RougeWarehouse | Zero-ETL & Real-Time Ingestion

For most of Redshift's history, getting data in meant writing and operating pipelines: extract jobs, S3 staging, scheduled COPY commands, and an orchestrator to keep them in order. Three Redshift features now cover the bulk of that work natively. We help you decide which one fits each source, set it up correctly, and keep the cost in view.

Zero-ETL integrations

A zero-ETL integration replicates tables from Amazon Aurora MySQL, Aurora PostgreSQL, RDS MySQL, RDS PostgreSQL or DynamoDB into a Redshift namespace continuously, with no pipeline to write. The data appears in Redshift as a queryable database that tracks the source within seconds to minutes.

What we handle:

Prerequisites on the source (parameter groups, binary logging or logical replication settings, encryption keys) and on the target (case sensitivity, resource policies, IAM)
Table and schema filtering so you replicate what analytics needs rather than every table the application owns
Limits and edge cases: unsupported data types, DDL handling, tables without primary keys, and what happens on source failover
Hidden cost review: replicated data consumes Redshift Managed Storage, and a continuously replicating integration keeps a Serverless workgroup from idling. We size for it.
Lag monitoring using SVV_INTEGRATION and CloudWatch, with alarms rather than hope

Zero-ETL data lands as a mirror of the source. The transformation layer (dbt, materialized views) still belongs to you; we design it so it runs on the replicated schema without copying data again.

Streaming ingestion from Kinesis Data Streams and Amazon MSK

Redshift can consume a Kinesis Data Stream or an MSK (Kafka) topic directly through a materialized view, with no Firehose hop and no intermediate S3 files. The pattern is an external schema over the stream, a materialized view that parses the payload, and a refresh policy (auto-refresh, or scheduled for batch-style consumption).

We design the view for the payload you actually have (JSON, Avro via Glue Schema Registry, or raw bytes), handle late and duplicate events in the downstream model, and set the refresh cadence against the latency the business needs and the RPU cost it implies. Amazon Data Firehose to S3 remains the right choice when you need durable landing files or fan-out to other consumers; we will tell you which.

S3 auto-copy jobs

COPY JOB turns a one-time COPY statement into a standing job that loads new objects as they land in an S3 prefix. It replaces cron-scheduled COPY scripts and the Lambda-plus-SQS plumbing many teams built to approximate it. We define file layout and compression so loads stay fast, set the job up with the right IAM role, and monitor it with SYS_LOAD_HISTORY and SYS_LOAD_ERROR_DETAIL.

When classic batch is still right

Zero-ETL and streaming are not free of trade-offs. Sources outside AWS, heavy pre-load transformation, slowly changing dimension logic, and workloads where a nightly batch is genuinely fine are all cases where AWS Glue, MWAA-orchestrated jobs or dbt batch models remain the better answer. A good part of our assessment is saying so.

Deliverables

Source-by-source ingestion design (zero-ETL, streaming, auto-copy or batch) with the reasoning written down
Configured integrations, streams and copy jobs with monitoring and alarms
Cost estimate per source and a review after the first month of real usage
Handover session with your data team

Talk to us

Tell us what your cluster looks like today and what it costs, and we will come back with a written assessment and a fixed-scope proposal. Contact us or call +1 (726) 227-3497.