Choose Amazon Redshift when your main requirement is SQL analytics within AWS and its warehouse operating model fits your team. Choose Databricks when data engineering, streaming, SQL analytics and machine learning need a shared platform.
Both now support serverless SQL and open-table workflows, so the useful comparison is how each runs your workloads, enforces access and accumulates cost, not simply “lakehouse versus warehouse.”
This comparison focuses on Databricks on AWS versus Amazon Redshift. For a wider shortlist, see our enterprise data platform comparison.
Databricks vs Redshift: the practical differences
| Decision | Databricks on AWS | Amazon Redshift |
|---|---|---|
| Main operating model | Shared platform for pipelines, SQL analytics and ML development | AWS-native SQL analytics across warehouse and data lake tables |
| SQL compute | SQL warehouses, including serverless | Provisioned clusters or Redshift Serverless |
| Open tables | Unity Catalog managed Delta Lake and Apache Iceberg tables | Query and write Glue-cataloged Apache Iceberg tables alongside warehouse data |
| Data engineering | SQL/Python pipelines and Spark-based processing within the platform | SQL transformations, supported zero-ETL integrations and streaming ingestion; add other AWS processing services where needed |
| Machine learning | Integrated development, experiment tracking, model governance and deployment capabilities | Redshift ML for SQL-driven workflows using SageMaker AI, with Bedrock integration for supported AI use cases |
| Governance | Unity Catalog for registered data and AI assets, alongside AWS infrastructure permissions | Native warehouse controls, plus IAM, Glue Data Catalog and Lake Formation for relevant lake access paths |
| Cost comparison | Separate classic and serverless billing boundaries | Separate provisioned, serverless and external-query billing boundaries |
How do architecture and open tables change the decision?
Both platforms can participate in a lakehouse architecture. Databricks provides Unity Catalog managed tables for Delta Lake and Apache Iceberg, combining table governance with storage lifecycle and optimization responsibilities.
Decide which engine and catalog own a table before designing shared access from other systems.
Redshift is no longer accurately described as a warehouse that can only read external files. AWS documents Iceberg queries against tables in the AWS Glue Data Catalog and Iceberg writes to Amazon S3 and S3 table buckets.
Its Iceberg SQL commands include inserts, updates, deletes and merges. Open-table access alone therefore does not establish a Databricks advantage.
The transaction boundary still matters. AWS documents that an Iceberg write cannot share an explicit transaction block with another query, and Iceberg write SQL cannot run inside a stored procedure.
Check these Iceberg transaction semantics before assuming an existing multi-step warehouse procedure can write directly to lake tables unchanged.
Storage and compute separation is also available on both sides. Redshift RG and RA3 nodes with managed storage let storage grow independently of compute. A comparison that assumes Redshift storage growth always requires additional nodes will misrepresent that architecture.
For Databricks, distinguish the hosting models: classic compute runs in your AWS account, while serverless compute runs in a Databricks-managed compute plane. That choice affects network connectivity, infrastructure ownership and billing.
If your evaluation also includes Azure, use the separate Databricks on Azure versus AWS comparison.
When another engine must use the same tables, validate its supported table features, catalog authentication and write behavior. An open format reduces dependence on a particular storage representation; it does not automatically make SQL, permissions, orchestration or model-serving configurations portable.
Which platform fits SQL, pipelines and machine learning?
SQL analytics and BI
Both belong in a SQL analytics evaluation. Databricks has dedicated SQL warehouses that connect to BI tools; its documentation states that SQL warehouses include Photon by default.
Comparing a tuned Redshift warehouse only against an arbitrary interactive Spark cluster would evaluate different operating choices.
For an existing Redshift estate, start with the queries, dashboards and refresh schedules already in production. Test the Databricks alternative using those consumers and an equivalent data snapshot.
Include queue time, cache state and concurrent refreshes in the result: a fast isolated query does not prove that the finance dashboard meets its morning deadline.
Ingestion and transformation
Databricks Lakeflow pipelines extend Spark Declarative Pipelines for batch and streaming work in SQL and Python. This is relevant when the same engineering team must ingest events, handle incremental transformations and produce tables for both analytics and ML.
