SANTA CLARA, Calif., Feb. 2, 2023 — Data Stack Summit has announced Pepperdata will return as the event’s Co-Chair, alongside Solution Monday, for their second annual, peer-to-peer focused summit on April 19, 2023.
This year, attendees are invited to rethink the modern data stack, delving into the latest best practices for building, scaling, and optimizing a modern data stack, including open-source solutions, the role of Kubernetes, and the integration of FinOps for cost and performance optimization.
Pepperdata’s industry experience as the leading platform for cost optimization of big data and Kubernetes workloads allows for valuable insights alongside visionaries, digital and technology leaders, and decision makers from across the big data, cloud, Kubernetes, and FinOps communities.
Registration has officially opened at datastacksummit.com/#register, and those interested in presenting at the summit may submit their proposed presentations at datastacksummit.com/cfp until February 17th.
This year’s call for presentations aims to attract expert speakers to address a range of high-priority industry topics such as:
- Building multi-model data platforms
- Implementing real-world AI/ML infrastructure
- Deploying applications with Kubernetes and GitOps
- Instrumenting for application-aware observability
- Streamlining the migration of workloads to the cloud
- Reducing unexpected costs overruns of cloud operations
- Rise of platform engineering for efficient and reliable deployment of applications
- Adopting FinOps techniques for optimal cloud cost management
Last year’s Data Stack Summit saw over 2,500 attendees from all over the world come together for sessions led by top platform engineers, data and cloud architects, DataOps and DevOps practitioners, and data leaders from companies like Autodesk, T-Mobile, American Airlines, Capital One, Meta, NVIDIA, and Uber.
ABOUT DATA STACK SUMMIT
Data Stack Summit is a peer-to-peer community focused on the building blocks of the modern data platform. The annual event delves into open source considerations, best practices for enterprise data operations, migrations, data observability, and optimizing data pipelines for performance at scale. The summit brings together data engineers, data and cloud architects, DataOps and DevOps practitioners, and data leaders to share real-world perspectives and insights.
Some of the topics that might be covered at the Data Stack Summit include monitoring data platforms, improving collaboration between operations and development, efficient migration strategies, controlling cloud costs, budgeting for optimization and scale, instrumenting for maximum visibility, application delivery and CI/CD with Kubernetes, capacity optimization in the cloud, real-world ML/AI infrastructures, multi-modal data processing, ETL/ELT maturity and optimization, and more. The summit provides a platform for experts to share their knowledge and experience on the cutting-edge of the data stack industry. For more information, visit datastacksummit.com.
ABOUT PEPPERDATA
Pepperdata products help customers reduce the cost and transform the performance of their big data cloud and Kubernetes workloads. Unlike solutions that provide only summary dashboards from infrastructure monitoring and APM vendors, Pepperdata automatically scales system resources while providing a detailed and correlated understanding of each application using hundreds of real-time application and infrastructure metrics. Pepperdata helps enterprises slash their cloud costs and extend the life of their on-premises environments with no manual intervention or code changes. Companies like Expedia and Royal Bank of Canada depend on Pepperdata to deliver big data success. For more information, visit www.pepperdata.com.
MEDIA CONTACT: Nina Pfister at 353111@email4pr.com and 781-929-5620.
Take a Free Thirty-Day Trial to See What Big Data Success Looks Like
Pepperdata products provide complete visibility and automation for your big data environment. Get the observability, automated tuning, recommendations, and alerting you need to efficiently and autonomously optimize big data environments at scale.