A Time-Series Database in Postgres - WHY?

In this episode of Simplyblock's Cloud Commute Podcast, host Chris Engelbert talks with Mike Freedman, co-founder and CTO of Timescale. Timescale enhances Postgres for handling time series data, analytics, and AI efficiently. The discussion covers Timescale's use of Kubernetes for scalable, decoupled compute and storage, ensuring high availability and efficient resource management, while tackling the challenges of managing stateful databases in Kubernetes.

In this episode of Cloud Commute, Chris and Mike discuss:

• Building TimescaleDB on top of Postgres for time series and analytics
• Challenges and benefits of partitioning, data lifecycle, and compression in databases
• Scaling databases in Kubernetes and overcoming stateful set limitations
• The future of AI integration and Postgres in application development

Interested to learn more about the cloud infrastructure stack like storage, security, and Kubernetes? Head to our website (www.simplyblock.io/cloud-commute-podcast) for more episodes, and follow us on LinkedIn (www.linkedin.com/company/simplyblock-io). You can also check out the detailed show notes on Youtube (www.youtube.com/watch?v=GrS8LmPVolE).

You can find Mike Freedman on X @michaelfreedman and Linkedin: /mfreed.

About simplyblock:

Simplyblock is an intelligent database storage orchestrator for IO-intensive workloads in Kubernetes, including databases and analytics solutions. It uses smart NVMe caching to speed up read I/O latency and queries. A single system connects local NVMe disks, GP3 volumes, and S3 making it easier to handle storage capacity and performance. With the benefits of thin provisioning, storage tiering, and volume pooling, your database workloads get better performance at lower cost without changes to existing AWS infrastructure.

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