Redshift also supports more than periodic batch loads. AWS documents streaming ingestion into materialized views from Kinesis Data Streams and Amazon MSK, and zero-ETL integrations for supported operational sources.
These can remove a replication pipeline you would otherwise maintain. They do not remove the need to define business transformations, data-quality rules or recovery behavior.
JSON is not a reason by itself to reject Redshift: its SUPER type and PartiQL support semi-structured data. For a workload involving custom Python libraries, document processing or reusable feature engineering, compare the whole processing path.
Identify what runs inside the selected platform and what requires another service, deployment pipeline and support owner.
Machine learning and AI
The distinction is the development workflow, rather than whether ML exists. Databricks documents an integrated ML lifecycle with feature engineering, MLflow experiment tracking, model management and deployment capabilities.
That is useful when engineers and data scientists need to iterate on the same governed datasets.
Redshift ML supports SQL-driven model creation using SageMaker AI and integration with Amazon Bedrock. It can fit a team that wants predictions close to warehouse queries while retaining its AWS ML tooling.
Training, external inference and related storage must be included in the architecture and budget; they are not interchangeable with ordinary warehouse query compute.
For a concrete assessment, trace one model from input data through feature preparation, training, approval, deployment and monitoring. The decision turns on which handoffs your team can operate, including how a changed source schema reaches the deployed model.
Where are governance policies actually enforced?
Compare the access path your users and applications will take. Unity Catalog supplies access control, lineage and auditing for data and AI assets registered in Databricks. It gives teams a common object and permission model, but AWS storage and network access still need deliberate configuration.
Redshift has native row-level security and dynamic data masking. It is inaccurate to imply that all fine-grained security requires an additional AWS product. For Iceberg access, AWS also documents Lake Formation fine-grained controls.
Use an actual authorization scenario: a regional analyst may see only their region’s sales and must not see unmasked customer email addresses.
Verify the result through the BI connection, scheduled job identity and any external engine that reads the underlying files. A policy demonstrated in one SQL interface does not prove that another access route enforces it.
Choose the governance model whose ownership you can maintain. Record who grants database or catalog access, who grants S3 access, and where the audit evidence for each route will be collected. Include revocation in the test so a departing user’s access is checked beyond the interactive session.
How should you compare Databricks and Redshift costs?
Compare the cost of delivering an accepted workload under the same service levels. DBUs, node-hours and RPU-hours are different consumption measures; none is a complete monthly bill.
| Configuration | Core billing boundary | Costs to add or verify |
|---|---|---|
| Databricks classic compute | Databricks usage charges plus AWS virtual machines and associated infrastructure | Storage, disks, networking, idle resources and separately used services |
| Databricks serverless services billed in DBUs | The DBU charge includes the underlying virtual machine cost | Applicable storage, networking and other product charges; avoid adding the same VM cost twice |
| Redshift provisioned | Node capacity; managed storage billed separately where applicable | Spectrum where applicable, concurrency scaling beyond included credits, backup and transfer charges |
| Redshift Serverless | Compute consumption in RPU-hours, with separate managed-storage charges | Storage, transfers and adjacent services; S3 external queries do not add a separate Spectrum charge |
The Databricks boundaries follow its official cost guidance. Use the AWS Redshift pricing page for the selected configuration: for example, RG uses its built-in data lake query engine, while applicable RA3 external queries use Spectrum. Do not apply a single external-query price model to every Redshift deployment.
Compare on-demand and committed consumption separately. An existing Redshift reservation can affect the near-term economics of moving a workload; a new commitment has a different utilization risk.
AWS now also documents Serverless Reservations, so “serverless” should not automatically mean “without a commitment.” Use matching region, currency, contract terms and evaluation horizon on both sides.
Apply those cost boundaries to the workload pattern:
- Predictable BI: Compare provisioned or reserved Redshift capacity with Databricks SQL consumption and applicable commitments. Evaluate utilization across reporting peaks and quieter periods, concurrency-related capacity, idle compute and storage. A committed rate only helps if the capacity is used.
