Decision comparison
Amazon Redshift vs Yellowbrick Data
Redshift and Yellowbrick are both columnar MPP warehouses with PostgreSQL roots, and they differ on who runs the system. Redshift is AWS-operated and AWS-only, with deep S3, IAM, Glue and SageMaker integration and a serverless option. Yellowbrick runs as containers on Kubernetes inside your own cloud account or data centre, keeping data and control within your own network, and is bought as capacity.
Architecture choice. These take different approaches to the same problem. Read the table as a fit question rather than a feature race.
All 2 are cloud data warehouses.
Quick Comparison
| Decision factor | Amazon Redshift | Yellowbrick Data |
|---|---|---|
| What it is | AWS's managed data warehouse, with provisioned clusters or a serverless option | A massively parallel data warehouse that runs as containers on Kubernetes, in your own cloud account or on your own hardware |
| Where it runs | AWS only, operated by AWS | Your own AWS, Azure or Google Cloud account, or your own data centre |
| Who holds the data | AWS-managed storage inside your account | Inside your own account or building, under your own network controls |
| Pricing shape | Amazon Redshift bills by usage and publishes no monthly plan. Provisioned clusters start at $0.543 per node-hour; Serverless is billed per RPU-hour from $0.375 in US East (N. Virginia), charged per second. New Serverless accounts get a $300 credit expiring after 90 days -- a free trial, not a free tier. Verified 2026-09-16 against aws.amazon.com/redshift/pricing/. | Contact for pricing |
| SQL dialect | PostgreSQL-derived, with Redshift-specific extensions | PostgreSQL-compatible, so existing Postgres drivers and tooling work |
| Lake integration | Spectrum queries data in S3 in place, joined against warehouse tables | External table access to object storage, without an equivalent managed lake service |
| Best fit | Teams on AWS wanting deep S3, IAM and SageMaker integration | Teams needing the warehouse inside their own network or on their own hardware |
Amazon Redshift
- What it is:
- AWS's managed data warehouse, with provisioned clusters or a serverless option
- Where it runs:
- AWS only, operated by AWS
- Who holds the data:
- AWS-managed storage inside your account
- Pricing shape:
- Amazon Redshift bills by usage and publishes no monthly plan. Provisioned clusters start at $0.543 per node-hour; Serverless is billed per RPU-hour from $0.375 in US East (N. Virginia), charged per second. New Serverless accounts get a $300 credit expiring after 90 days -- a free trial, not a free tier. Verified 2026-09-16 against aws.amazon.com/redshift/pricing/.
- SQL dialect:
- PostgreSQL-derived, with Redshift-specific extensions
- Lake integration:
- Spectrum queries data in S3 in place, joined against warehouse tables
- Best fit:
- Teams on AWS wanting deep S3, IAM and SageMaker integration
Yellowbrick Data
- What it is:
- A massively parallel data warehouse that runs as containers on Kubernetes, in your own cloud account or on your own hardware
- Where it runs:
- Your own AWS, Azure or Google Cloud account, or your own data centre
- Who holds the data:
- Inside your own account or building, under your own network controls
- Pricing shape:
- Contact for pricing
- SQL dialect:
- PostgreSQL-compatible, so existing Postgres drivers and tooling work
- Lake integration:
- External table access to object storage, without an equivalent managed lake service
- Best fit:
- Teams needing the warehouse inside their own network or on their own hardware
Public signals
Verified factual signals only. Bars appear only for like-for-like metrics with five weekly assessments for every tool; missing evidence stays explicit. These signals do not establish enterprise adoption, product quality, or total cost.
| Metric | Amazon Redshift | Yellowbrick Data |
|---|---|---|
| GitHub commits, 90d(Developer adoption) | 13 | 0 |
| GitHub stars(Developer adoption) | 71 | 4 |
| Search interest(Market interest) | 1 | Unavailable |
| Hacker News mentions, 90d(Community interest) | 0 | 0 |
| npm weekly downloads(Developer adoption) | 204.3k | Not available |
| Product Hunt comments(Community interest) | 1 | Not available |
| Product Hunt reviews(Community interest) | 0 | Not available |
| Product Hunt votes(Community interest) | 68 | Not available |
| PyPI weekly downloads(Developer adoption) | 9.8M | Not available |
| Stack Overflow questions(Community interest) | 8.8k | Not available |
| Docker Hub pulls(Product adoption) | Not available | 4.3k |
As of September 21, 2026 — updated weekly.
Health & risk evidence
Observed public-source checks for mapped package versions and repositories.
