Cutting Cloud Data Costs with Databricks: 5 Strategies
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Cutting Cloud Data Costs with Databricks: 5 Strategies
By Brian Wineland
Cloud costs can get out of control quickly. You start with a few workloads, a few terabytes of data, and the plan to “scale as needed.” Before long, your monthly bill grows, and no one is exactly sure why. If your team runs analytics or AI workloads on Databricks, this scenario may sound familiar.
Databricks is one of the most capable platforms for big data processing and machine learning. It can handle massive datasets, complex transformations, and real-time analytics. The problem is that it will consume as many resources as you allow. Without active governance, that flexibility can become a financial liability.
At DataStrike, we work with SMBs and mid-market enterprises to take control of their data infrastructure costs. Across industries, we see the same patterns: clusters running for days without jobs, storage ballooning from neglected data, workloads using premium compute when cheaper options would be just as effective. The good news is that these problems are solvable. Once you implement the right strategies, cost savings compound month after month. Here are five strategies we use to help clients cut Databricks costs while making their data platform faster, more efficient, and easier to maintain.
1. Match Compute Resources to Workload Demand
One of the fastest ways to reduce Databricks costs is to ensure your compute power scales in lockstep with demand. Databricks supports autoscaling, which adjusts cluster size in real time based on job load. This means you scale up during heavy processing, but also scale down (or shut off entirely) when workloads are idle.
Beyond autoscaling, choosing the right type of compute instance can yield huge savings. For example:
- On GCP, preemptible VMs offer similar savings.
- On Azure, low-priority VMs can drastically cut costs for non-critical workloads.
These are ideal for batch jobs, ETL processes, or exploratory analytics where brief interruptions are acceptable. The trade-off is that these instances can be reclaimed at any time, so your jobs must be fault-tolerant. We don’t just “turn on” autoscaling, we tune it for your exact workload patterns. Our engineers implement checkpointing and retry logic so that if a spot instance is reclaimed, your job resumes without losing progress or racking up unnecessary charges. We also profile your workloads to determine when premium compute is justified and when low-cost alternatives will do the job just as well. Over time, we adjust configurations as your data needs evolve, ensuring you’re never overpaying for compute. The payoff can be significant, organizations leveraging spot-powered configurations have cut compute costs by up to 25% while also slashing storage costs by over 80%.
2. Accelerate SQL Workloads with Photon
If your Databricks jobs are SQL-heavy, enabling Photon can be one of the highest ROI moves Photon is Databricks’ next-generation query engine, built in C++ and optimized for modern CPU architectures. It often delivers 2–3x faster query execution compared to the default engine. Faster queries mean less compute time, which translates directly into cost savings. In many client environments, enabling Photon has cut runtime costs by 40% or more.
The biggest gains typically come with workloads involving large aggregations, complex joins, and wide datasets. Even better, upgrading to the latest Databricks Runtime with Photon support is a relatively low-effort change. We run before-and-after benchmarks to quantify the impact of Photon on your specific workloads. Not every job benefits equally, so we prioritize the ones that will yield the biggest bang for your buck. We also manage compatibility testing, rollout, and runtime upgrades, so your teams can start saving without disruption. And because we continuously monitor performance, we can identify new workloads that would benefit from Photon over time.
3. Optimize Storage with Delta Lake and Intelligent File Management
While compute often gets the blame for high Databricks bills, storage is the silent budget killer. Poorly structured datasets can inflate costs by forcing Databricks to read far more data than necessary. Migrating to Delta Lake for structured datasets is one of the most effective fixes. Delta Lake delivers:
- ACID transactions for reliability.
- Schema enforcement to maintain data quality.
- Faster read/write operations compared to plain Parquet or CSV.
- Optimizations like Z-Ordering, data skipping, and efficient partitioning to reduce the amount of data scanned.
