1. Introduction
1.1 Survey goal and related work
1.2 Contributions and outline
2. A Brief History of Data Lakes
2.1 2010–2013: Beginnings
2.2 2014–2015: Criticisms and further development
2.3 2016–present: Prosperity and diversity
3. Data Lake Architecture and Proposed Categorization Criteria
3.1 Pond and zone architectures
3.2 Proposed architecture and categorization criteria
3.3 Data lake users
4. Storage
4.1 File-based storage systems
4.2 Single data store
4.3 Polystore systems
4.4 Cloud data lakes
4.5 Summary and discussion
5. Ingestion
5.1 Metadata extraction
5.2 Metadata modeling
6. Maintenance
6.1 Dataset organization
6.2 Related dataset discovery
6.3 Data integration
6.4 Metadata enrichment
6.5 Data cleaning
6.6 Schema evolution
6.7 Data provenance
7. Exploration
7.1 Query-driven data discovery
7.2 Heterogeneous data querying
8. Challenges and Future Directions
8.1 Data lakes in digital business transformation
8.2 Data lakes meet machine learning
8.3 Advanced analytics and transaction management
9. Conclusion and Outlook