Amazon Redshift
September 21, 2026Package vulnerabilities
npm · @aws-sdk/client-redshift@3.1136.0 · PyPI · redshift-connector@2.1.16
0 vulnerabilities
across 2 packages
Repository security score
github.com/aws/amazon-redshift-jdbc-driver
4.5/10
Yellowbrick Data
Package vulnerabilities
Not available
Repository security score
Not available
Interface Preview
Yellowbrick Data
Feature Comparison
| Feature | Amazon Redshift | Yellowbrick Data |
|---|---|---|
| Query | ||
| Standard SQL analytics | Full support | Full support |
| PostgreSQL wire compatibility | Partial support | Full support |
| Materialised views | Full support | Full support |
| Query data in object storage | Full support | Partial support |
| Operations | ||
| Managed by the cloud provider | Full support | Not verified |
| Automatic concurrency scaling | Full support | Partial support |
| Runs on Kubernetes | Not verified | Full support |
| Pause compute when idle | Full support | Full support |
| Deployment | ||
| Run on-premise | Not verified | Full support |
| Run outside AWS | Not verified | Full support |
| Serverless option | Full support | Not verified |
| Same deployment model everywhere | Not verified | Full support |
| Ecosystem | ||
| Native cloud identity integration | Full support | Partial support |
| In-database machine learning | Full support | Not verified |
| Streaming ingestion | Full support | Partial support |
| Predictable fixed cost | Partial support | Full support |
Query
Standard SQL analytics
PostgreSQL wire compatibility
Materialised views
Query data in object storage
Operations
Managed by the cloud provider
Automatic concurrency scaling
Runs on Kubernetes
Pause compute when idle
Deployment
Run on-premise
Run outside AWS
Serverless option
Same deployment model everywhere
Ecosystem
Native cloud identity integration
In-database machine learning
Streaming ingestion
Predictable fixed cost
Which approach fits
Redshift and Yellowbrick are both columnar MPP warehouses with PostgreSQL roots, and they differ on who runs the system. Redshift is AWS-operated and AWS-only, with deep S3, IAM, Glue and SageMaker integration and a serverless option. Yellowbrick runs as containers on Kubernetes inside your own cloud account or data centre, keeping data and control within your own network, and is bought as capacity.
When each approach fits
Choose Amazon Redshift if:
Choose Amazon Redshift when you are committed to AWS and no custody rule blocks it. Spectrum queries S3 data in place, RA3 nodes separate managed storage from compute, concurrency scaling absorbs bursts without permanent headroom, streaming ingestion from Kinesis and Kafka is native, and IAM, Glue, QuickSight and SageMaker integrate without extra work.
Choose Yellowbrick Data if:
Choose Yellowbrick when the warehouse must stay inside your own network or building, or when you want one deployment model across a private data centre and more than one cloud. Kubernetes handles scheduling and recovery, PostgreSQL compatibility keeps existing drivers and tooling working, and capacity pricing fixes the bill regardless of query volume.
These scenarios reflect the available product evidence. Your requirements, existing stack, and team expertise should guide the final decision.
Frequently Asked Questions
Does Redshift not already run in our account?
The cluster is provisioned into your AWS account and your VPC, which satisfies many network requirements. What it does not change is that AWS operates the service and manages the storage layer. For most organisations that is fine and well covered by existing agreements. For those under a rule that no third party may operate the system, it is the distinction that matters, and it is why Yellowbrick exists.
How different are the SQL dialects?
Both descend from PostgreSQL, so basic analytical SQL is close and much of it ports with light editing. Redshift has diverged with its own extensions, distribution and sort key syntax, and Spectrum external tables. Yellowbrick stays closer to PostgreSQL wire compatibility, so existing Postgres drivers and many Postgres-aware tools connect without a new integration path.
What operational work does each require?
Redshift requires schema decisions — distribution and sort keys, though automatic table optimisation handles much of it now — and little else; AWS runs the rest, and serverless removes sizing entirely. Yellowbrick requires a Kubernetes platform and someone to own upgrades and capacity. If you already run Kubernetes well, that is a smaller step than it sounds; if you do not, it is a real commitment.
Which is cheaper?
Capacity pricing suits a warehouse that is busy most of the day, because you use what you bought and no query changes the invoice. Per-node or per-capacity-unit pricing with pausing and concurrency scaling suits load concentrated in a few hours, because quiet time costs little. Measure what fraction of a normal weekday your warehouse actually executes queries before comparing list prices.
What do we lose by leaving the AWS ecosystem?
Spectrum's ability to query S3 in place, native streaming ingestion, in-database machine learning through Redshift ML, and IAM-based access control that matches the rest of your AWS estate. Those are replaceable with pipeline tooling and external services, but each replacement is work and another component to run. Count them honestly before treating portability as free.
How hard is it to migrate between them?
Analytical SQL ports with modest editing, because both speak a standard dialect. The work is in everything around the tables: ingestion pipelines, orchestration schedules, the BI semantic layer, row-level security rules and the access model. Budget for those rather than for table definitions, and expect to run both in parallel for a period while you reconcile numbers against the old system.