BigQuery vs. Redshift: The Cloud Data Warehouse Showdown (No Boring Slides)

Let me tell you a story about my biggest data infrastructure mistake.

It was 2018. My team had to choose our first cloud data warehouse. After weeks of meetings, fancy vendor demos, and enough PowerPoint slides to crash a laptop, we made our choice.

We chose wrong.

Two years and $300,000 in unnecessary costs later, we migrated to a different platform. The migration took six painful months of weekend work and emergency rollbacks.

I'm telling you this because if you're evaluating BigQuery vs. Redshift right now, you're making one of the most important decisions of your data infrastructure life. Get it wrong, and you'll feel the pain for years.

After working with both platforms extensively (and making plenty of mistakes), I'm going to show you the real differences—no marketing fluff, just practical insights from the battlefield.

The Quick Answer: Who Should Choose What?

Choose Google BigQuery if:

• You want a serverless experience (no infrastructure management)
• Your workload is unpredictable with spikes in usage
• Your team knows SQL but isn't strong in database administration
• You need to analyze massive datasets (petabyte-scale) without performance tuning

Choose Amazon Redshift if:

• You're already deep in the AWS ecosystem
• You need predictable performance for consistent workloads
• You have dedicated database administrators for tuning
• You want more control over your cluster configuration and costs

But wait—the real answer is more nuanced than that. Let's dig deeper.

The Cost Comparison: It's Not What You Think

Most comparisons get this wrong because they compare list prices instead of real-world usage.

BigQuery's Pricing Model: Pay-Per-Query

-- This query will cost you ~$5.00 to run
-- (assuming it processes 1TB of data at $5/TB)
SELECT 
  user_id,
  COUNT(*) as event_count
FROM `your_project.analytics.events`
WHERE event_date BETWEEN '2024-01-01' AND '2024-03-31'
GROUP BY user_id;

The reality: BigQuery charges you for the data processed by each query. No queries, no cost. This is fantastic for unpredictable workloads but dangerous if you don't monitor your queries.

Redshift's Pricing Model: Pay-Per-Cluster

-- This query's cost is buried in your monthly cluster fee
-- Whether you run it once or 1000 times, the cost is the same
SELECT 
  userid,
  COUNT(*) as event_count
FROM analytics.events
WHERE event_date BETWEEN '2024-01-01' AND '2024-03-31'
GROUP BY userid;

The reality: Redshift charges you for running clusters 24/7. If you provision a $10,000/month cluster, you pay $10,000 whether you use it heavily or let it sit idle.

The Performance Reality: It Depends (Seriously)

I've seen teams choose based on performance benchmarks that don't reflect real-world use. Here's what actually matters:

BigQuery's Secret Weapon: The BigQuery BI Engine

For dashboard queries, BigQuery's in-memory BI Engine can return results in under a second—even on billion-row tables. This is a game-changer for Tableau or Looker users tired of waiting for dashboards to load.

Redshift's Performance Advantage: Predictable Workloads

For consistent ETL jobs and scheduled reports, a properly tuned Redshift cluster can provide more predictable performance. The key phrase is "properly tuned"—which requires significant expertise.

The SQL Differences That Will Bite You

You think SQL is standardized until you switch platforms. Here are the practical differences:

Date Truncation:

-- BigQuery
SELECT DATE_TRUNC(date_column, MONTH)

-- Redshift  
SELECT DATE_TRUNC('month', date_column)

First Value Analysis:

-- BigQuery (supports IGNORE NULLS)
FIRST_VALUE(column_name IGNORE NULLS)

-- Redshift (doesn't support IGNORE NULLS)
-- You need workarounds that add complexity

JSON Handling:

-- BigQuery (clean native syntax)
JSON_EXTRACT(json_column, '$.property')

-- Redshift (functional but uglier)  
JSON_EXTRACT_PATH_TEXT(json_column, 'property')

These small differences become major headaches during migrations or if you support both platforms.

The Operational Overhead: What Nobody Tells You

BigQuery: Almost Zero Administration

I managed a 5PB BigQuery environment with exactly zero dedicated administrators. The platform:

• Automatically scales to handle any workload
• Requires no vacuuming or table optimization
• Handles security updates automatically
• Provides built-in ML capabilities

Redshift: Requires Constant Gardening

A comparable Redshift environment required three full-time engineers to:

• Manage cluster scaling and maintenance windows
• Tune table distribution styles and sort keys
• Vacuum tables to reclaim space
• Monitor disk space and query queues

The Decision Framework: 5 Questions to Ask

Don't choose based on what's "cooler." Answer these questions honestly:

1. What's your team's expertise? (Choose the platform that matches your skills)
2. How predictable is your workload? (Steady = Redshift, Spiky = BigQuery)
3. What cloud are you already using? (Sticking with one cloud saves complexity)
4. What's your tolerance for administration? (None = BigQuery, Some = Redshift)
5. What are your compliance needs? (Both meet most requirements, but check specifics)

The Migration Path: If You Change Your Mind

We migrated from Redshift to BigQuery in six painful months. Here's what we learned:

1. Test with real workloads, not just sample data
2. Use the built-in migration tools from both providers
3. Expect SQL rewrites—it's never just a lift-and-shift
4. Plan for dual writing during the transition period
5. Monitor costs closely—the pricing models are completely different

The Verdict: There's No Right Answer

After working with both platforms for years, here's my honest assessment:

BigQuery feels like the future—serverless, scalable, and incredibly productive for most teams. But you pay for that convenience, and cost control requires discipline.

Redshift feels like a traditional database—predictable, controllable, and familiar for database experts. But it requires more hands-on management and expertise.

The right choice isn't about which platform is "better." It's about which platform is better for your specific team, workload, and constraints.

Your Next Step: Try Before You Commit

Don't make our $300,000 mistake. The best way to evaluate is to test both platforms with your actual data and queries. Both platforms offer free tiers and trials:

• BigQuery: First 1TB of query processing free each month
• Redshift: Free trial clusters available

Run your most important queries on both platforms. Compare not just performance, but also the developer experience, monitoring tools, and total cost of ownership.

This hands-on testing is the only way to know which platform truly fits your needs.

Still not sure which is right for you? Reply with your specific use case and I'll give you my honest opinion.