- Intermittent analytics: Compare Redshift Serverless and Databricks serverless SQL over the same query schedule. Include billed active time, minimum billing increments, autoscaling and auto-stop behavior, plus storage and applicable network charges. Check that the chosen settings also meet startup and response-time requirements.
- Mixed ETL + BI + ML: Compare Databricks pipeline, SQL and model training/serving consumption with Redshift plus the processing and ML services the design actually needs. Include orchestration, retained data copies, transfers and operational handoffs; demonstrate any consolidation benefit rather than assuming it.
Across all three, include retries, backfills, support and engineering time. For migration, add translation, validation and parallel operation.
For SKU-level forecasting, use the Databricks pricing guide. Compare cost per successful pipeline run and cost of serving the agreed query workload, alongside freshness and latency results.
Can Databricks and Redshift work together?
Yes. Lakehouse Federation for Redshift lets Databricks expose a Redshift database through a Unity Catalog connection and foreign catalog. This can support exploration or a staged transition while selected workloads remain in Redshift.
The documented prerequisites include a Unity Catalog-enabled workspace, network connectivity to Redshift and supported compute. For SQL warehouses, the connector requires pro or serverless warehouses on a supported version.
Databricks also documents that this connector cannot query Redshift external data, so do not assume that a Redshift external schema becomes a route to every lake table behind it.
Pushdown determines where work happens. The connector supports pushing down operations such as filters and aggregates, but not window functions. Its type mapping also exposes Redshift SUPER as a Spark string.
Check query plans and downstream parsing before treating a federated table as equivalent to an ingested, locally optimized table.
Coexistence therefore needs a cost and ownership boundary. A federated query can consume resources in both platforms and remains dependent on source availability and connectivity.
Keep a named source of truth, identify the reads that still reach Redshift, and decide which repeated or latency-sensitive workloads justify ingestion instead.
What should you measure before choosing?
Use production-shaped workloads to resolve the differences that matter to your organization. The examples below describe evaluation scenarios, not benchmarks or results from a Dateonic client.
| Evaluation workload | Conditions to preserve | Evidence to compare |
|---|---|---|
| Finance BI during the reporting peak | Same data snapshot, metric definitions, user permissions and concurrent refresh pattern | Correct totals, dashboard completion, p95 latency including queueing, and total serving cost |
| Event ingestion with late or corrected records | Same event history, freshness requirement, transformation rules and replay case | Time to usable output, correction handling, recovery effort and cost of a successful processing cycle |
| A model fed by operational data | Same input history, feature definitions, approval requirements and deployment target | Reproducible features, handoffs between tools, governed access and complete training/inference cost |
Separate native-table and external-Iceberg tests; keep track of the catalog, table features and maintenance work used in each. Also report cold-start behavior separately from warm-cache query runs. Otherwise, a difference in data placement or cached results can be mistaken for an engine advantage.
Set acceptance thresholds with the workload owner before testing. If a missing dependency prevents a result, label that criterion inconclusive rather than counting it as a pass. Our Databricks PoC success criteria explain how to turn these measurements into an investment decision.
When should you retain Redshift, add Databricks or migrate?
Retain Redshift when the SQL estate meets its service levels, the team can maintain its controls, and the wider data or ML requirements are already served effectively. Improve a demonstrated bottleneck before funding a platform replacement.
Add Databricks alongside Redshift when an identified pipeline or ML workload benefits from its engineering environment but existing warehouse consumers still have a reason to stay. Assign table ownership and synchronization responsibilities so coexistence does not create competing versions of the same business metric.
Migrate selected workloads when the comparison demonstrates a material benefit after translation effort, parallel running and operating responsibilities are included. Review SQL dialect differences, stored procedures, UDFs, decimal and timestamp behavior, and transaction requirements before estimating the move. Use the enterprise warehouse migration playbook once that decision has been made.
If you are deciding what to retain in Redshift, where Databricks could add value or which workloads to migrate, discuss your architecture and constraints with Dateonic’s Databricks consulting team. Start with the workloads, service levels and operating costs the decision needs to resolve.