Another common (and costly) problem is the small files issue. Spark must open each file individually, and if you have thousands of tiny files, job execution slows and costs rise. Databricks’ OPTIMIZE command can consolidate these into fewer, larger files, improving performance and lowering costs. We manage the end-to-end Delta Lake migration, including schema design, partitioning strategy, and index optimization. We also set up automated file compaction schedules to keep small-file sprawl under control. By combining storage efficiency with query optimization, we not only lower your costs but also make your analytics teams more productive by delivering faster query times.
4. Establish Cost Monitoring and Alerts
You can’t fix what you can’t measure. While Databricks offers native cost and usage tracking, integrating these metrics into your cloud provider’s billing tools, like AWS Cost Explorer, Azure Cost Management, or GCP Billing, gives you a complete picture of where your money is going. The most effective cost control happens before waste becomes a large bill. That means catching:
- Idle clusters still running.
- Jobs with unusually long runtimes.
- Spikes in resource usage that don’t match expected patterns.
Setting up real-time alerts allows you to act quickly when something’s off. We build automated monitoring workflows that do more than just send alerts. When an anomaly is detected, our systems can trigger remediation actions like shutting down clusters, throttling workloads, or notifying the right stakeholders instantly. We also conduct scheduled spend reviews with your team so you’re always operating with clear, actionable insights into cost trends.
5. Enforce Tagging and Use Job Clusters for Isolation
In multi-team environments, shared all-purpose clusters can be a hidden source of waste. They tend to stay running longer than needed and have multiple teams competing for the same resources, driving up costs. Switching to job clusters for most workloads ensures clusters spin up for a single job, run it, and shut down immediately. This not only eliminates idle costs but also makes it easier to track exactly where costs are coming from. Tagging is the other half of the equation. Every cluster, job, and resource should be tagged with department, project, and environment data. This makes cost attribution clear and enables data-backed budgeting decisions. Our experts design tagging frameworks that integrate directly with your billing systems, making cost reporting automatic and accurate. We also implement job cluster policies aligned with governance rules, ensuring workloads are isolated, costs are tracked per team, and no cluster runs longer than necessary.
Why This Matters for Your Business
Every dollar you overspend on cloud infrastructure is a dollar you can’t invest elsewhere. For SMBs and mid-market companies, keeping Databricks costs under control can be the difference between staying on budget and facing an unexpected cloud bill that blows up your financial forecast. The strategies above save money while also promoting a faster data platform that's more reliable, and more predictable. That’s something every organization can use. In fact, according to recent research, organizations deploying Databricks have achieved, on average, a 42% reduction in total cost of ownership and a 58% reduction in infrastructure costs, while boosting query performance by 65%. through smarter compute and storage optimization With DataStrike’s managed services, you’re not just reacting to cost spikes; you’re proactively engineering them out of your environment.
Our services include:
- Architecture and Deployment: Custom Databricks setups tailored to your performance, security, and compliance needs.
- Performance and Cost Audits: In-depth reviews that identify quick wins for immediate ROI.
- Automation and Governance: Idle cluster termination, autoscaling, and tagging policies baked into your workflows.
- Data Pipeline Optimization: Photon enablement, Delta Lake tuning, and efficient partitioning strategies.
- Ongoing Managed Services: Continuous monitoring, patching, and cost optimization as your data needs change.
With DataStrike managing your Databricks platform, you get the agility of a modern cloud data stack, without the runaway costs.
Final Thoughts
Databricks is a transformative platform for organizations that need to process and analyze large datasets at scale. But without a strong cost management strategy, it can just as easily become a runaway expense. By aligning compute resources with demand, leveraging Photon for faster queries, optimizing storage, monitoring spend in real time, and enforcing strong governance, you can keep costs under control while improving performance. At DataStrike, our mission is to help you get the most out of every dollar you spend on Databricks.
About DataStrike
DataStrike is the industry leader in 100% onshore data infrastructure services and enables companies to harness IT changes as a catalyst for growth. With a network of highly specialized experts, strategic partnerships with the world's biggest technology providers, and a platform agnostic approach, DataStrike provides innovative solutions and practical guidance to accelerate digital transformation initiatives and drive better business outcomes for small-to mid-sized businesses. Click here to learn more about our service offerings.
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