- Unmatched availability of Amazon S3 provides the scale, agility, and flexibility required to combine different data and analytics approaches.
- Build and store your data lakes on AWS to gain deeper insights than with traditional data silos and data warehouses allow.
- Foundational layer to simplify data integration, management and governance tasks.
Utilize Amazon SageMaker for end-to-end ML workflows, encompassing data preprocessing, model training, and deployment. Polestar optimizes the rich set of services provided by AWS enabling you to create and deploy models efficiently, while also integrating with other AWS offerings to enhance your applications with ML-driven insights.
Polestar enables you to process vast amounts of data using services like Amazon EMR, and perform real-time data streaming using Amazon Kinesis. Utilize Amazon QuickSight for interactive and scalable business intelligence. AWS Glue ensures seamless ETL tasks, enabling data integration across various sources. With AWS Advanced Analytics, you can process, analyze, and visualize data at scale, empowering data-driven decision-making.
Leveraging services like Amazon Glue and Amazon Kinesis, Polestar's data engineers help you to seamlessly orchestrate and automate data workflows, ensuring data integrity, scalability, and high availability.
Polestar enables you to utilise the capabilities of Amazon Redshift to petabyte-scale data warehousing with columnar storage and parallel processing, while Amazon S3 serves as a scalable and cost-effective data lake.
Transform legacy data systems into modern, cloud-native architectures By leveraging AWS database migration services and AWS Schema Conversion Tool. Services like Amazon Lambda allows you to run code without provisioning or managing servers, enabling advanced insights through AWS-native data services and seamlessly migrate on-premises databases to the cloud.