Databricks vs Redshift: Lakehouse vs AWS Warehouse (2026)

Both Databricks and Redshift run on AWS infrastructure, query data in S3, and serve analytical SQL. The difference is ownership. Redshift is AWS's warehouse — deeply wired into IAM, Glue, and the billing console you already use. Databricks is an independent lakehouse that keeps your data in open formats and gives you Spark, Python, and ML tooling AWS does not match natively.

How do they compare at a glance?

What is the real architecture trade-off?

Redshift is a traditional warehouse that added a data-lake bridge. Databricks is a data lake that added warehouse-grade SQL.

Redshift stores data in its own managed columnar format across compute nodes. Each node holds a slice of the data, queries fan out across slices, and zone maps skip blocks that can't match the filter. Redshift Spectrum extends queries to S3, but Spectrum reads are slower and billed separately ($5/TB scanned). The core Redshift experience assumes your hot data lives inside the cluster.

Databricks stores everything in S3 as Delta Lake tables — Parquet files plus a JSON transaction log. Compute clusters (or serverless SQL warehouses) spin up, read from S3, and shut down. There is no "inside the cluster" storage. The data stays in your account in open formats whether Databricks is running or not.

Why this matters on AWS specifically: Redshift is the path of least resistance. It shares IAM roles with your other AWS services, appears in your consolidated AWS bill, and integrates with Glue Data Catalog out of the box. Databricks adds a second control plane, a separate identity model, and a separate invoice. You gain open formats and multi-cloud portability. You pay for it in operational surface area.

How does pricing compare?

Both platforms have moved toward serverless pricing, but the billing mechanics differ enough that the same workload can cost 2-3x more on one versus the other depending on how you run it.

Redshift Serverless charges $0.375 per RPU-hour (Redshift Processing Unit). A base capacity of 8 RPUs running 8 hours/day for 22 workdays = ~$528/month. Idle time costs nothing. Provisioned Redshift (dc2.large nodes at $0.25/hr, ra3.xlplus at $1.086/hr) is cheaper for always-on workloads but requires capacity planning.

Databricks SQL Serverless charges ~$0.70/DBU. A small SQL warehouse consumes ~8 DBUs/hour, so 8 hours/day for 22 days = ~$985/month in DBU charges alone. You also pay AWS for the underlying EC2 and S3 — add roughly 30-50% on top. Classic (non-serverless) warehouses cost less per DBU ($0.22/DBU for SQL Pro) but require you to manage cluster sizing.

Rule of thumb: Redshift Serverless is cheaper for teams that run mostly SQL analytics with predictable query patterns. Databricks becomes cost-competitive when you add ML, streaming, and Python workloads — spreading the compute cost across use cases that Redshift would require separate services (SageMaker, Kinesis, Glue) to handle.

How do ML workflows differ?

This is where the platforms diverge most.

Databricks is a full ML platform. Data scientists work in notebooks on the same clusters that run SQL analytics. MLflow tracks experiments, logs models, registers versions, and serves predictions as REST endpoints. Feature Store centralizes feature engineering. GPU clusters run PyTorch and TensorFlow training jobs. The entire ML lifecycle — data prep, training, evaluation, deployment, monitoring — stays inside one platform with one set of permissions.

Redshift is a warehouse that connects to ML. CREATE MODEL in Redshift ML sends training data to SageMaker Autopilot, which builds a model and imports it back as a SQL function. For simple classification or regression ("predict churn from this table"), it works without leaving SQL. For anything beyond AutoML — custom architectures, hyperparameter sweeps, GPU training, experiment tracking — you move to SageMaker, which is a separate console, separate IAM roles, separate billing, and a significant learning curve.

If your data team is 80% SQL analysts who occasionally need a forecast, Redshift ML is simpler. If your data team includes ML engineers who iterate on models weekly, Databricks saves them from stitching together three AWS services.

How well does each fit the AWS ecosystem?

Redshift is AWS-native. IAM roles grant Redshift access to S3, Glue, Secrets Manager, and KMS without managing credentials. CloudWatch monitors cluster health. Glue Data Catalog serves as a shared metastore for Redshift, Athena, and EMR. Lake Formation applies fine-grained access policies. EventBridge triggers on Redshift events. QuickSight connects natively. Everything shares one bill.

Databricks on AWS deploys into your VPC and uses EC2 instances, but it maintains its own workspace, identity layer, and catalog. Cross-service access requires configuring instance profiles or IAM roles that Databricks clusters assume. Unity Catalog can federate with Glue Data Catalog, but it is a parallel governance layer, not a replacement. Billing splits between your AWS invoice (EC2, S3) and a separate Databricks invoice.

For teams already deep in AWS — Lake Formation policies, Glue crawlers, Step Functions pipelines, QuickSight dashboards — Redshift slots in without adding a new control plane. For teams that treat AWS as infrastructure but want platform independence, Databricks' separation is the point: you could move the same Delta tables to Azure or GCP without rewriting anything.

When does Redshift win?

• Your team is SQL-first. Analysts write SQL, BI tools read from the warehouse, and nobody needs notebooks or Spark. Redshift's SQL dialect is PostgreSQL-compatible — existing tools and skills transfer directly.
• You are consolidating on AWS. Glue, Lake Formation, QuickSight, SageMaker, Athena — Redshift is the warehouse these services were designed to work with. One vendor, one bill, one support contract.
• Concurrency matters. Redshift's concurrency scaling handles dashboard load spikes without manual cluster resizing. For BI-heavy workloads with hundreds of concurrent users, Redshift's auto-scaling is battle-tested.
• Cost at pure SQL scale. For teams spending under $2,000/month on analytical SQL, Redshift Serverless is typically 30-50% cheaper than Databricks SQL.

