Cut Analytics Costs with a Dremio Data Lakehouse (and Keep Your BigQuery)

Please note:
Cost reductions and efficiency gains are highly dependent on each organization’s unique workloads and system architecture.

Organizations want the ease and speed of a data warehouse without the runaway costs. As usage grows, bills climb due to repeated scans, data copies, and constant ELT. A lakehouse approach with Dremio changes the cost profile: query data where it lives, govern centrally, and right‑size warehouse usage. SIT, Dremio’s implementation partner in Israel, helps teams make this shift with clear guardrails, measurable savings, and minimal disruption.

What drives warehouse costs up

• Repeated scans of large tables for dashboards and ad hoc analysis
• Materialized copies and derived tables to standardize metrics
• ETL jobs that move data from object storage into the warehouse
• Opaque unit pricing and vendor markups for storage
• Concurrency bursts that trigger autoscaling and more credits/slots

From our latest Webinar: Break Free from Slow, Complex Data Processes with Dremio

How a lakehouse lowers the total cost of data ownership

• Store data once in open formats (Apache Iceberg/Parquet) on low‑cost object storage and query it directly.
• Use Dremio as the SQL engine and semantic layer to avoid constant data movement.
• Offload a large portion of BI and self‑service from BigQuery to Dremio; keep the warehouse for specialized workloads.
• Define governed data products to cut ad hoc normalization and duplicate tables.

Keep SQL and BI workflows

Dremio supports standard SQL and connects to common BI tools. Teams keep their structure query language skills and dashboards—no proprietary DSL or retraining. Governance policies (row/column level) apply consistently across tools.

Manage analytics spend with DCUs

Dremio Cloud engines consume DCUs per hour. You choose an engine size based on workload, then measure and adjust. Transparent unit pricing gives you a lever for cost control.

Instance Type	DCU/hr	On-Demand Price ($/hr)
XX Small	4	$1.56
X Small	8	$3.12
Small	16	$6.24
Medium	32	$12.48
Large	64	$24.96
X Large	128	$49.92
2X Large	256	$99.84
3X Large	512	$199.68

Where savings typically appear

• Dashboard and self‑service offload: Route frequent BI queries to Dremio. Use reflections (smart aggregations/materializations) to reduce repeated scans and CPU time.
• ETL reduction: Replace nightly transforms that only serve reporting with virtual datasets and governed data products.
• Fewer duplicates: Define metrics once in the semantic layer instead of proliferating derived tables.
• Pay once for storage: Keep canonical data in object storage; stop warehousing it by default.
• Analyst offload for exploration: For datasets that fit on a developer laptop, pull a filtered subset from Dremio as an Arrow dataframe and run follow‑on queries locally (e.g., DuckDB). Avoid spinning up cluster compute for every iteration. Promote stable logic back into governed views.

Sizing patterns that control cost

• Exploration: Small engine (16 DCU) during business hours; analysts offload subsets locally for iteration.
• Dashboards: Medium engine (32 DCU) for refresh windows; add reflections to eliminate full rescans.
• Batch transforms: Larger engines scheduled for heavy jobs; shut down outside job windows.
  Practical note: If an engine has zero replicas, first query may incur ~2 minutes of startup. Warm engines before scheduled refreshes.

Data normalization and governance

Use data products to define canonical dimensions and measures once. Apply role‑based access, row/column policies, lineage, and auditing centrally. This reduces duplicated logic across teams, improves consistency, and supports compliance.

A governed workflow that scales

• Explore: Build and test queries in Dremio; when helpful, pull a subset locally for fast iteration.
• Formalize: Promote working logic to a governed view so BI tools consume consistent definitions.
• Accelerate: Add reflections for those views to avoid repeated full scans and lower per‑query cost.

Hybrid with BigQuery

Keep BigQuery for specialized warehouse features and workloads that benefit from them. Shift routine BI and exploration to Dremio on the lakehouse. Result: fewer slots/credits consumed, lower total cost, and retained performance.

From Spend to Speed: SIT’s Approach

Here is our work road map: 

Start with the bill, follow the workload

We sit down with your team and walk through one or two dashboards everyone relies on. We tie line items in your BigQuery bill (credits/slots, storage, ELT runs) to the actual queries and jobs behind them. The outcome is a shared picture of where costs and delays come from—your workloads and their price tags, not guesses.

Choose the right engine size, get faster results

With the hotspots identified, we start Dremio engines sized for the job: small for exploration, medium for dashboard refresh windows, and large only for periodic heavy transforms. We add reflections on the views those dashboards use most, so you stop rescanning the same wide tables. Each engine has a clear purpose, and each reflection cuts unnecessary compute.

Make the lakehouse usable on day one

We land curated data in Iceberg, define a semantic layer so “revenue” means the same thing everywhere, and apply row/column access policies your teams already recognize. BI connects to governed views—no retraining, no broken dashboards. Governance isn’t an afterthought; it’s how the lakehouse stays consistent.

Prove it, then roll it out

We measure before and after using your dashboards and refresh cadence. You see fewer BigQuery credits burned, fewer ELT jobs needed, and faster refresh times. From there, we phase the rollout by business domain, retiring duplicate tables and noisy jobs as each team adopts the governed views. BigQuery stays where it adds value; Dremio takes the day‑to‑day BI load.

Time to examine your data costs

Want warehouse‑class analytics at data lake costs—without losing SQL or governance? Contact us about a focused cost assessment and Dremio engine sizing. We’ll help you move the right workloads to the lakehouse, keep BigQuery where it shines, and bring your monthly spend under control.