Data lakes promise scalability, flexibility, and cost-effectiveness compared to traditional data warehouses. But if you find yourself with large amounts of unstructured files, duplicate tables, and undocumented pipelines, your “lake” has become a swamp and you have problems with your data lake.
Here are five reasons your data lake might be failing, along with clear, actionable steps you can take to prevent your data lake from becoming a swamp.
1. Lack of metadata governance (You don’t know what’s there)
What’s going wrong:
- No catalog
- No ownership
- Lack of clear documentation.
- 10 versions of the same “customer” dataset with different schemas.
How to fix it:
- Implement governance tools like Unity Catalog (Databricks) or Microsoft Purview (Azure) to clearly organize your metadata.
- Adopt naming conventions and use tagging (if possible) for ownership.
- Implement updates to your metadata in your pipeline, automating this process.
2. Sticking to ETL instead of ELT
What’s going wrong:
- Data is transformed before landing.
- No retention of original raw data.
- Permanent data loss from failed processes.
How to fix it:
- Shift to an ELT strategy where raw data is landed first in formats like Parquet or Delta Lake. Transformations should then occur downstream.
- Use Delta Lake’s versioning and rollback features to safeguard data integrity.
- Manage transformations in tools like DBT or Databricks Notebooks/Workflows to improve debugging capabilities.
3. Using your lake as a file dump
What’s going wrong:
- Unstructured, mixed file formats causing chaos.
- Frequent storage bloating and poor query performance.
- Difficulty maintaining consistency.
How to fix it:
- Standardize your data storage using formats such as Delta, Parquet, or ORC.
- Use partitioning on selected tables to improve query performance while balancing cost.
- Introduce automated schema validation and null checks to maintain data quality upon arrival.
4. Missing lineage and logging
What’s going wrong:
- Difficulty tracing data sources.
- Troubleshooting is complex and time-consuming.
- Dependencies are difficult to manage and debug.
How to fix it:
- Leverage lineage-aware transformation tools like DBT to automatically track and display data lineage.
- Integrate monitoring and audit logging via Azure Data Factory or Databricks.
- Maintain your transformation logic within Git, to maintain traceability and accountability.
5. No one actually uses it
What’s going wrong:
- Analysts just keep on using spreadsheets.
- Stakeholders rely heavily on dashboard exports rather than direct queries.
- General distrust in the data lake’s accuracy and reliability.
How to fix it:
- Develop intuitive semantic layers atop curated data using dbt MetricFlow or Power BI.
- Guide your users towards the clearly defined and reliable “gold-level” dataset.
- Focus on thorough documentation and training to build user confidence and ensure usability across your organization.
Conclusion: Don’t build a swamp
Creating a successful data lake takes a bit more effort than just dumping your files into storage. It requires structure, governance, lineage, usability and clarity. If your lake feels like a swamp, revisit these requirements. Otherwise, your data platform will definitely not be used for it’s full potential and you have problems with your data lake. Some readers might already have noticed that a well-managed data lakes looks very similar to a data lake house. A combination of a traditional data warehouse and data lake. If your data lake is not delivering the results you want, it might be worth looking into implementing a full data lakehouse solution.