When does Databricks win?

• You need ML and analytics on the same platform. Notebooks, MLflow, GPU clusters, Feature Store, model serving — all governed by Unity Catalog, all reading from the same Delta tables. No SageMaker context-switching.
• Open formats are a requirement. Delta Lake tables are Parquet files in your S3. No proprietary storage. Read them with Spark, Trino, DuckDB, Flink, or anything else. UniForm exposes them as Iceberg and Hudi simultaneously.
• Multi-cloud is real, not hypothetical. If you run workloads on Azure or GCP alongside AWS, Databricks' cross-cloud support and Delta Sharing let you query and share data without ETL between clouds.
• Streaming and batch in one engine. Spark Structured Streaming processes events into the same Delta tables that SQL dashboards read. One framework, one set of tests, no Kinesis-to-Glue-to-Redshift pipeline to maintain.
• Data engineering complexity. If your pipelines involve Python transforms, schema evolution, slowly changing dimensions, or merge (upsert) operations, Delta Lake's ACID transactions and MERGE INTO handle this natively.

Decision tree

    Are you on AWS exclusively?
    |
    +-- Yes
    |   +-- Primary workload?
    |   |   |
    |   |   +-- SQL analytics and dashboards only
    |   |   |   +-- Budget under $2k/month?
    |   |   |   |   └── Redshift Serverless
    |   |   |   └── Need concurrency for 100+ BI users?
    |   |   |       └── Redshift provisioned (ra3 nodes)
    |   |   |
    |   |   +-- SQL + ML + Python notebooks
    |   |   |   └── Databricks (unified platform)
    |   |   |
    |   |   +-- Streaming + batch unified
    |   |   |   └── Databricks (Structured Streaming + Delta)
    |   |   |
    |   |   └── Open format / vendor independence required
    |   |       └── Databricks (Delta Lake on your S3)
    |   |
    |   └── Deep in Lake Formation + Glue + QuickSight?
    |       └── Redshift (native integration, minimal new infra)
    |
    +-- Multi-cloud (AWS + Azure/GCP)
        └── Databricks (runs on all three, Delta Sharing across)

FAQ

Can Redshift read Delta Lake tables?

Not directly. Redshift Spectrum reads Parquet, ORC, Avro, and JSON in S3, but it does not understand Delta Lake's transaction log. You can query the underlying Parquet files, but you lose ACID guarantees, time travel, and schema evolution. AWS Glue can crawl Delta tables and surface them to Athena, which then reads them correctly — but that bypasses Redshift entirely.

Is Databricks on AWS more expensive than Redshift?

For pure SQL workloads, typically yes — 30-60% more. The gap narrows or inverts when you factor in ML and streaming. Running Redshift + SageMaker + Kinesis + Glue to match Databricks' capabilities often costs more than Databricks alone, and the integration overhead is real engineering time.

What about Redshift Spectrum vs Databricks for querying S3?

Redshift Spectrum charges $5/TB scanned and runs queries through your Redshift cluster's leader node. Performance depends on file format, partitioning, and how much data Spectrum must scan. Databricks queries S3 natively (it is the primary storage layer) with Photon's vectorized engine — no per-scan surcharge, but you pay for cluster uptime. For frequent S3 queries, Databricks' model avoids the unpredictable per-scan costs.

Should I use both?

It works but adds complexity. Some teams keep Redshift for BI and reporting (QuickSight integration, PostgreSQL compatibility) and Databricks for data engineering and ML. Delta Sharing or S3 as the shared layer makes this practical. The cost is two platforms to govern, two billing streams, and two sets of access policies to maintain.

How do I migrate from Redshift to Databricks?

UNLOAD from Redshift to S3 as Parquet, then create Delta tables over those files. Databricks provides a migration accelerator that generates table DDL from Redshift metadata. The harder part is rewriting stored procedures, Redshift-specific SQL (DISTKEY, SORTKEY, late-binding views), and downstream BI connections.

What about Athena as a middle ground?

Athena is serverless SQL on S3 at $5/TB scanned — no cluster to manage, no Databricks subscription. For ad-hoc queries and light analytics on Parquet/Iceberg data in S3, Athena is cheaper than both. It lacks the ML, streaming, and warehouse performance tiers of Databricks and Redshift, but for teams that need S3 queries without committing to either platform, it is a valid starting point.

Does Redshift support open table formats like Iceberg?

Redshift added support for querying Apache Iceberg tables through Redshift Spectrum and the Glue Data Catalog in late 2025. You can read Iceberg tables in S3, but Redshift cannot write Iceberg format natively — writes still go into Redshift's proprietary storage. Databricks reads and writes Delta, Iceberg (via UniForm), and Hudi, making it the stronger choice if open formats are a hard requirement for your data platform.

Where Fastero fits

Fastero connects to both Redshift (PostgreSQL wire protocol) and Databricks (SQL warehouse endpoints). The AI agent generates SQL tuned for whichever engine your data lives on — zone-map-aware queries for Redshift, partition-pruning for Delta tables on Databricks. If you are evaluating both or running them in parallel, Fastero queries across them in the same analysis without requiring you to pick one.

Related reading

• Databricks vs BigQuery: Lakehouse vs Warehouse
• Snowflake vs Databricks: Warehouse vs Lakehouse
• Redshift vs BigQuery: AWS vs Google Cloud Warehouses
• Best Data Warehouse Tools in 2026

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