dataaaaaa!

The NYC dbt Meetup convenes data professionals to discuss analytics engineering, modern data stacks, DataOps, transformation modeling, and testing. Sessions focus on implementing software engineering best practices like modularity, version control, and documentation within transformation workflows.

Stockholm MLOps hosts its Winterbash meetup for production AI professionals, focusing on architecture and operational tooling. The technical gathering features six local startups presenting eight-minute demos and architecture pitches on machine learning pipelines, deployments, and MLOps platforms.

The 11th official dbt Seoul meetup features technical sessions focusing on modern data engineering workflows. Presentations highlight building semantic layers using MetricFlow and navigating the architecture challenges of migrating financial data platforms to Snowflake in the cloud.

The Paris Apache Airflow Meetup covers engineering patterns for data pipelines and AI workloads. A technical session details using the Airflow Common AI provider to orchestrate RAG, SQL generation, and multi-turn agents within DAGs, utilizing durable execution and human-in-the-loop validation.

Deploying AI models onto production data platforms requires moving beyond prototypes to manage runtime failures, testing, and continuous quality evaluation. Data engineers must navigate infrastructure costs, operational trade-offs, and monitoring to maintain production-grade pipelines.

Product Analytics Talks convenes data and AI practitioners to discuss analytical decision-making, experimentation, and AI agents. Alex Litvinov provides a hands-on engineering perspective on building data and AI solutions, addressing the technical realities behind implementation and scaling.

This meetup convenes practitioners using ClickHouse for real-time observability, analytics, and production-scale workloads. Sessions focus on architectural best practices and scaling strategies from engineers at Common Room, Fiveonefour, and Outreach pushing the boundaries of real-time data.

Technical talks examine Apache Iceberg architectures, including V4 metadata restructuring for streaming and portability. Engineers detail migrating from AWS Glue to Snowflake Horizon via REST catalogs, governing AI data with Lakekeeper, and running multi-engine lakehouses across DuckDB, Spark, and

The Toronto ClickHouse Meetup features technical discussions on ClickHouse, a column-oriented DBMS built for fast OLAP workloads capable of processing billions of rows per second. Sessions highlight real-world analytical database applications, observability, and AI integration with Rootly AI.

This Databricks meetup covers multi-agent architectures using Genie One and Unity Catalog. Sessions focus on establishing semantic context layers via Metric Views and dbt query history, enforcing governance guardrails over agent data access, and deriving automated lineage across pipelines.

O meetup de dbt em Sao Paulo foca em engenharia de analytics e transformacao de dados com SQL. A programacao técnica inclui modelagem do mercado segurador a partir de arquivos CSV, atualizacoes do Summit e discussoes sobre o impacto da IA nas stacks modernas de dados, testes e DataOps.

The meetup covers integrating agentic spec-driven development into dbt workflows to improve data modeling and issue tracing. Another session highlights an architecture using Snowflake, dbt, and Power BI to ingest raw operational data and AI traces, using dbt to transform and test trusted models.

Databricks Lakebase integrates PostgreSQL into the lakehouse ecosystem to bridge operational databases with analytics and AI workloads. This architecture eliminates data silos, minimizes pipeline complexity, and provides unified, secure data access across modern data engineering environments.

Chennai Data Circle is hosting CDC50 - CONFLUENCE, a community gathering for data and AI practitioners. The technical program features live speaker demo sessions and a roundtable discussion evaluating core data engineering fundamentals against an AI-first approach for modern architectures.

Data platforms must evolve to handle agent-scale traffic characterized by high query volume, frequency, and zero latency tolerance. Architecture discussions focus on supporting automated AI queries rather than human workloads, leveraging ClickHouse to eliminate lag and prevent confident incorrect

The Chicago meetup features a technical session on building reliable data pipelines with Lakeflow Pipelines in Databricks. Lead Data Engineer Matthew Kurtz shares practical insights on data ingestion, pipeline reliability, and complex transformations for modern data engineering platforms.

A gathering for data and analytics engineers features discussions around data pipelines, data quality, and open-source tooling. Hosted by Orchestra and Lightdash, an open-source AI-native analytics platform, the technical meetup brings practitioners together to discuss maintaining clean data.

A breakfast panel for data professionals in Copenhagen explores real-world data and AI initiatives, focusing on what data teams are shipping, operational bottlenecks, and evolving engineering workflows. Analytics and BI leaders discuss emerging technical practices and everyday pipeline challenges.

The Apache DataFusion meetup highlights query engine performance and integration into modern data infrastructure. Technical talks cover reading Apache Parquet efficiently, implementing OpenLineage metadata tracking, building serverless log engines, and managing AI search and compaction workflows.

The dbt community gathering highlights technical presentations on dbt Charts and implementing Jev models in dbt. Sessions focus on analytics engineering workflows, data modeling patterns, and integrating visualization layers directly into the dbt transformation ecosystem.

Open Source After Work at SAP Labs focuses on open data, AI, and modern data architectures. Topics cover open-source data engineering tools, including Confluent Tableflow, Apache Iceberg, and Nessie, alongside real-world cloud platform implementations using Dremio and STACKIT to optimize demand

Connecting an analytics agent directly to a data warehouse often fails, requiring dedicated context layers between the model and the underlying data. Maintaining verifiable answers relies on keeping these context layers fresh as business logic evolves and keeping human oversight in the loop.

The Apache Iceberg meetup highlights key data lakehouse patterns. Roku presents migrating Hive tables to Iceberg using Apache Gravitino and a REST catalog for multi-cloud governance without breaking Spark or Trino pipelines. Snowflake covers semantic interoperability using Apache Ossie and Polaris.

Modern data infrastructure often fails non-human consumers due to siloed sources and poor discoverability. As highlighted, "The model isn't the problem.

A discussion on lakehouse architectures covers the latest Apache Iceberg release and its native support in open-source Unity Catalog. Key topics include production-impacting Iceberg updates, interoperability considerations, and practical AI workflows for modern data engineering platforms.