The explosion of cloud data costs is generally caused by unoptimized queries that scan too much data, overly frequent refreshes, and a lack of partitioning. To address this, companies must implement table partitioning and clustering, optimize data warehouse configurations (such as Snowflake or BigQuery), and empower teams through a FinOps approach to analytical cost tracking.
The Quarter the Bill Went Out of Control
In the offices of the Sidi Maârouf business district in Casablanca, the surprise was brutal for this major Moroccan retailer. Upon opening the billing report for its cloud infrastructure, the CFO found that the bill for data storage and analysis had multiplied fivefold in a single quarter. Yet, the company had not hired any new analysts, and sales volumes had not experienced any exceptional spike. This is the classic scenario of cloud data cost drift—a reality faced by many Moroccan companies migrating their infrastructure to modern technologies without adapting their operational governance.
The transition to managed data warehouses promises flexibility and virtually unlimited computing power. However, this lack of physical limits quickly turns into a budgetary trap when consumption is not rigorously controlled. Unlike legacy physical servers installed on-premise, where costs were fixed and predictable, modern platforms charge based on consumed resources, compute seconds, or terabytes of data read. A single technical misstep can trigger an immediate and significant price hike.
The Queries That Cost the Most Without Anyone Knowing
The primary cause of this budget explosion often lies in the very formulation of the queries written by analysts or automatically generated by visualization tools. In a traditional environment, using the global analysis command is a common habit. On a platform like Google BigQuery, this command forces the system to scan the entire table for every column, billing the equivalent of several gigabytes or even terabytes for a simple routine check. A poorly optimized query repeated every hour by a dashboard can cost several thousand dirhams per month on its own.
Similarly, poor configuration of Snowflake optimization can keep disproportionately large virtual compute warehouses active for minor tasks. If the auto-suspend delay is too long, the company pays for unused compute power running idle. Training technical teams to write efficient, cost-effective queries is the first line of defense against budget waste.
Partitioning and Clustering: The Quick Wins
To immediately reduce resource consumption without impacting team workflows, it is essential to structure data tables intelligently. This is where partitioning and clustering come into play. Partitioning consists of breaking down a large table into smaller segments, for example, by transaction date or geographic region. Thus, when an analyst looks for last month's sales in Casablanca, the query engine only scans the relevant partition instead of scanning the company's entire ten-year history.
Clustering complements this approach by sorting data within each partition based on columns frequently used in filters. During our interventions with major clients such as Marjane Holding or Label'Vie, the systematic implementation of these two mechanisms instantly divided the volume of data scanned by daily queries by ten. This translates into an immediate and proportional drop in the monthly bill, while significantly improving dashboard loading times for decision-makers.
Over-Frequent Refreshes and Forgotten Tables
Another source of substantial savings lies in the critical analysis of data pipelines. Very often, business teams request real-time or hourly data refreshes for indicators that are only consulted once a week. Every pipeline execution consumes bandwidth and compute power. Reducing the refresh frequency to a daily rhythm or aligning it with the actual needs of the business significantly lightens the load on the infrastructure.
Furthermore, the data lifecycle is rarely managed end-to-end. Test environments created by developers for one-off projects frequently remain active and stored indefinitely. These forgotten temporary tables accumulate silent storage costs month after month. Applying automatic deletion rules on temporary tables and cold-archiving rarely accessed historical data are simple actions with high added value.
Tracking Costs by Team to Foster Accountability
Sustainable control of cloud data costs cannot be achieved without transforming the company's internal culture, often referred to as a FinOps data approach. It is crucial to move away from the mindset where only the IT department has visibility into spending. By implementing resource tagging systems by project, department, or use case, it becomes possible to accurately allocate costs to each consuming team.
When a marketing or finance department receives a monthly report detailing its exact share of consumption in the global cloud budget, behaviors naturally change. Teams become active participants in their own optimization. At Data Scale Business, we support companies in setting up these budget tracking dashboards and defining automatic alert thresholds to stop cost overruns before they turn into bad surprises at the end of the month. Regaining control of your data means, above all, mastering its cost to guarantee an optimal return on investment.
