Cloud Data Warehouses for Embedded Analytics

The Warehouse Embedded Analytics Trap

You have (or are evaluating) a cloud data warehouse - Snowflake, BigQuery, Databricks, or Firebolt.

It's powerful. It handles your analytical workloads. Your data team loves it. Then you decide to embed analytics into your product.

You point dashboards at your warehouse. They work. Users query them. Everything seems fine.

Then three things happen:

1. Costs spike unexpectedly

You expected to add embedded dashboards and pay a bit more. Instead, costs jump 50%, 100%, sometimes 300%. 

Why? 

Warehouses charge per query or per data scanned. Embedded analytics means hundreds or thousands of end users running queries simultaneously. Each query can scan gigabytes or terabytes of data. Your bill explodes.

2. Dashboards feel slow

Warehouse queries that complete in 2-3 seconds feel glacially slow inside an embedded dashboard. End users expect sub-second responses. Under concurrent load, warehouses often produce 3-5 second tail latencies. Users click, wait, refresh, and lose trust.

3. You can't control query behavior

Warehouses were designed for analytical queries (scan billions of rows, aggregate, return results). Embedded analytics requires millions of lightweight, concurrent queries (scan millions of rows, filter, return results fast). Your warehouse doesn't know how to optimize for this. Neither does your team.

By now, you've made a choice:

1. Option A: Accept slow dashboards and rising costs as the cost of doing business
2. Option B: Build a caching layer, query optimization engine, and access control system yourself (9-12 months, $300K–500K)
3. Option C: Use a delivery layer designed specifically for embedded analytics on top of warehouses

Most teams don't know Option C exists. For most teams, the decision comes down to speed, predictability, and ownership.

If you read How to Choose Databases for Embedded Analytics: Complete Guide (and Ship Faster with Databrain), you learned that embedded analytics success depends on database architecture.It covered operational databases, real-time analytics engines, and distributed SQL systems.

This article goes deeper: It addresses the next critical decision teams face. 

It focuses on:

1. When warehouses make sense for embedded analytics (spoiler: it's not always)
2. Where warehouses fail embedded analytics workloads
3. How to use warehouses safely without breaking your product or your budget
4. The role of a delivery layer in making warehouses work for embedded analytics

The core reality: Warehouses are powerful analytical platforms. They're not designed for embedded analytics. The mistake is pretending they can do both without friction.

Modern Cloud Data Warehouses: The 4 Major Platforms

Snowflake: The Mature, Multi-Cloud Option

Core Innovation: Separation of storage and compute. You can scale query workloads independently of storage, allowing strong isolation and governance.

Best for:

1. Teams needing reliable SQL analytics
2. Multi-cloud deployments
3. Shared analytical workloads (multiple teams querying the same data)
4. Regulatory environments requiring strong governance

Embedded analytics reality:

1. Interactive latency: 1-5 seconds (depends on warehouse size and query tuning)
2. Concurrency: Scales, but pricing grows with active warehouses
3. Cost model: Predictable if you understand credit consumption

Warehouse-specific problem: Pricing scales directly with concurrency. Each active dashboard user spins up computation. At 100 concurrent users, your warehouse costs become a product support line item.

Warehouse docs: Snowflake Connection Guide

BigQuery: Google's Approach

Core Innovation: Fully serverless execution. No capacity planning, no cluster sizing. Queries scale automatically.

Best for:

1. Teams already invested in Google Cloud
2. Large analytical scans (terabyte-scale queries)
3. Event data and time-series analytics
4. ML workflows (tight integration with AI)

Embedded analytics reality:

1. Interactive latency: 1-10 seconds (depends on query complexity)
2. Concurrency: Automatic, but costs are unpredictable
3. Cost model: Pay-per-TB scanned (dangerous for embedded analytics)

Warehouse-specific problem: BigQuery's pricing model is a nightmare for embedded dashboards. If your dashboard query scans 100 GB and 100 users run it simultaneously, you're charged for 10 TB of scanned data. That's $50-100 per refresh cycle for a single dashboard.

Warehouse docs: BigQuery Connection Guide

Databricks: The Lakehouse Model

Core Innovation: Combines data lakes (Delta Lake, Parquet) with warehouse-like query performance through Apache Spark.  

Best for:

1. Organizations needing unified analytics + ML + data engineering
2. Teams using open data formats
3. Complex multi-stage transformations

Embedded analytics reality:

1. Interactive latency: 1-5 seconds (cluster-dependent)
2. Concurrency: Requires disciplined cluster sizing
3. Cost model: DBU (Databrick Units) based, highly variable

Warehouse-specific problem: Databricks requires significant operational discipline for embedded analytics. Cluster autoscaling is often too slow for user-facing dashboards. You end up over-provisioning clusters (wasting money) or under-provisioning (slow dashboards).

Warehouse docs: Databricks Connection Guide

Firebolt: Purpose-Built for Interactive Analytics

Core Innovation: Indexed, compressed storage with decoupled query engines designed for high concurrency and sub-second latency.

