Why this matters
Choosing how your models persist in the warehouse changes cost, speed, and reliability. The right materialization keeps dashboards fast, ELT runs predictable, and stakeholder SLAs green.
- Speed up BI queries by precomputing heavy joins and aggregations.
- Control compute costs by avoiding repeated expensive queries.
- Meet freshness SLAs with the right refresh strategy (batch vs near-real-time).
- Improve developer velocity by keeping build times sane and predictable.
Typical Analytics Engineer tasks affected
- Designing star schemas and deciding which models become tables vs views.
- Optimizing a slow dashboard by moving logic into incremental or aggregate tables.
- Balancing freshness vs cost for frequently-used metrics.
- Refactoring a DAG to reduce full refresh times.
Concept explained simply
Materialization means how a model exists in your warehouse at the end of a build: ephemeral, view, table, incremental table, materialized view, or dynamic table. Each trades compute, storage, and freshness differently.
Mental model
Think of materializations as a spectrum between on-demand calculation and precomputation:
- On-demand (view): cheap storage, potentially expensive per query.
- Precompute once (table): pay the compute up front, reads are fast.
- Precompute continuously (incremental, materialized view, dynamic table): pay small compute chunks over time to keep things fresh.
Common materializations and when to use them
- Ephemeral: Used only as a CTE within downstream models. Great for small, reusable helper logic that you don’t want to persist.
- View: Lightweight, always fresh, but may be slow for complex queries. Good for small models, staging layers, and low-traffic use.
- Table: Persisted data. Fast reads, predictable BI performance. Refresh costs depend on how often you rebuild. Good for dimensions and moderate-size facts.
- Incremental Table: Stores previous results and processes only new/changed data. Ideal for large, append-heavy or slowly changing facts where full rebuilds are expensive.
- Materialized View (if your warehouse supports it): Warehouse-managed, auto-updated based on source changes. Great for low-latency aggregates and filters; be mindful of maintenance costs.
- Dynamic Table (continuous pipelines in some warehouses): Keeps a table updated on a defined schedule or based on dependencies. Useful for near-real-time without complex orchestration.
Heuristics you can memorize
- Small + simple + infrequent use → View.
- Frequently queried + joins/aggregations → Table.
- Large + append-only + predictable keys → Incremental with MERGE/DELETE strategy.
- Low-latency aggregates on supported platforms → Materialized view or dynamic table.
- Helper logic used by one downstream model → Ephemeral.
Decision framework
- Data size: tiny/small can stay as views; large benefits from table or incremental.
- Change pattern: append-only or key-based updates enable incremental.
- Query frequency: if queried a lot, precompute (table/incremental/mv/dt).
- Complexity: heavy joins/windows/aggregates benefit from precomputation.
- Freshness SLA: near-real-time favors materialized views/dynamic tables; daily SLA can be batch tables/incremental.
- Cost: views shift cost to readers; tables shift cost to builds; mviews/dynamic tables spread cost over time.
- Warehouse features: use what your platform supports and you can operate.
Quick choose guide
- If daily batch is fine and model is heavy → Table (rebuilt daily).
- If large fact grows daily and full rebuild is slow → Incremental with MERGE.
- If metrics must be fresh within minutes → Materialized view or dynamic table.
- If model is intermediate helper only → Ephemeral.
- If exploratory or low-traffic staging → View.
Worked examples
Example 1: Daily sales summary for BI dashboards
Need: Fast queries; freshness by next morning. Data: moderate size; heavy joins and aggregates.
Choice: Table. Rebuild nightly. Reads are fast; cost occurs in the scheduled build, not every dashboard view.
Why not a view?
Heavy aggregations would execute on every dashboard query, spiking compute and latency. Precomputing avoids that.
Example 2: Large event fact with late-arriving updates
Need: Keep up with tens of millions of rows daily; handle updates on a natural key.
Choice: Incremental table with MERGE on event_id and event_timestamp. Process only new/changed rows; occasionally run a backfill window for late data.
Refresh strategy
- Daily incremental run: load last 2 days to catch late arrivals.
- Monthly maintenance: compact partitions or vacuum as needed (warehouse-specific).
Example 3: Near-real-time top-line metrics
Need: KPI dashboard within 5 minutes of data landing; moderate complexity aggregations.
Choice: Materialized view or dynamic table (if supported). The warehouse maintains freshness with incremental updates, balancing latency and cost.
Cost control
- Restrict to essential columns and prefiltered sets.
- Keep aggregation logic efficient (avoid excessive joins in the maintained object).
Exercises
Note: The quick test is available to everyone. Only logged-in users will have their progress saved.
Exercise 1 — Map scenarios to materializations
See the exercise card below. After completing, use the checklist to self-verify.
Self-check checklist
- You considered data size, change pattern, frequency, and SLA for each scenario.
- You justified the choice with performance and cost trade-offs.
- You identified any periodic maintenance or backfill strategy if needed.
Common mistakes and how to self-check
- Overusing tables for tiny models: wastes storage and rebuild time. Self-check: Is the model small and rarely queried? Make it a view.
- Using views for heavy dashboards: slow and expensive at read time. Self-check: Review query profiles; if scans and joins are heavy, precompute.
- Incremental without a reliable key: leads to duplicates or missed updates. Self-check: Confirm unique keys and update windows.
- Materialized views with complex joins: maintenance cost can spike. Self-check: Keep maintained objects focused and test refresh cost.
- Ignoring freshness SLAs: wrong choice causes staleness or unnecessary cost. Self-check: Align materialization with the SLA and usage pattern.
Quick health checks
- Row counts and metrics parity between source logic and materialized output.
- Freshness timestamps meet agreed SLA.
- Query latency on BI dashboards improved after change.
- Build/refresh cost curve is predictable over a week.
Practical projects
- Refactor a slow dashboard: Move a complex view to a table and measure latency before/after. Document cost impact.
- Build an incremental fact: Implement MERGE with a 2-day backfill window; schedule a monthly compact/cleanup job.
- Near-real-time KPI: Implement a materialized view or dynamic table for a single KPI and set a 5–10 minute freshness target.
Who this is for
- Analytics Engineers and BI Developers optimizing models for performance and cost.
- Data Analysts maintaining dashboards with strict SLAs.
Prerequisites
- Comfort with SQL joins, aggregations, and window functions.
- Basic understanding of your warehouse’s capabilities (views, tables, incremental, materialized views, dynamic tables).
- Familiarity with your orchestration/build tool’s configuration (e.g., scheduling, incremental keys).
Learning path
- Start with views for staging and simple transformations.
- Promote frequently-used heavy models to tables.
- Scale large facts with incremental strategies and proper keys.
- Add materialized views or dynamic tables where near-real-time is needed.
- Continuously monitor cost and latency; iterate choices as usage grows.
Next steps
- Complete the exercise and quick test.
- Pick one production model and evaluate if its current materialization is still optimal.
- Plan a small experiment (A/B) to validate a change before rolling it out widely.
Mini challenge
Choose one model in your environment. Propose a new materialization that improves either latency or cost without breaking freshness. Write a 3-sentence justification and a rollback plan.