GoDataFest

Attendees, exhibitors & solutions

Who this event is for

Industry trends

Key trends shaping this market and its wider industry.

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Frequently asked

Glossary

1

Data lakehouse

An architecture that combines cheap object-storage data lakes with warehouse-style tables, transactions and governance. Expect sessions at GoDataFest to compare lakehouse table formats and query engines against classic warehouse stacks.

2

Data contract

A versioned agreement between a data producer and its consumers covering schema, freshness, ownership and breaking-change rules. It is the usual mechanism Dutch data teams discuss when they need to stop pipeline breakage at source.

3

Data mesh

An operating model that pushes domain ownership of data products to business teams, with a self-serve platform underneath. Relevant when a data festival agenda turns to organisational design rather than tooling.

4

Semantic layer

A governed model of metrics and dimensions that sits between raw tables and BI or AI tools so numbers are defined once. Frequently discussed alongside metrics consistency across dashboards and LLM assistants.

5

Feature store

A system that stores and serves machine-learning features for both training and real-time inference, keeping definitions consistent between the two. Feature stores sit at the meeting point of data engineering and ML engineering tracks.

6

MLOps

The practices and tooling for versioning, deploying, monitoring and retraining machine-learning models in production. It covers model registries, CI/CD for models and drift detection.

7

Retrieval-augmented generation (RAG)

A pattern where a large language model answers using documents retrieved from your own index, instead of relying only on training data. RAG sessions usually focus on chunking, embeddings, evaluation and access control.

8

Vector database

A store optimised for similarity search over embedding vectors, used for semantic search, recommendation and RAG. Trade-offs versus extending an existing search engine or relational database are common discussion points.

9

Data lineage

A traceable map of how data moves from source systems through transformations to reports and models. It underpins impact analysis when a schema changes and is central to data governance tooling.

10

Data observability

Monitoring for data quality and pipeline health: freshness, volume, schema drift, null rates and distribution shifts. Teams use it to catch bad data before it reaches dashboards or models.

11

Reverse ETL

The practice of pushing modelled data from the warehouse back into operational tools such as CRM, marketing automation or support systems. It is how analytics output becomes day-to-day operational action.

12

AI governance

Policies, documentation and controls for how AI systems are built and used, including risk classification, model documentation and human oversight. In the EU this now intersects with the AI Act and GDPR obligations.