DataSet has achieved Amazon Web Services (AWS) Container Competency status. DataSet is a SentinelOne solution that offers logging, monitoring, and troubleshooting in container environments, enabling organizations to fully achieve the benefits of cloud, containers, and Kubernetes.

Achieving the AWS Container Competency differentiates DataSet as an AWS Partner Network (APN) member that provides specialized expertise and proven success in delivering solutions for customers looking to manage, deploy, secure, and monitor their container workloads on AWS.

"We are excited to achieve AWS Container Competency status and to continue our efforts in accelerating our customers’ cloud journey," Rajiv Taori, General Manager

DataSet seamlessly works with AWS services such as Amazon Elastic Kubernetes Service (Amazon EKS), Amazon Elastic Container Service (Amazon ECS), and AWS  Fargate, continuously collects metrics, events, and logs from the entire stack, surfaces anomalies and uncovers their root causes, so DevOps, SRE, and engineering teams can detect and resolve performance issues faster than ever before. Read more about this from the case studies referenced on the AWS partner page.

"Dynamic container environments generate a lot of fast-moving data. Traditional solutions are expensive, difficult to scale, and slow to detect anomalies. DataSet delivers easy scalability and real-time performance at a fraction of the cost. SentinelOne is committed to helping customers efficiently modernize their applications by using containers with the range of powerful tools AWS provides, and this recognition further advances our partnership in delivering customer success," Rajiv Taori, General Manager

About the Architecture

DataSet’s unique architecture combines high performance, low overhead, index-free design, and massively parallel processing that unlocks an unmatched log analytics experience:

  • Schema-Less Ingestion: Experience enormous flexibility in data collection and ingestion without any processing overhead of schema evaluation
  • Streaming Engine: Create materialized views for repeat queries, so high-res dash-boards refresh, accurate alerts fire and automation tasks trigger within seconds.
  • Index-Free Design: Columnar data format eliminates the need to maintain index clusters, re-index, and re-shard storage.
  • Massively Parallel Query Engine: Query engine uses horizontal scheduling, devoting the entire cluster – every CPU core on every compute node –to one query at a time.
  • Cost-Efficient Object Storage: Dedicates every node in our compute cluster to retrieve data from Amazon S3 in parallel, saturating the entire network band-width to fetch compressed data in the most efficient way possible.