Build a Data Lakehouse Without Sacrificing SQL, Speed, or Governance
Many teams want to lower analytics costs but worry about breaking familiar SQL workflows or losing governance. A data lakehouse with Dremio can help, but the transition isn’t just a flip of a switch. It requires rebuilding the semantic layer, redefining permissions, and sometimes making minor changes to queries or dashboards. With careful planning, SIT—Dremio’s implementation partner in Israel—guides organizations through a migration that balances cost, performance, and governance.
What Changes from Warehouse-Only?
- Storage: Canonical data is kept in open formats (Apache Iceberg/Parquet) on low-cost object storage.
- Compute: Dremio engines query data in place, add acceleration as needed, and allow sizing compute to the actual workload.
- Governance: Row/column policies, lineage, and auditing are centrally managed across all sources, but require a fresh permissions design.
- Coexistence: BigQuery remains in use for specialized workloads; day-to-day BI and exploration often shift to the lakehouse.
Keep Your SQL (Structure Query Language)
Dremio supports standard SQL and integrates with common BI tools. Analysts keep their dashboards, queries, and workflows. There’s no need to learn a proprietary language. Pushdown, vectorized execution, and columnar formats ensure efficient scans and aggregates on the lake. However, some queries or reports may need minor adjustments during the transition.
Why the Lakehouse Can Improve Cost and Agility—But Not Always Automatically
No copy by default: Stop loading duplicates into a warehouse; query data where it lives.
Fewer ETL/ELT jobs: Replace many derived tables with governed views and virtual datasets.
Right‑sized compute: Start small engines for exploration, scale up for batch transforms, and shut down outside windows.
Open formats: Avoid lock‑in and storage markups; reuse data across engines when needed.
Important: Cost savings are not guaranteed for every use case. Workloads with heavy, complex computations can still incur significant resource costs on Dremio. Object storage, networking, and data movement also contribute to the total cost. The biggest savings are typically realized for BI and self-service analytics, especially where data movement and duplication are minimized. Results depend on your specific architecture, query patterns, and a well-planned semantic layer.
Data Normalization via a Semantic Layer
Define canonical dimensions and measures in Dremio’s semantic layer, promoting stable logic to governed views so all BI tools use the same definitions. This reduces duplicated SQL, keeps metrics consistent, and supports auditing. Building this layer is a key step in the migration.
Governance Patterns Across the Lakehouse
- Access control: Enforce role-based, row-level, and column-level policies. Redefining permissions is required during migration.
- Lineage and auditing: Track where data comes from and how it’s used.
- Least privilege by design: Grant only the access needed per role.
- Compliance alignment: Map policies to regulatory needs while keeping workflows simple.
Performance Enablers on the Lake—With Planning
- Reflections: Smart aggregations/materializations can accelerate queries on selected dashboards and datasets. This is not automatic and requires thoughtful design, choosing the right columns for reflections, and ongoing monitoring of hit rates.
- Columnar + vectorization: Efficient operators reduce CPU time on large datasets.
- Caching: Reuse results for recurring queries where appropriate.
- Engine isolation: Separate engines for BI, ad hoc, and batch to avoid noisy neighbors.
Performance improvements depend on careful architecture, monitoring, and ongoing tuning—not just enabling features out of the box.
A Realistic Migration Path
Inventory: Identify top dashboards and heavily scanned tables; list recurring ELT jobs that create derived tables.
Model: Land curated data in Iceberg, define data products and governed views for those dashboards.
Accelerate: Add reflections to target views; size engines for business hours and refresh windows.
Verify: Validate SQL compatibility, access policies, and performance against baseline.
Expand: Decommission redundant ELT and duplicate tables; onboard additional sources and teams.
Migration will require rebuilding the semantic layer, redefining permissions, and—occasionally—adjusting queries or dashboards.
Analyst Workflow that Scales
Explore: Build queries in Dremio; when needed, pull a filtered subset to a local Arrow dataframe for fast iteration (e.g., DuckDB).
Formalize: Promote stable logic to governed views; wire BI tools to these views.
Accelerate: Add reflections; monitor hit rates and refresh costs; adjust as needed.
How SIT Implements a Data Lakehouse Transition
We start by mapping your goals, data sources, and reporting needs. Together, we design a blueprint that aligns architecture, governance, and engine strategy with your existing tools and service levels—so there’s no guesswork. Our team sets up the lakehouse foundations: we land curated data in Iceberg tables, define clear access policies, build a semantic layer for consistent metrics, and configure reflections and engine schedules to fit your business cycles.
Not everything moves at once. We work with you to decide which workloads and dashboards stay in BigQuery and which are better suited for the lakehouse, so you only shift what makes sense for cost and performance. When the transition is ready, we deliver clear runbooks for admins and analysts, set up live cost and performance dashboards, and provide an ongoing optimization plan—ensuring your team is confident and self-sufficient long after go-live.
Caveats & Disclaimer
A move to a data lakehouse isn’t “plug and play.” Expect to invest in semantic layer rebuild, permissions redefinition, and some query/report changes. Performance and cost savings depend heavily on your data size, workload patterns, semantic layer design, and reflection strategy. Some workloads—especially those with complex, heavy computations—may still require significant resources.
Disclaimer:
Actual savings and performance improvements depend heavily on your specific workload, query patterns, data size, and lakehouse design.
Time to move from a warehouse‑only model to a governed lakehouse
Contact SIT to get a blueprint and phased rollout strategy. We’ll help you keep what works in BigQuery, shift what doesn’t, and reduce total cost while improving control—always with a plan tailored to your real needs.
