AWS Data Warehouse Modernization Services

Redshift Serverless suits variable or unpredictable workloads, development environments and teams that do not want to manage capacity, because it scales compute automatically and charges for usage. Provisioned RA3 clusters suit steady, high-utilization workloads where reserved node pricing can be more economical. Many organizations use both, connected through data sharing. We model your workload patterns before recommending a configuration.

Automated conversion with DMS Schema Conversion or the Schema Conversion Tool handles a large share of DDL and simpler procedural code, and DMS Schema Conversion can use generative AI to help with more complex objects. The proportion varies widely by source platform and coding style. We run the tools early during assessment, review the conversion reports and estimate manual effort for remaining objects, so there are no surprises later.

We build reconciliation checks for every migrated table and critical metric, comparing row counts, sums and sampled records between the old and new warehouses. During parallel running, business users verify key reports. Differences are investigated and documented: some reveal migration defects to fix, others reveal historic errors in the legacy system that the business decides to correct.

Assessment and proof of concept typically take four to eight weeks. Migration of a mid-sized warehouse often takes several months, delivered subject area by subject area, and large enterprise estates with extensive ETL and reporting can take longer. Parallel running adds a few weeks for each area. A phased approach reduces risk and lets business users benefit from migrated areas early.

It is rarely either-or. Redshift excels at high-concurrency BI and complex SQL, while an Iceberg-based lake on S3 is ideal for large raw datasets, ML and multi-engine access. Redshift can query lake data directly, and the lakehouse architecture of Amazon SageMaker unifies catalog and permissions across both. We place each workload where it runs best and avoid unnecessary copies.

Options depend on the tool and its role. Some tools, such as Informatica, can be repointed to Redshift and kept. Others are better replaced with AWS Glue, dbt or zero-ETL integrations, especially when licenses are expensive or jobs are simple. We assess each pipeline and recommend whether to repoint, rebuild or retire it, considering cost, skills and risk.