Caching And Extract Versus Live Decisions

Why this matters

As a BI Analyst, you will decide whether dashboards query data live or use cached/extracted data. The right choice keeps dashboards fast, affordable, and fresh enough for stakeholders. Typical tasks you will face:

• Designing a KPI dashboard that loads under 3 seconds during peak hours.
• Balancing near real-time visibility for operations with warehouse cost limits.
• Scheduling extracts with incremental refresh to reduce load on source systems.
• Setting cache time-to-live (TTL) to control staleness vs. speed.
• Avoiding performance regressions when concurrency grows.

Concept explained simply

There are two broad ways to power a BI view:

• Live: Each dashboard action sends a query to the source system (e.g., warehouse). Freshness is highest, but cost and latency can grow with traffic.
• Extract/Cache: Data is periodically copied, aggregated, or cached. Views are much faster and cheaper to run, but data can be minutes/hours old.

Mental model: Freshness–Speed–Cost triangle

You can usually optimize two of the three:

• Freshness: Live queries or very short cache TTL.
• Speed: Extracts, pre-aggregations, and longer TTL.
• Cost: Caching and extracts reduce compute against source systems.

Decide by setting explicit targets: latency (e.g., <3s), staleness budget (e.g., <15m), and cost cap (e.g., daily credits).

Decision framework (fast checklist)

1. Define staleness budget: How old can data be and still be useful? (e.g., 5m, 15m, 1h, 1d)
2. Set performance target: Page should load in X seconds at Y concurrent users.
3. Check source load sensitivity: Is the warehouse or operational DB OK with many queries?
4. Estimate data volume and complexity: Big joins and billions of rows favor extracts/pre-aggregations.
5. Consider security: Row-level security and user-specific data may reduce cache hit rates.
6. Decide:
   • If staleness budget is strict (near 0), prefer Live with optimizations (materialized views, aggregates).
   • If staleness budget is flexible, prefer Extract/Cache with incremental refresh.

Worked examples

Example 1: Executive KPIs (daily)

• Need: Loads <2s, data can be up to 24h old.
• Traffic: Moderate.
• Decision: Extract with daily full refresh + small incremental for late-arriving rows.
• Extras: Cache TTL 12h; pre-aggregate to daily grain.

Example 2: Support queue monitoring (near real-time)

• Need: <5m staleness, <3s load, high concurrency during business hours.
• Decision: Hybrid: Live against a fast pre-aggregated table updated every 2–3 minutes.
• Extras: Short cache TTL (1–2m) to absorb bursts; limit drill-down to recent period.

Example 3: Product analytics with large history

• Need: Analysts explore 2B+ events; staleness budget 1–6h.
• Decision: Extract partitioned by date, incremental based on event_date and updated_at.
• Extras: Pre-aggregate to session/day; cache top queries for 4h.

Example 4: Finance cutoff dashboard

• Need: During monthly close, data must be exact as of cutoff; later it should not change without an intentional refresh.
• Decision: Extract snapshot at cutoff; disable auto-refresh until the close is done.
• Extras: Show "As of" timestamp at top of dashboard.

How caching works (practical)

• Query result cache: Stores results for identical queries and parameters. Great for repeated views; may be bypassed by user-specific filters.
• Data extracts: Stored data files/tables refreshed on schedule. Fast and stable performance; data is as fresh as last refresh.
• Pre-aggregations/materialized views: Precomputed tables that speed up both live and extracted approaches.

Configuration recipes (generic)

1. Define staleness budget: e.g., 15 minutes.
2. Pick mode: If budget <= 5 minutes, consider Live + pre-aggregations; else Extract/Cache.
3. Set refresh: Incremental on updated_at; schedule every 15 minutes.
4. Set TTL: Query cache TTL 10–15 minutes to align with refresh.
5. Pre-aggregate: Create daily/ hourly summary tables for heavy dashboards.
6. RLS note: If row-level security is per user, cache at group-level where safe to improve hit rates.

Exercises you can do now

Note: The quick test is available to everyone; only logged-in users will have their progress saved.

Exercise 1: Choose the right mode

Your sales dashboard has:

• Staleness budget: 30 minutes
• Users: 60 concurrent during peak
• Data: 200M rows, wide fact table, joins to 6 dims
• Source sensitivity: Warehouse credits are limited at peak

Decide: Live or Extract/Cache? Propose refresh schedule, TTL, and any pre-aggregations.

Exercise 2: Tune a slow live dashboard

A live operations page loads in 12 seconds. Requirements: <3 seconds, data <= 2 minutes old, heavy filtering by region and product.

Propose changes to meet the targets without overloading the source.

Common mistakes and how to self-check

• Undefined freshness target: Always write a staleness budget (e.g., "<= 15m").
• Overusing live when data could be 30–60m old; costs soar and pages slow.
• Overusing extracts for near-real-time needs; users lose trust.
• No incremental refresh: Full refreshes are slow and expensive.
• Ignoring RLS effects: User-specific filters reduce cache reuse; consider group-level caching where policy allows.
• Mismatched TTL and refresh: TTL longer than refresh hides updates; TTL much shorter than refresh wastes cache.

Practical projects

• Project 1: Convert a slow live dashboard to an extract-based version with incremental refresh and a 30-minute TTL. Measure load time, cost, and cache hit rate before/after.
• Project 2: Build a near real-time monitoring view using live queries against a pre-aggregated table that updates every 2 minutes. Add a short TTL and simulate 50 concurrent users.

Who this is for

• BI Analysts, Data Analysts, and Analytics Engineers responsible for dashboard performance and reliability.

Prerequisites

• Basic SQL (joins, filters, aggregations).
• Familiarity with BI dashboards and data refresh concepts.
• Understanding of row-level security basics.

Learning path

1. Caching and Extract vs Live decisions (this lesson).
2. Pre-aggregations and materialized views.
3. Concurrency planning and cost controls.
4. Monitoring refresh health and cache hit rates.

Next steps

• Apply the decision framework to one of your dashboards this week.
• Document the chosen mode, staleness budget, TTL, and refresh plan.
• Run the quick test to reinforce key concepts.

Mini challenge (5 minutes)

Design for a marketing dashboard with hourly KPI updates, 100 concurrent viewers, cost-sensitive warehouse, and tolerance of 30–60 minutes staleness. Write a 3-line plan: mode, refresh, TTL, and one pre-aggregation.