Best for:

1. Product teams requiring fast, interactive dashboards
2. High-concurrency workloads with predictable latency
3. Organizations willing to adopt a specialized warehouse

Embedded analytics reality:

1. Interactive latency: 100 ms – 1 second (benchmark-based)
2. Concurrency: Scales well; engine-level isolation prevents contention
3. Cost model: Engine-based pricing (transparent, scales with usage)

Warehouse-specific problem: Firebolt is newer and has a smaller ecosystem. If you need deep integration with data orchestration or ML platforms, you'll supplement with other tools.

Warehouse docs: Firebolt Connection Guide

How DataBrain helps: DataBrain adds metric modeling, multi-tenancy, and embedding governance. This lets you ship dashboards faster without rebuilding permissions and access control per dashboard.

Real Performance Under Embedded Analytics Load

The numbers below reflect commonly observed production behavior across SaaS workloads.

What matters most for embedded analytics is tail latency (P95), not averages.

Latency Under Concurrency

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Reality check: Anything above 1 second is noticeable in a dashboard. Anything above 2 seconds feels like a stall. Firebolt is purpose-built to stay under 1 second. Others require significant tuning.

Real Scenarios: Where Warehouse + DataBrain Works

Scenario 1: Financial SaaS with Multi-Tenant Dashboards

Setup: Snowflake + DataBrain

Customer problem:

1. Multiple trading teams need access to their own trading data
2. Each team's dashboard queries millions of events
3. Security team concerned about row-level security across dashboards

Result with DataBrain:

1. Row-level security enforced at query layer, not per dashboard
2. Teams can build new dashboards in days, not weeks

See a real example of this in our Financial SaaS with Multi Tenant Dashboards scenario.

Scenario 2: E-Commerce Platform (BigQuery + DataBrain)

Setup: BigQuery + DataBrain

Customer problem:

1. Seller dashboards scan 500 GB+ per query
2. Dashboards take 5-8 seconds to load
3. No way to govern which sellers see which data

Result with DataBrain:

1. Query costs drop (pre-filtering)
2. Dashboard latency reduced
3. Multi-tenancy built-in (each seller sees only their data)

Scenario 3: Data-Heavy B2B (Databricks + DataBrain)

Setup: Databricks + DataBrain

Customer problem:

1. Cluster autoscaling too slow for embedded dashboards
2. Teams over-provision clusters to avoid slowness
3. Data engineering and analytics workloads compete for resources

Result with DataBrain:

1. Separate analytics cluster, isolated from engineering workloads
2. Analytics team and engineering team no longer compete for resources

How DataBrain Makes Warehouses Safe for Embedded Analytics

DataBrain doesn't replace your warehouse. It adds the missing delivery layer.

Problem 1: Slow Dashboards

Warehouse behavior: Tail latency under concurrency is high (warehouse designed for analysts, not thousands of end users).

Databrain solution:

1. Pre-computed aggregations (don't recalculate every query)
2. Materialized views (pre-built result sets for common patterns)

Result: Sub-second dashboards from multi-second warehouse queries.

Problem 2: Access Control Scattered Across Dashboards

Warehouse behavior: No multi-tenant awareness; views and row-level security are warehouse-level concepts, not embedded-analytics-level concepts.

DataBrain solution:

1. Centralized metric definitions (single source of truth)
2. Multi-tenant aware (each tenant's queries are isolated)
3. Row-level security at query layer (enforced for every query)

Result: Consistent permissions across all dashboards, compliance ready.

Problem 3: Embedding Complexity

Warehouse behavior: No embedding primitives; teams build custom APIs or wire dashboards directly to SQL.

DataBrain solution:

1. Native embedding SDKs (embed dashboards)
2. Secure credential handling 
3. Filter propagation (dashboard app filters)

Result: Embedded dashboards ship in days, not weeks.

Decision Framework: How to Choose

• Speed and interactive UX matter most → Firebolt
• Large analytical scans dominate → BigQuery
• Open formats + ML workflows required → Databricks
• Governance and multi-cloud matter → Snowflake

There is no universally correct choice. There is only fit for workload.

FAQ: Warehouses + Embedded Analytics

Q: Can't I just use my warehouse as-is for embedded analytics?

A: Technically, yes. Realistically, no. You'll face slow dashboards, cost explosions, and access control chaos. DataBrain fixes these problems.

Q: Do I really need to add another tool? Can I just optimize the warehouse?

A: You can optimize forever. Most teams optimize warehouse queries for weeks and still end up with slow, expensive embedded dashboards. DataBrain's embedding capabilities solve problems that warehouse optimization alone cannot.

Q: What if my dashboard needs super fresh data (real-time)?

A: DataBrain supports configurable cache lifetimes. You can set cache to 1 second, 10 seconds, or real-time (no caching).

Warehouse-Only vs. Warehouse + DataBrain

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The Bottom Line

Cloud data warehouses are powerful. They're also expensive and slow when used directly for embedded analytics.

The solution isn't to abandon warehouses. It's to add a delivery layer that makes them work safely and cost-effectively inside your product.

DataBrain connects to your warehouse: Snowflake, BigQuery, Databricks, or Firebolt, and transforms it from a reporting tool into an embedded analytics powerhouse.

Your next move: Choose one of the options above and take the first step.

The ROI is clear. The migration is low-risk. The impact is immediate.

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