IDUG EMEA 2026

Data lakes have become a foundational component of modern data platforms, enabling organizations to store and process large volumes of structured, semi-structured, and unstructured data with high scalability and flexibility.
For data architects and database administrators, the challenge lies not only in designing a robust data lake architecture but also in ensuring consistent performance, governance, and operational reliability across diverse workloads.
A well-designed data lake architecture typically follows a layered approach, incorporating ingestion, storage, processing, metadata management, and access layers.
Data is ingested through batch processing and streaming pipelines into cost-effective, distributed storage systems, based on object storage.
Processing frameworks support transformation and enrichment, while metadata and catalog services provide schema management, lineage, and discoverability. Increasingly, modern architectures adopt lakehouse patterns to bridge the gap betwe