FinOps for data: 7 practices to reduce the cost of your data lake

The original promise of data lakes was tempting: to centralize massive volumes of structured and unstructured data in a single cloud ecosystem at affordable costs. However, without ongoing planning and governance, what should be a strategic asset for business intelligence and Large Language Models (LLMs) often turns into a "data swamp,a disorganized, opaque, and financially uncontrolled repository.

If your cloud bill increases month after month while your data team spends more time "putting out fires" than extracting value from the data, your operation urgently needs a FinOps approach to data.

Why do data lakes become data swamps, and how can we avoid them?

Understanding how a data lake degrades is the first step in stemming the financial drain. The data occurs due to a combination of lack of governance, inefficient pipelines, and disorganized accumulation of files.

The root causes of degradation:

• Uncritical ingestion (the "Save Everything" effect): With the relatively low cost of raw data storage initially, teams begin ingesting raw data (RAW DB) without cleaning, categorization, or predicting its real usefulness.
• Increased complexity and lack of visibility: as volume grows, duplicate data, obsolete files, and datasets without clear owners emerge, making it impossible to predict costs and impacts in a multi-cloud environment.
• Inefficient queries and pipelines: without structural optimization (such as proper partitioning or compaction), simple requests in analytics engines consume excessive CPU, RAM, or computing nodes.
• Lack of lifecycle management (ILM): data that has not been accessed for years remains in the high-performance primary storage layer, generating unnecessary costs.

To avoid this pitfall, the solution is not to stop data collection, but rather to apply a FinOps, uniting finance, engineering, and business to create financial accountability and operational efficiency in the cloud.

7 FinOps practices to reduce costs and organize your data lake

1. Implement a unified Lakehouse architecture

The rigid separation between Data Lakes (for raw data storage) and Data Warehouses (for structured queries) often results in continuous data duplication. Adopting a Lakehouse allows for the consolidation of raw data storage with advanced reporting and analytics management in an integrated interface. Solutions like Skyone Studio unify iPaaS, lakehouse, and data governance, eliminating operational costs derived from managing multiple disconnected tools.

Read also: What is iPaaS? Understand how to connect business systems

2. Automation of data cleaning and standardization

Processing and cleaning data at the input stage drastically reduces the volume consumed in storage and the analytics layer . Use automated data validation workflows to eliminate duplicates, identify noise, and transform unstructured JSON files into optimized structured formats . Automating data cleaning processes prevents useless files from taking up space and consuming scanning resources .

3. File format optimization and compression

Storing raw data in row-oriented formats (such as JSON or CSV) severely increases the cost of analytical queries.

• Converting files to columnar formats (such as Parquet or ORC) reduces storage space by up to 80% through efficient compression.
• In analytical queries, engines read only the requested columns, drastically reducing the amount of data scanned and decreasing the consumption of computational units.

4. Storage lifecycle management (Tiering)

Not all data requires instant, real-time access. Implement automated lifecycle rules to move data from hot storagetocold or archive storageaftera specified period of inactivity.

• Hot Data (Bronze/RAW): Frequent access for transformation and active pipelines.
• Warm Data (Silver/Prepared): periodic queries and reports.
• Cold Data (Gold/Historical): Maintained strictly for compliance, audit, and other audit purposes.

5. Cost governance and visibility by consumption

Visibility is the backbone of FinOps. Without understanding who consumes the resources, optimization becomes impossible.

• Identify the source of costs by tagging queries and buckets by cost center, project, or department.
• Monitor consumption and request logs to identify bottlenecks and misaligned pipelines.
• Utilize strategic tools with transparent pricing and no surprises in variable rates to maintain budget predictability.

6. Intelligent auto-scaling and resource management

Statically allocated computing resources for data processing generate waste when the volume of requests drops. Using platforms that support auto-scaling , adjusting vCPU, memory, and nodes according to the actual load of applications and databases, ensures high performance during peak times (such as accounting closing) without paying for idle capacity during low-traffic periods.

7. Centralized governance and access control

Disorganized access to data is not only a cybersecurity risk, but also a generator of hidden costs. When multiple users execute non-optimized queries directly on the data lake, computing costs inflate. Centralizing access control, integrating corporate environments with secure authentication (such as Single Sign-On and Zero Trust), and defining consumption profiles ensures that the right data is accessed via optimized reports and dashboards (such as integration via Metabase or Power BI).

You may also be interested in: Skyone migrates Populis to Oracle cloud and reduces costs by 38%

Conclusion

The transition from a data swamp to an efficient and optimized ecosystem with FinOps not only reduces infrastructure costs, but also prepares the company for the next strategic step: the secure adoption of Generative Artificial Intelligence and AI Agents.

Working with structured, integrated, and governed data is the basic prerequisite for powering Learning Language Models (LLMs) and autonomous flows without compromising your organization's security, compliance, or budget.

Do you want to eliminate data silos, optimize your infrastructure, and prepare your business for the Age of Artificial Intelligence?

Discover how the Skyone Platform centralizes integrations, lakehouse, cybersecurity, and cloud governance to ensure maximum performance with complete cost predictability.

Written by Skyone