Build the data foundation.

A focused ETL pipeline connecting 3-5 sources to a single destination runs $12,000-$25,000 to build and test. Larger architectures with real-time streaming via Kafka, multiple transformation layers in dbt, and custom orchestration in Airflow are typically $30,000-$80,000. We quote against a clear architecture diagram, not abstract hours.

A migration from a legacy warehouse to a modern stack typically takes 6-12 weeks. The range depends on how many existing dbt models or transformation jobs need to be ported, data volume, and whether we can use a parallel-run period to validate outputs before cutover.

Batch is simpler and cheaper -- use it when data that's an hour or a day old is fine. Real-time streaming with Kafka or Kinesis makes sense when you need live dashboards, sub-minute alerting, or real-time ML inference. Most businesses benefit from a hybrid: batch for historical analytics, streaming for operational metrics.

A data warehouse (Redshift, Snowflake) is structured, fast for SQL analytics, and the right default for most teams. A data lake stores raw data cheaply and supports ML training pipelines. Most of our clients end up with a lakehouse pattern: raw files in S3, transformed and modeled data in a warehouse, orchestrated by Airflow.

We implement validation at three layers. Source validation catches schema drift and null rate anomalies before data enters the pipeline. Transformation-layer tests in dbt validate business logic. Destination monitoring via Great Expectations flags statistical anomalies. Failed validations trigger Airflow alerts and halt downstream dependencies.