakehouse formats with database simplicity by using database-backed metadata to eliminate the small files problem. It supports ACID transactions and hidden partition pruning.

Google Cloud Next highlights advancements in AI and machine learning, application development, and data analytics. Data engineers can leverage new patterns for integrating large-scale datasets with cloud-native architectures to drive intelligence. The event focuses on how these tools empower teams.

The Metabase AI Hackathon encourages developers to build applications using an MCP server, Agent API, and file-based development tools. These features allow engineers to integrate a semantic layer into AI workflows or Slack.

Data agents require more than SQL proficiency to deliver accurate results; they must integrate business logic and semantic definitions. Effective context management involves evaluating trade-offs in formatting and scaling context from individual analysts to entire organizations.

The ClickHouse Paris Meetup focuses on high-performance analytical database engineering. Experts from Equativ and ClickHouse discuss real-world implementations and technical updates. Attendees dive into what is new in the world of ClickHouse to optimize large-scale data processing and storage.

Fivetran and dbt Labs are merging to build open data infrastructure focused on the agentic era. This integration aims to streamline data ingestion and transformation workflows.

The Paris meetup focuses on columnar data formats for in-memory and disk storage. Technical sessions cover zero-materialization merging to solve the small file problem via run-end encoded Arrow manipulation and implementing Arrow specifications in C++20.

Modern data engineering focuses on semantic layers to provide context for AI agents, ensuring reliable self-service by resolving metric ambiguity. Architects must also balance frugal design with AI integration when modeling large datasets, such as filtering millions of entities into qualified

Snowflake adaptive warehouses automate resource scaling to optimize performance and cost. Engineers must understand the specific billing mechanics and configuration nuances to avoid unexpected charges.