It depends on the workload. Redshift is designed for analytical workloads, governed reporting and querying large volumes of structured data. Aurora is a relational database suited to transactional applications that need MySQL or PostgreSQL compatibility, while DynamoDB is a NoSQL service designed for very high-throughput, low-latency access at scale. Most organisations use more than one. AWS increasingly supports direct integrations between services, including zero-ETL patterns that allow data from services such as Aurora or DynamoDB to flow into Redshift without maintaining a traditional pipeline for every movement of data.

OpenSearch is designed primarily for search and observability rather than transactional storage. Typical uses include full-text search, application and security logs, event analytics and real-time operational monitoring. It normally sits alongside databases such as Aurora or DynamoDB. Operational data can be fed into OpenSearch so technical teams can search, investigate and monitor what is happening without using the transactional database itself as the search layer. Ardent AWS Page (1)

We treat AWS cost governance as an ongoing engineering discipline rather than something addressed only during migration. That includes right-sizing Redshift compute, reviewing Aurora and DynamoDB capacity, monitoring consumption patterns and identifying resources or configurations that are creating unnecessary spend. Our managed services team can continue this after go-live, combining cost monitoring with performance tuning so savings are not achieved by simply reducing the capacity users actually need.

Amazon Bedrock provides managed access to foundation models and is well suited to applications such as RAG and generative AI where teams want to build against existing models without managing the underlying model infrastructure. SageMaker provides more control over the wider machine-learning lifecycle, including building, training, deploying and operating custom models. Some AI programmes use both: Bedrock for foundation-model applications and SageMaker where bespoke machine learning or greater model control is required.

Yes. Ardent can assess, design, build and migrate AWS data environments, then provide ongoing monitoring, cost governance, performance optimisation and incident response. Our managed services team can take responsibility for the operational layer after launch, allowing internal engineers to focus on new products, data workloads and AI use cases rather than day-to-day infrastructure management.